From 2fcd2702d23685054653cda33b31785a625d15f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?B=C3=A9r=C3=A9nice=20Batut?= Date: Thu, 29 Sep 2022 12:22:49 +0000 Subject: [PATCH 01/66] Add draft for the taxonomic assignment tutorial --- .../taxonomic-assignment/tutorial.md | 223 ++++++++++++++++++ .../taxonomic-assignment/workflows/index.md | 3 + 2 files changed, 226 insertions(+) create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/workflows/index.md diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md new file mode 100644 index 00000000000000..e3e4364be07805 --- /dev/null +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -0,0 +1,223 @@ +--- +layout: tutorial_hands_on +title: Taxonomic Assignment +zenodo_link: +questions: +- Which species are present in my sample? +objectives: +- explain how taxonomic assignment works +- apply Kraken2 to assign taxonomic labels +- apply Krona to visualize results of assignment and understand the output +- identify taxonomic classification tool that fits best depending on their data +level: Introductory +key_points: +- To do +time_estimation: 45M +contributors: +- Sophia120199 +--- + +# Introduction +{:.no_toc} + +Microbiome data can be gathered from different environments such as soil, water or the human gut. The biological interest lies in general in the question how the microbiome present at a specific site influences this environment. Metagenomic samples contain DNA from different organisms at a specific site, where the sample was collected. Metagenomic data can be used to find out which organisms coexist in that niche and which genes are present in the different organisms. Metatranscriptomic samples include the transcribed gene products, thus RNA, that therefore allow to not only study the presence of genes but additionally their expression in the given environment. The following tutorial will focus on metagenomics data, but the principle is the same for metatranscriptomics data. +The investigation of co-occurring and thus potentially interacting microbes present at a specific site and their relative abundance is also called "microbial community profiling". +Basic for this is to find out which microbes are present in the sample. This can be achieved for all known microbes, where the DNA sequence specific for a certain species is known. The process of sorting single DNA reads derived from sequencing to a species (or other ranks) is called taxonomic assignment. In general, taxonomy is the study of sorting organisms into different groups within a larger system according to similarities and differences. The groups are named, defined, classified and hierarchically ordered. The principal ranks from top to bottom (*with examples for the human being*) are domain (*eukarya*), kingdom (*animalia*), phylum (*chordata*), class (*mammalia*), order (*primates*), family (*hominidae*), genus (*homo*), and species (*sapiens*). From this classification, one can generate a tree of life, also known as a phylogenetic tree. It is a rooted tree that describes the relationship of all life on earth. At the root sits the “last universal common ancestor” and the three main branches (in taxonomy also called domains) are bacteria, archaea and eukaryotes. Most important for this is the idea that all life on earth is derived from a common ancestor and therefore when comparing two species, you will -sooner or later- find a common ancestor for all of them. + +When we talk about metagenomic data here, what we start with is sequences derived from DNA fragments that could be isolated from the sample of interest. Ideally, from all microbes present in the sample, we would also find DNA. The underlying idea of taxonomic assignment is to compare the DNA sequences found in the sample (reads) to DNA sequences of a database. When a read matches a database DNA sequence of a known microbe, we can derive a list with microbes present in the sample. +The comparison of reads to database sequences can be done in different ways, leading to three different types of taxonomic assignment. + +## Three types of taxonomic assignment + +For the **k-mer based** analysis, databases as well as the samples DNA are broken into k-mers about 30 bp length for comparison. From all the genomes in the database, where a specific k-mer is found, a lowest common ancestor (LCA) tree is derived and the abundance of k-mers within the tree is counted. This is the basis for a root-to-leaf path calculation, where the path with the highest score is used for classification of the sample. By counting the abundance of k-mers, also an estimation of relative abundance of taxa is possible. The major advantage of k-mer based analysis is the low compute cost. Major disadvantages are the low detection accuracy, that the unclassified percentage is unknown and that there is no gene detection, no SNVs detection and no genomic comparison possible. An example for a k-mer based analysis tool is Kraken2, which will be used in this tutorial + +![Kraken functionality](./images/Kraken_algorithm.png "Kraken functionality.") xxx citation + +For the **gene based** analysis, reads about 150 bp length are aligned to reference genes about 1 kbp length. Next, marker genes are used to estimate species abundance. Furthermore, genes can be analyzed in isolation for presence or absence in a specific condition. +The major advantage is the detection of the pangenome (entire set of genes within a species). Major disadvantages are the high compute cost, low detection accuracy and that the unclassified percentage is unknown. At least intragenic SNVs can be detected and low-resolution genomic comparison is possible. + +For the **genome based** analysis, read pairs of 150 bp length are aligned to reference genomes of about 3 Mbp length. Considering the coverage and breadth, genomes are used to measure genome abundance. Furthermore, genes can be analyzed in genomic context. Advantages of this method are the high detection accuracy, that the unclassified percentage is known, that all SNVs can be detected and that high-resolution genomic comparisons are possible. This method takes medium compute cost. + +After this theoretical introduction, let's now get hands on analyzing an actual dataset! + +> ### Agenda +> +> In this tutorial, we will deal with: +> +> 1. TOC +> {:toc} +> +{: .agenda} + +# Background on data + +The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas, that is studied because of its special environmental conditions. It is paired-end data with JP4D_R1.fastq.gz being the forward reads and JP4D_R2.fastq.gz being the reverse reads. Additionally, the reads have been trimmed using __cutadapt__ (s. xxx tutorial) + +## Hands on: Importing the data + +> ### {% icon hands_on %} Hands-on: Data upload +> +> 1. Import the following files from [Zenodo](zenodo link) +> - `JP4D_R1.fastq.gz` +> - `JP4D_R2.fastq.gz` +> +> +> > ### {% icon tip %} Tip: Importing data via links +> > +> > * Copy the link location +> > * Open the Galaxy Upload Manager +> > * Select **Paste/Fetch Data** +> > * Paste the link into the text field +> > * Press **Start** +> {: .tip} +{: .hands_on} + +# Hands on: Run Kraken2 for taxonomic assignment + +Our input data is the DNA reads of microbes present at Cuatro Ciénegas. To find out which microbes are present, we will compare the reads of the sample to sequences of known microbes stored in a database using the algorithm Kraken2, which is k-mer based. For this tutorial, we will use the Standard plus protozoa & fungi (2021) database. + +> ### {% icon hands_on %} Hands-on: Assign taxonomic labels with Kraken2 +> +> 1. {% tool [Kraken2](toolshed.g2.bx.psu.edu/repos/iuc/kraken2/kraken2/2.0.8_beta+galaxy0) %} with the following parameters: +> - *"Single or paired reads"*: `Paired` +> - *"Print scientific names instead of just taxids"*: `No` +> - *"Confidence"*: `0.1` +> - In *"Create Report"*: +> - *"Print a report with aggregrate counts/clade to file"*: `Yes` +> - *"Format report output like Kraken 1's kraken-mpa-report"*: `Yes` +> - *"Select a Kraken2 database"*: `Standard plus protozoa & fungi (2021)` +> +> > ### {% icon comment %} Comment +> > +> > A confidence score of 0.1 means that at least 10% of the k-mers should match entries in the database. This value can be reduced if a less restrictive taxonomic assignation is desired. +> {: .comment} +> +{: .hands_on} + + +Kraken2 will create two output files called "Classification" and "Report". + +Let's have a look at the classification file. (xxx add image) It has 5 columns: + +1. C/U: classified/unclassified +2. Sequence ID +3. Taxonomy ID +4. Length of sequence (read1|read2 for paired reads) +5. indicates LCA mapping of each k-mer in the sequence |:| indicates end of first read, start of second read for paired reads --> example: "n k-mers assigned to taxon xxx" + + +![Kraken2 Classification Output](./images/Kraken2_classification_screenshot.png "Kraken2 Classification Output.") + + +Let's also have a look at the report file. (xxx add image) It has 2 columns: +1. taxon name grouped into d_domain, p_phylum, c_class, o_order, f_family, g_genus, s_species +2. number of reads assigned to specific taxon + +![Kraken2 Report Output](./images/Kraken2_report_screenshot.png "Kraken2 Report Output.") + +As both files are not very well readable, we will use __Krona__ to visualize the data. + +# Analyze taxonomic assigment + +Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the __Krona pie chart__ tool ({% cite Ondov_2011 %}). But first, we need to convert the output generated by Kraken2 so it can be used as an input from the Krona tool. + +## Convert output from Kraken2 so it can be used for Krona + +__Convert Kraken__ tool is designed to translate results of the Kraken metagenomic classifier (see citations below) to the full representation of NCBI taxonomy. It does so by using Taxonomic ID field provided by Kraken. The output of this tool can be directly visualized by the Krona tool. + +> ### {% icon hands_on %} Hands-on: Convert Kraken2 Output +> +> 1. {% tool [Convert Kraken](xxx) %} with the following parameters: +> - *"Choose dataset to convert"*: Classification Output of Kraken2 +> - *"Select a taxonomy database"*: `2022-03-08` +> - *"Read name"*: `column:2` +> - *"Taxonomy ID field"*: `column:3` +> +{: .hands_on} + + +## Visualize the taxonomical classification with Krona + +__Krona__ allows hierarchical data to be explored with zooming, multi-layered pie charts. With this tool, we can easily visualize the composition of the bacterial communities and compare how the populations of microorganisms are modified according to the conditions of the environment. + +> ### {% icon hands_on %} Hands-on: Visualize data with Krona +> +> 1. {% tool [Krona pie chart](xxx) %} with the following parameters: +> - *"Type of input data"*: `taxonomy` +> - *"Input file"*: Output file of Convert Kraken +> +{: .hands_on} + + +Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765e92bccb62d7f6daa/display/?preview=True&dataset=0&node=0&collapse=true&color=false&depth=8&font=11&key=true). Using the search bar we can check if certain taxa are present. + + +> ### {% icon question %} Questions +> +> How many percent of the bacteria consists of the genus "paracoccus"? +> +> +> > ### {% icon solution %} Solution +> > +> > 6 %. +> +> {: .solution} +> +{: .question} + + + +# Discussion: Choosing the right tool + +When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools and their results are presented in the following section, with focus on tools that are available in Galaxy. +When talking about taxonomic assignment or taxonomic classification, most of the time we actually talk about two methods, that in practice are often used interchangeably: while **taxonomic binning** is the classification of individual sequence reads to reference taxa, **taxonomic profiling** refers to investigating relative abundances of taxa within a dataset but not to the classification of individual reads. +Tools for taxonomic classification can be divided into three groups. Nevertheless, all of them require a pre-computed database based on previously sequenced microbial DNA or protein sequences. +1. **DNA-to-DNA** classification tools compare sequencing reads with genomic databases of DNA sequences (Bracken, Kraken, Kraken2, MegaBLAST) +2. **DNA-to-Protein** classification tools compare sequencing reads with genomic databases of protein sequences (more computationally intensive because of analysis of all six frames of potential DNA-to amino acid translation) (DIAMOND) +3. **Marker based** classification tools use a reference database that only includes a subset of gene sequences (e.g. 16S rRNA sequence), which is quick, but introduces bias (MetaPhlAn2) + +By using these tools for taxonomic binning, one always has to be aware of false positives due to the vast search space on the one hand (which dominantly occurs at low abundances) and of false negatives due to presently undiscovered microbial species that are not part of any database yet. When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. + +When benchmarking different classification tools, several metrics are used to compare their performance: +1. **Precision**: proportion of true positive species identified in the sample divided by number of total species identified by the method +2. **Recall**: proportion of true positive species divided by the number of distinct species actually in the sample +3. Precision-recall curve: each point represents the precision and recall scores at a specific abundance threshold → **area under the precision-recall curve (AUPR)** + +4. **L2 distance**: representation of abundance profiles → how accurately the abundance of each species or genera in the resulting classification reflects the abundance of each species in the original biological sample (“ground truth”) + +## Binning tools (“Critical Assessment of Metagenome Interpretation (CAMI) – a benchmark of metagenomics software”) +As mentioned earlier, taxonomic binning is the classification of individual sequence reads to reference taxa by grouping sequences into bins with a taxonomic label attached. All investigated binning tools have some characteristics in common: +- Small bins are not reliable +- High purity can be reached for higher ranks +- Below family level, all programs performed poorly +(either assigning very little data +or assigning more, but substantial misclassification) +- Completeness degrades at lower ranks and for low-abundant bins +- At increasing taxonomic distances to the reference, purity and completeness drop substantially +- Presence of plasmids and viral sequences doesn’t have any effect on binning performance + +MEGAN and Kraken perform similarly, as they rely on similar algorithms but utilize different data properties. + +## Profiling tools + +Profilers, which are tools that investigate relative abundances of taxa within a dataset, fall into three groups depending on their performance: +1. Profilers, that correctly predict relative abundances +2. Precise profilers (suitable, when many false positives would increase cost and effort in downstream analysis) +3. Profilers with high recall (suitable for pathogen detection, when the failure of detecting an organism can have severe negative consequences) + +However, some characteristics are common to all profilers: +- Most profilers only perform well until the family level +- Drastic decrease in performance between family and genus level, while little change between order and family level +- Fidelity of abundance estimates decreases notably when viruses and plasmids were present +- Taxonomic profilers vs profiles from taxonomic binning: +Precision and recall of the taxonomic binners were comparable to that of the profilers; +abundance estimation at higher ranks was more problematic for the binners + +MetaPhlAn 2.0 belongs to the group of precise profilers. On the basis of the average of precision and recall, over all samples and taxonomic ranks, MetaPhlAn 2.0 performed second best of all 10 profilers tested. + +"Critical Assessment of Metagenome Interpretation (CAMI): The second round of challenges" + +![CAMI II software ranking](./images/CAMI_software_ranking.png "CAMI II software ranking") + + + diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/workflows/index.md b/topics/metagenomics/tutorials/taxonomic-assignment/workflows/index.md new file mode 100644 index 00000000000000..e092e0ae66ddd4 --- /dev/null +++ b/topics/metagenomics/tutorials/taxonomic-assignment/workflows/index.md @@ -0,0 +1,3 @@ +--- +layout: workflow-list +--- From 6d04b39a0115c29ee1eac5120871cb06dc6115ee Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Thu, 29 Sep 2022 14:24:59 +0200 Subject: [PATCH 02/66] Add files via upload --- .../CAMI_software_ranking.PNG | Bin 0 -> 123220 bytes 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a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index e3e4364be07805..514b0ef6bc16f7 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -1,10 +1,13 @@ --- -layout: tutorial_hands_on -title: Taxonomic Assignment -zenodo_link: +layout: tutorial_hands_on +title: Taxonomic Assignment of Metagenomic Data +zenodo_link: still needs to be created questions: -- Which species are present in my sample? +- Which species (or genera, families, ...) are present in my sample? + objectives: + +- explain what taxonomic assignment is - explain how taxonomic assignment works - apply Kraken2 to assign taxonomic labels - apply Krona to visualize results of assignment and understand the output @@ -18,25 +21,39 @@ contributors: --- # Introduction -{:.no_toc} + +The term **"microbiome"** describes "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity" (Whipps et al. 1988). + +Microbiome data can be gathered from different environments such as soil, water or the human gut. The biological interest lies in general in the question how the microbiome present at a specific site influences this environment. **Metagenomic samples** contain DNA from different organisms at a specific site, where the sample was collected. Metagenomic data can be used to find out which organisms coexist in that niche and which genes are present in the different organisms. + +Metatranscriptomic samples include the transcribed gene products, thus RNA, that therefore allow to not only study the presence of genes but additionally their expression in the given environment. The following tutorial will focus on metagenomics data, but the principle is the same for metatranscriptomics data. + +The investigation of microbes present at a specific site and their relative abundance is also called **“microbial community profiling”**. +Basic for this is to find out which microbes are present in the sample. This can be achieved for all known microbes, where the DNA sequence specific for a certain species is known. The process of sorting single DNA reads derived from sequencing to a species (or other ranks) is called **taxonomic assignment**. -Microbiome data can be gathered from different environments such as soil, water or the human gut. The biological interest lies in general in the question how the microbiome present at a specific site influences this environment. Metagenomic samples contain DNA from different organisms at a specific site, where the sample was collected. Metagenomic data can be used to find out which organisms coexist in that niche and which genes are present in the different organisms. Metatranscriptomic samples include the transcribed gene products, thus RNA, that therefore allow to not only study the presence of genes but additionally their expression in the given environment. The following tutorial will focus on metagenomics data, but the principle is the same for metatranscriptomics data. -The investigation of co-occurring and thus potentially interacting microbes present at a specific site and their relative abundance is also called "microbial community profiling". -Basic for this is to find out which microbes are present in the sample. This can be achieved for all known microbes, where the DNA sequence specific for a certain species is known. The process of sorting single DNA reads derived from sequencing to a species (or other ranks) is called taxonomic assignment. In general, taxonomy is the study of sorting organisms into different groups within a larger system according to similarities and differences. The groups are named, defined, classified and hierarchically ordered. The principal ranks from top to bottom (*with examples for the human being*) are domain (*eukarya*), kingdom (*animalia*), phylum (*chordata*), class (*mammalia*), order (*primates*), family (*hominidae*), genus (*homo*), and species (*sapiens*). From this classification, one can generate a tree of life, also known as a phylogenetic tree. It is a rooted tree that describes the relationship of all life on earth. At the root sits the “last universal common ancestor” and the three main branches (in taxonomy also called domains) are bacteria, archaea and eukaryotes. Most important for this is the idea that all life on earth is derived from a common ancestor and therefore when comparing two species, you will -sooner or later- find a common ancestor for all of them. +When talking about taxonomic assignment or taxonomic classification, most of the time we actually talk about two methods, that in practice are often used interchangeably: while **taxonomic binning** is the classification of individual sequence reads to reference taxa, **taxonomic profiling** refers to investigating relative abundances of taxa within a dataset but not to the classification of individual reads. +Tools for taxonomic classification can be divided into three groups. Nevertheless, all of them require a pre-computed database based on previously sequenced microbial DNA or protein sequences. +1. **DNA-to-DNA** classification tools compare sequencing reads with genomic databases of DNA sequences (Bracken, Kraken, Kraken2, MegaBLAST) +2. **DNA-to-Protein** classification tools compare sequencing reads with genomic databases of protein sequences (more computationally intensive because of analysis of all six frames of potential DNA-to amino acid translation) (DIAMOND) +3. **Marker based** classification tools use a reference database that only includes a subset of gene sequences (e.g. 16S rRNA sequence), which is quick, but introduces bias (MetaPhlAn2) -When we talk about metagenomic data here, what we start with is sequences derived from DNA fragments that could be isolated from the sample of interest. Ideally, from all microbes present in the sample, we would also find DNA. The underlying idea of taxonomic assignment is to compare the DNA sequences found in the sample (reads) to DNA sequences of a database. When a read matches a database DNA sequence of a known microbe, we can derive a list with microbes present in the sample. -The comparison of reads to database sequences can be done in different ways, leading to three different types of taxonomic assignment. +> ### {% icon details %} More details on taxonomy +> +> In general, taxonomy is the study of sorting organisms into different groups within a larger system according to similarities and differences. The groups are named, defined, classified and hierarchically ordered. The principal ranks from top to bottom (*with examples for the human being*) are domain (*eukarya*), kingdom (*animalia*), phylum (*chordata*), class (*mammalia*), order (*primates*), family (*hominidae*), genus (*homo*), and species (*sapiens*). From this classification, one can generate a tree of life, also known as a phylogenetic tree. It is a rooted tree that describes the relationship of all life on earth. At the root sits the “last universal common ancestor” and the three main branches (in taxonomy also called domains) are bacteria, archaea and eukaryotes. Most important for this is the idea that all life on earth is derived from a common ancestor and therefore when comparing two species, you will -sooner or later- find a common ancestor for all of them. +> +> {: .details} -## Three types of taxonomic assignment +When we talk about metagenomic data here, what we start with is sequences derived from DNA fragments that could be isolated from the sample of interest. Ideally, from all microbes present in the sample, we would also find DNA. The underlying idea of taxonomic assignment is to compare the DNA sequences found in the sample (reads) to DNA sequences of a database. When a read matches a database DNA sequence of a known microbe, we can derive a list with microbes present in the sample. +The comparison of reads to database sequences can be done in different ways, leading to three different types of taxonomic assignment: k-mer based, gene-based and genome-based analysis. -For the **k-mer based** analysis, databases as well as the samples DNA are broken into k-mers about 30 bp length for comparison. From all the genomes in the database, where a specific k-mer is found, a lowest common ancestor (LCA) tree is derived and the abundance of k-mers within the tree is counted. This is the basis for a root-to-leaf path calculation, where the path with the highest score is used for classification of the sample. By counting the abundance of k-mers, also an estimation of relative abundance of taxa is possible. The major advantage of k-mer based analysis is the low compute cost. Major disadvantages are the low detection accuracy, that the unclassified percentage is unknown and that there is no gene detection, no SNVs detection and no genomic comparison possible. An example for a k-mer based analysis tool is Kraken2, which will be used in this tutorial +- For the **k-mer based** analysis, databases as well as the samples DNA are broken into k-mers about 30 bp length for comparison. From all the genomes in the database, where a specific k-mer is found, a lowest common ancestor (LCA) tree is derived and the abundance of k-mers within the tree is counted. This is the basis for a root-to-leaf path calculation, where the path with the highest score is used for classification of the sample. By counting the abundance of k-mers, also an estimation of relative abundance of taxa is possible. The major advantage of k-mer based analysis is the low compute cost. Major disadvantages are the low detection accuracy, that the unclassified percentage is unknown and that there is no gene detection, no SNVs detection and no genomic comparison possible. An example for a k-mer based analysis tool is Kraken2, which will be used in this tutorial -![Kraken functionality](./images/Kraken_algorithm.png "Kraken functionality.") xxx citation +![Kraken functionality](../../images/taxonomic-assignment/Kraken_algorithm.PNG "Kraken functionality.") {% cite Wood.2014 %} -For the **gene based** analysis, reads about 150 bp length are aligned to reference genes about 1 kbp length. Next, marker genes are used to estimate species abundance. Furthermore, genes can be analyzed in isolation for presence or absence in a specific condition. +- For the **gene based** analysis, reads are aligned to reference genes about 1 kbp length. Next, marker genes are used to estimate species abundance. Furthermore, genes can be analyzed in isolation for presence or absence in a specific condition. The major advantage is the detection of the pangenome (entire set of genes within a species). Major disadvantages are the high compute cost, low detection accuracy and that the unclassified percentage is unknown. At least intragenic SNVs can be detected and low-resolution genomic comparison is possible. -For the **genome based** analysis, read pairs of 150 bp length are aligned to reference genomes of about 3 Mbp length. Considering the coverage and breadth, genomes are used to measure genome abundance. Furthermore, genes can be analyzed in genomic context. Advantages of this method are the high detection accuracy, that the unclassified percentage is known, that all SNVs can be detected and that high-resolution genomic comparisons are possible. This method takes medium compute cost. +- For the **genome based** analysis, read pairs of 150 bp length are aligned to reference genomes of about 3 Mbp length. Considering the coverage and breadth, genomes are used to measure genome abundance. Furthermore, genes can be analyzed in genomic context. Advantages of this method are the high detection accuracy, that the unclassified percentage is known, that all SNVs can be detected and that high-resolution genomic comparisons are possible. This method takes medium compute cost. After this theoretical introduction, let's now get hands on analyzing an actual dataset! @@ -51,9 +68,9 @@ After this theoretical introduction, let's now get hands on analyzing an actual # Background on data -The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas, that is studied because of its special environmental conditions. It is paired-end data with JP4D_R1.fastq.gz being the forward reads and JP4D_R2.fastq.gz being the reverse reads. Additionally, the reads have been trimmed using __cutadapt__ (s. xxx tutorial) +The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas, that is studied because of its special environmental conditions {% cite Okie.2020 %}. It is a collection of paired-end data with R1 being the forward reads and R2 being the reverse reads. Additionally, the reads have been trimmed using [__cutadapt__ ](https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html#trim-and-filter---short-reads) -## Hands on: Importing the data +## Hands on: Importing the data (xxx copy the Data upload chapter of other tutorials? use snippet?) > ### {% icon hands_on %} Hands-on: Data upload > @@ -70,16 +87,15 @@ The dataset we will use for this tutorial comes from an oasis in the mexican des > > * Paste the link into the text field > > * Press **Start** > {: .tip} -{: .hands_on} -# Hands on: Run Kraken2 for taxonomic assignment +# Hands on: k-mer based taxonomic assignment of metagenomic data -Our input data is the DNA reads of microbes present at Cuatro Ciénegas. To find out which microbes are present, we will compare the reads of the sample to sequences of known microbes stored in a database using the algorithm Kraken2, which is k-mer based. For this tutorial, we will use the Standard plus protozoa & fungi (2021) database. +Our input data is the DNA reads of microbes present at Cuatro Ciénegas. To find out which microbes are present, we will compare the reads of the sample to sequences of known microbes stored in a database using the algorithm Kraken2, which is k-mer based. For this tutorial, we will use the Standard plus protozoa & fungi (2021) database. The Standard database includes archaea, bacteria, viral, plasmid, human, and UniVec_Core data. > ### {% icon hands_on %} Hands-on: Assign taxonomic labels with Kraken2 > > 1. {% tool [Kraken2](toolshed.g2.bx.psu.edu/repos/iuc/kraken2/kraken2/2.0.8_beta+galaxy0) %} with the following parameters: -> - *"Single or paired reads"*: `Paired` +> - *"Single or paired reads"*: `Paired Collection` > - *"Print scientific names instead of just taxids"*: `No` > - *"Confidence"*: `0.1` > - In *"Create Report"*: @@ -97,7 +113,7 @@ Our input data is the DNA reads of microbes present at Cuatro Ciénegas. To find Kraken2 will create two output files called "Classification" and "Report". -Let's have a look at the classification file. (xxx add image) It has 5 columns: +Let's have a look at the classification file. It has 5 columns: 1. C/U: classified/unclassified 2. Sequence ID @@ -106,20 +122,55 @@ Let's have a look at the classification file. (xxx add image) It has 5 columns: 5. indicates LCA mapping of each k-mer in the sequence |:| indicates end of first read, start of second read for paired reads --> example: "n k-mers assigned to taxon xxx" -![Kraken2 Classification Output](./images/Kraken2_classification_screenshot.png "Kraken2 Classification Output.") +![Kraken2 Classification Output](../../images/taxonomic-assignment/Kraken2_classification_screenshot.PNG "Kraken2 Classification Output.") + +> ### {% icon question %} Questions +>Let's have a look at the first line of the Kraken2 classification output. +>1. Is it classified or unclassified? +>2. How long is the sequenece? +>3. What is the taxonomy ID? +> +> +> +> > ### {% icon solution %} Solution +> >1. classified +> >2. 142 bp +> >3. 398580 +> > +> +> {: .solution} +> +{: .question} + -Let's also have a look at the report file. (xxx add image) It has 2 columns: +Let's also have a look at the report file. It has 2 columns: 1. taxon name grouped into d_domain, p_phylum, c_class, o_order, f_family, g_genus, s_species 2. number of reads assigned to specific taxon -![Kraken2 Report Output](./images/Kraken2_report_screenshot.png "Kraken2 Report Output.") +![Kraken2 Report Output](../../images/taxonomic-assignment/Kraken2_report_screenshot.PNG "Kraken2 Report Output.") + +> ### {% icon question %} Questions +> +> 1. What family does Paracoccus sp. MC1862 belong to? +> 2. How many reads were assigned to Paracoccus sp. MC1862 +> +> +> > ### {% icon solution %} Solution +> > +> > 1. Rhodobacteraceae +> > 2. 917 +> +> {: .solution} +> +{: .question} + -As both files are not very well readable, we will use __Krona__ to visualize the data. +As both files contain a lot of information, we will use __Krona__ {% cite Ondov.2011 %}to visualize the data. # Analyze taxonomic assigment -Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the __Krona pie chart__ tool ({% cite Ondov_2011 %}). But first, we need to convert the output generated by Kraken2 so it can be used as an input from the Krona tool. +Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the __Krona pie chart__ tool ({% cite Ondov.2011 %}). But first, we need to convert the output generated by Kraken2 so it can be used as an input from the Krona tool. ## Convert output from Kraken2 so it can be used for Krona @@ -128,7 +179,7 @@ __Convert Kraken__ tool is designed to translate results of the Kraken metagenom > ### {% icon hands_on %} Hands-on: Convert Kraken2 Output > > 1. {% tool [Convert Kraken](xxx) %} with the following parameters: -> - *"Choose dataset to convert"*: Classification Output of Kraken2 +> - *"Choose dataset to convert"*: Datset collection: Classification Output of Kraken2 > - *"Select a taxonomy database"*: `2022-03-08` > - *"Read name"*: `column:2` > - *"Taxonomy ID field"*: `column:3` @@ -144,7 +195,8 @@ __Krona__ allows hierarchical data to be explored with zooming, multi-layered pi > > 1. {% tool [Krona pie chart](xxx) %} with the following parameters: > - *"Type of input data"*: `taxonomy` -> - *"Input file"*: Output file of Convert Kraken +> - *"Input file"*: Dataset collection: Output file of Convert Kraken +> - *"Combine data from multiple data sets?"*: yes > {: .hands_on} @@ -154,29 +206,30 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 > ### {% icon question %} Questions > -> How many percent of the bacteria consists of the genus "paracoccus"? +> 1. How many percent of the bacteria consists of the genus "paracoccus"? +> 2. Is there any Escherichia coli present? If yes, how many reads were found? +> 3. Where might the eukaryotic DNA come from? > > > > ### {% icon solution %} Solution > > -> > 6 %. +> > 1. 5 %. +> > 2. It is present and 162 reads were found. +> > 3. It's probably human contamination. > > {: .solution} > {: .question} + +>

+>

# Discussion: Choosing the right tool -When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools and their results are presented in the following section, with focus on tools that are available in Galaxy. -When talking about taxonomic assignment or taxonomic classification, most of the time we actually talk about two methods, that in practice are often used interchangeably: while **taxonomic binning** is the classification of individual sequence reads to reference taxa, **taxonomic profiling** refers to investigating relative abundances of taxa within a dataset but not to the classification of individual reads. -Tools for taxonomic classification can be divided into three groups. Nevertheless, all of them require a pre-computed database based on previously sequenced microbial DNA or protein sequences. -1. **DNA-to-DNA** classification tools compare sequencing reads with genomic databases of DNA sequences (Bracken, Kraken, Kraken2, MegaBLAST) -2. **DNA-to-Protein** classification tools compare sequencing reads with genomic databases of protein sequences (more computationally intensive because of analysis of all six frames of potential DNA-to amino acid translation) (DIAMOND) -3. **Marker based** classification tools use a reference database that only includes a subset of gene sequences (e.g. 16S rRNA sequence), which is quick, but introduces bias (MetaPhlAn2) - -By using these tools for taxonomic binning, one always has to be aware of false positives due to the vast search space on the one hand (which dominantly occurs at low abundances) and of false negatives due to presently undiscovered microbial species that are not part of any database yet. When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. +When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools and their results are presented in the following section, with focus on tools that are available in Galaxy ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}). +When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. When benchmarking different classification tools, several metrics are used to compare their performance: 1. **Precision**: proportion of true positive species identified in the sample divided by number of total species identified by the method @@ -215,9 +268,9 @@ abundance estimation at higher ranks was more problematic for the binners MetaPhlAn 2.0 belongs to the group of precise profilers. On the basis of the average of precision and recall, over all samples and taxonomic ranks, MetaPhlAn 2.0 performed second best of all 10 profilers tested. -"Critical Assessment of Metagenome Interpretation (CAMI): The second round of challenges" + -![CAMI II software ranking](./images/CAMI_software_ranking.png "CAMI II software ranking") +![CAMI II software ranking](../../images/taxonomic-assignment/CAMI_software_ranking.PNG "CAMI II software ranking") {% cite Meyer.2022 %} From fb959d3a2a5a7282455d929e8671638381e02578 Mon Sep 17 00:00:00 2001 From: Sophia120199 Date: Wed, 5 Oct 2022 20:13:12 +0000 Subject: [PATCH 05/66] add tutorial.bib and update tutorial text --- .../taxonomic-assignment/tutorial.bib | 105 ++++++++++++++++++ .../taxonomic-assignment/tutorial.md | 77 ++++++------- 2 files changed, 142 insertions(+), 40 deletions(-) create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib new file mode 100644 index 00000000000000..acd0eb230d4952 --- /dev/null +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib @@ -0,0 +1,105 @@ +% This file was created with Citavi 6.14.0.0 + +@article{Meyer.2022, + abstract = {Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.}, + author = {Meyer, Fernando and Fritz, Adrian and Deng, Zhi-Luo and Koslicki, David and Lesker, Till Robin and Gurevich, Alexey and Robertson, Gary and Alser, Mohammed and Antipov, Dmitry and Beghini, Francesco and Bertrand, Denis and Brito, Jaqueline J. and Brown, C. Titus and Buchmann, Jan and Bulu{\c{c}}, Aydin and Chen, Bo and Chikhi, Rayan and Clausen, Philip T. L. C. and Cristian, Alexandru and Dabrowski, Piotr Wojciech and Darling, Aaron E. and Egan, Rob and Eskin, Eleazar and Georganas, Evangelos and Goltsman, Eugene and Gray, Melissa A. and Hansen, Lars Hestbjerg and Hofmeyr, Steven and Huang, Pingqin and Irber, Luiz and Jia, Huijue and J{\o}rgensen, Tue Sparholt and Kieser, Silas D. and Klemetsen, Terje and Kola, Axel and Kolmogorov, Mikhail and Korobeynikov, Anton and Kwan, Jason and LaPierre, Nathan and Lemaitre, Claire and Li, Chenhao and Limasset, Antoine and Malcher-Miranda, Fabio and Mangul, Serghei and Marcelino, Vanessa R. and Marchet, Camille and Marijon, Pierre and Meleshko, Dmitry and Mende, Daniel R. and Milanese, Alessio and Nagarajan, Niranjan and Nissen, Jakob and Nurk, Sergey and Oliker, Leonid and Paoli, Lucas and Peterlongo, Pierre and Piro, Vitor C. and Porter, Jacob S. and Rasmussen, Simon and Rees, Evan R. and Reinert, Knut and Renard, Bernhard and Robertsen, Espen Mikal and Rosen, Gail L. and Ruscheweyh, Hans-Joachim and Sarwal, Varuni and Segata, Nicola and Seiler, Enrico and Shi, Lizhen and Sun, Fengzhu and Sunagawa, Shinichi and S{\o}rensen, S{\o}ren Johannes and Thomas, Ashleigh and Tong, Chengxuan and Trajkovski, Mirko and Tremblay, Julien and Uritskiy, Gherman and Vicedomini, Riccardo and Wang, Zhengyang and Wang, Ziye and Wang, Zhong and Warren, Andrew and Willassen, Nils Peder and Yelick, Katherine and You, Ronghui and Zeller, Georg and Zhao, Zhengqiao and Zhu, Shanfeng and Zhu, Jie and Garrido-Oter, Ruben and Gastmeier, Petra and Hacquard, Stephane and H{\"a}u{\ss}ler, Susanne and Khaledi, Ariane and Maechler, Friederike and Mesny, Fantin and Radutoiu, Simona and Schulze-Lefert, Paul and Smit, Nathiana and Strowig, Till and Bremges, Andreas and Sczyrba, Alexander and McHardy, Alice Carolyn}, + year = {2022}, + title = {Critical Assessment of Metagenome Interpretation: the second round of challenges}, + pages = {429--440}, + volume = {19}, + number = {4}, + journal = {Nature methods}, + doi = {10.1038/s41592-022-01431-4} +} + + +@article{Okie.2020, + abstract = {Several universal genomic traits affect trade-offs in the capacity, cost, and efficiency of the biochemical information processing that underpins metabolism and reproduction. We analyzed the role of these traits in mediating the responses of a planktonic microbial community to nutrient enrichment in an oligotrophic, phosphorus-deficient pond in Cuatro Ci{\'e}negas, Mexico. This is one of the first whole-ecosystem experiments to involve replicated metagenomic assessment. Mean bacterial genome size, GC content, total number of tRNA genes, total number of rRNA genes, and codon usage bias in ribosomal protein sequences were all higher in the fertilized treatment, as predicted on the basis of the assumption that oligotrophy favors lower information-processing costs whereas copiotrophy favors higher processing rates. Contrasting changes in trait variances also suggested differences between traits in mediating assembly under copiotrophic versus oligotrophic conditions. Trade-offs in information-processing traits are apparently sufficiently pronounced to play a role in community assembly because the major components of metabolism-information, energy, and nutrient requirements-are fine-tuned to an organism's growth and trophic strategy.}, + author = {Okie, Jordan G. and Poret-Peterson, Amisha T. and Lee, Zarraz Mp and Richter, Alexander and Alcaraz, Luis D. and Eguiarte, Luis E. and Siefert, Janet L. and Souza, Valeria and Dupont, Chris L. and Elser, James J.}, + year = {2020}, + title = {Genomic adaptations in information processing underpin trophic strategy in a whole-ecosystem nutrient enrichment experiment}, + volume = {9}, + journal = {eLife}, + doi = {10.7554/eLife.49816} +} + + +@article{Ondov.2011, + abstract = {BACKGROUND + +A critical output of metagenomic studies is the estimation of abundances of taxonomical or functional groups. The inherent uncertainty in assignments to these groups makes it important to consider both their hierarchical contexts and their prediction confidence. The current tools for visualizing metagenomic data, however, omit or distort quantitative hierarchical relationships and lack the facility for displaying secondary variables. + +RESULTS + +Here we present Krona, a new visualization tool that allows intuitive exploration of relative abundances and confidences within the complex hierarchies of metagenomic classifications. Krona combines a variant of radial, space-filling displays with parametric coloring and interactive polar-coordinate zooming. The HTML5 and JavaScript implementation enables fully interactive charts that can be explored with any modern Web browser, without the need for installed software or plug-ins. This Web-based architecture also allows each chart to be an independent document, making them easy to share via e-mail or post to a standard Web server. To illustrate Krona's utility, we describe its application to various metagenomic data sets and its compatibility with popular metagenomic analysis tools. + +CONCLUSIONS + +Krona is both a powerful metagenomic visualization tool and a demonstration of the potential of HTML5 for highly accessible bioinformatic visualizations. Its rich and interactive displays facilitate more informed interpretations of metagenomic analyses, while its implementation as a browser-based application makes it extremely portable and easily adopted into existing analysis packages. Both the Krona rendering code and conversion tools are freely available under a BSD open-source license, and available from: http://krona.sourceforge.net.}, + author = {Ondov, Brian D. and Bergman, Nicholas H. and Phillippy, Adam M.}, + year = {2011}, + title = {Interactive metagenomic visualization in a Web browser}, + pages = {385}, + volume = {12}, + journal = {BMC Bioinformatics}, + doi = {10.1186/1471-2105-12-385} +} + + +@article{Sczyrba.2017, + abstract = {Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from $\sim$700 newly sequenced microorganisms and $\sim$600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.}, + author = {Sczyrba, Alexander and Hofmann, Peter and Belmann, Peter and Koslicki, David and Janssen, Stefan and Dr{\"o}ge, Johannes and Gregor, Ivan and Majda, Stephan and Fiedler, Jessika and Dahms, Eik and Bremges, Andreas and Fritz, Adrian and Garrido-Oter, Ruben and J{\o}rgensen, Tue Sparholt and Shapiro, Nicole and Blood, Philip D. and Gurevich, Alexey and Bai, Yang and Turaev, Dmitrij and DeMaere, Matthew Z. and Chikhi, Rayan and Nagarajan, Niranjan and Quince, Christopher and Meyer, Fernando and Balvo{\v{c}}iūt{\.{e}}, Monika and Hansen, Lars Hestbjerg and S{\o}rensen, S{\o}ren J. and Chia, Burton K. H. and Denis, Bertrand and Froula, Jeff L. and Wang, Zhong and Egan, Robert and {Don Kang}, Dongwan and Cook, Jeffrey J. and Deltel, Charles and Beckstette, Michael and Lemaitre, Claire and Peterlongo, Pierre and Rizk, Guillaume and Lavenier, Dominique and Wu, Yu-Wei and Singer, Steven W. and Jain, Chirag and Strous, Marc and Klingenberg, Heiner and Meinicke, Peter and Barton, Michael D. and Lingner, Thomas and Lin, Hsin-Hung and Liao, Yu-Chieh and Silva, Genivaldo Gueiros Z. and Cuevas, Daniel A. and Edwards, Robert A. and Saha, Surya and Piro, Vitor C. and Renard, Bernhard Y. and Pop, Mihai and Klenk, Hans-Peter and G{\"o}ker, Markus and Kyrpides, Nikos C. and Woyke, Tanja and Vorholt, Julia A. and Schulze-Lefert, Paul and Rubin, Edward M. and Darling, Aaron E. and Rattei, Thomas and McHardy, Alice C.}, + year = {2017}, + title = {Critical Assessment of Metagenome Interpretation-a benchmark of metagenomics software}, + pages = {1063--1071}, + volume = {14}, + number = {11}, + journal = {Nature methods}, + doi = {10.1038/nmeth.4458} +} + + +@article{Wood.2019, + abstract = {Although Kraken's k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85{\%}, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.}, + author = {Wood, Derrick E. and Lu, Jennifer and Langmead, Ben}, + year = {2019}, + title = {Improved metagenomic analysis with Kraken 2}, + keywords = {Alignment-free methods;Metagenomics;Metagenomics classification;Metagenomics/methods;Microbiome;Minimizers;Probabilistic data structures;Software}, + pages = {257}, + volume = {20}, + number = {1}, + journal = {Genome biology}, + doi = {10.1186/s13059-019-1891-0}, + file = {Kraken2:Attachments/Kraken2.pdf:application/pdf} +} + + +@article{Wood.2014, + abstract = {Kraken is an ultrafast and highly accurate program for assigning taxonomic labels to metagenomic DNA sequences. Previous programs designed for this task have been relatively slow and computationally expensive, forcing researchers to use faster abundance estimation programs, which only classify small subsets of metagenomic data. Using exact alignment of k-mers, Kraken achieves classification accuracy comparable to the fastest BLAST program. In its fastest mode, Kraken classifies 100 base pair reads at a rate of over 4.1 million reads per minute, 909 times faster than Megablast and 11 times faster than the abundance estimation program MetaPhlAn. Kraken is available at http://ccb.jhu.edu/software/kraken/.}, + author = {Wood, Derrick E. and Salzberg, Steven L.}, + year = {2014}, + title = {Kraken: ultrafast metagenomic sequence classification using exact alignments}, + keywords = {Archaea/classification/genetics;Bacteria/classification/genetics;Classification;Humans;Metagenome;Metagenomics/methods;Sensitivity and Specificity;Sequence Alignment/methods;Sequence Analysis, DNA/methods;Software}, + pages = {R46}, + volume = {15}, + number = {3}, + journal = {Genome biology}, + doi = {10.1186/gb-2014-15-3-r46}, + file = {Kraken:Attachments/Kraken.pdf:application/pdf} +} + + +@article{Ye.2019, + abstract = {Metagenomic sequencing is revolutionizing the detection and characterization of microbial species, and a wide variety of software tools are available to perform taxonomic classification of these data. The fast pace of development of these tools and the complexity of metagenomic data make it important that researchers are able to benchmark their performance. Here, we review current approaches for metagenomic analysis and evaluate the performance of 20 metagenomic classifiers using simulated and experimental datasets. We describe the key metrics used to assess performance, offer a framework for the comparison of additional classifiers, and discuss the future of metagenomic data analysis.}, + author = {Ye, Simon H. and Siddle, Katherine J. and Park, Daniel J. and Sabeti, Pardis C.}, + year = {2019}, + title = {Benchmarking Metagenomics Tools for Taxonomic Classification}, + pages = {779--794}, + volume = {178}, + number = {4}, + journal = {Cell}, + doi = {10.1016/j.cell.2019.07.010}, + file = {Ye, Siddle et al. 2019 - Benchmarking Metagenomics Tools for Taxonomic:Attachments/Ye, Siddle et al. 2019 - Benchmarking Metagenomics Tools for Taxonomic.pdf:application/pdf} +} + + diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 514b0ef6bc16f7..0e0f48b7d36d6d 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -19,7 +19,14 @@ time_estimation: 45M contributors: - Sophia120199 --- - +> ### Agenda +> +> In this tutorial, we will deal with: +> +> 1. TOC +> {:toc} +> +{: .agenda} # Introduction The term **"microbiome"** describes "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity" (Whipps et al. 1988). @@ -48,7 +55,7 @@ The comparison of reads to database sequences can be done in different ways, lea - For the **k-mer based** analysis, databases as well as the samples DNA are broken into k-mers about 30 bp length for comparison. From all the genomes in the database, where a specific k-mer is found, a lowest common ancestor (LCA) tree is derived and the abundance of k-mers within the tree is counted. This is the basis for a root-to-leaf path calculation, where the path with the highest score is used for classification of the sample. By counting the abundance of k-mers, also an estimation of relative abundance of taxa is possible. The major advantage of k-mer based analysis is the low compute cost. Major disadvantages are the low detection accuracy, that the unclassified percentage is unknown and that there is no gene detection, no SNVs detection and no genomic comparison possible. An example for a k-mer based analysis tool is Kraken2, which will be used in this tutorial -![Kraken functionality](../../images/taxonomic-assignment/Kraken_algorithm.PNG "Kraken functionality.") {% cite Wood.2014 %} +![Kraken functionality](../../taxonomic-assignment/images/Kraken_algorithm.png "Kraken functionality.") {% cite Wood.2014 %} - For the **gene based** analysis, reads are aligned to reference genes about 1 kbp length. Next, marker genes are used to estimate species abundance. Furthermore, genes can be analyzed in isolation for presence or absence in a specific condition. The major advantage is the detection of the pangenome (entire set of genes within a species). Major disadvantages are the high compute cost, low detection accuracy and that the unclassified percentage is unknown. At least intragenic SNVs can be detected and low-resolution genomic comparison is possible. @@ -57,36 +64,38 @@ The major advantage is the detection of the pangenome (entire set of genes withi After this theoretical introduction, let's now get hands on analyzing an actual dataset! -> ### Agenda -> -> In this tutorial, we will deal with: -> -> 1. TOC -> {:toc} -> -{: .agenda} + # Background on data The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas, that is studied because of its special environmental conditions {% cite Okie.2020 %}. It is a collection of paired-end data with R1 being the forward reads and R2 being the reverse reads. Additionally, the reads have been trimmed using [__cutadapt__ ](https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html#trim-and-filter---short-reads) -## Hands on: Importing the data (xxx copy the Data upload chapter of other tutorials? use snippet?) - > ### {% icon hands_on %} Hands-on: Data upload > -> 1. Import the following files from [Zenodo](zenodo link) +> 1. Create a new history for this exercise +> +> {% snippet faqs/galaxy/histories_create_new.md %} +> +> 2. Import the FASTQ file pairs from [Zenodo]({{ page.zenodo_link }}) or a data library: > - `JP4D_R1.fastq.gz` > - `JP4D_R2.fastq.gz` + -`JC1A_R1.fastq.gz` + - `JC1A_R2.fastq.gz` +> `` > +> ``` > -> > ### {% icon tip %} Tip: Importing data via links -> > -> > * Copy the link location -> > * Open the Galaxy Upload Manager -> > * Select **Paste/Fetch Data** -> > * Paste the link into the text field -> > * Press **Start** -> {: .tip} +> {% snippet faqs/galaxy/datasets_import_via_link.md %} +> {% snippet faqs/galaxy/datasets_import_from_data_library.md %} + +> +> 3. Create a paired collection. +> +> {% snippet faqs/galaxy/collections_build_list_paired.md %} +> +{: .hands_on} + + # Hands on: k-mer based taxonomic assignment of metagenomic data @@ -122,7 +131,7 @@ Let's have a look at the classification file. It has 5 columns: 5. indicates LCA mapping of each k-mer in the sequence |:| indicates end of first read, start of second read for paired reads --> example: "n k-mers assigned to taxon xxx" -![Kraken2 Classification Output](../../images/taxonomic-assignment/Kraken2_classification_screenshot.PNG "Kraken2 Classification Output.") +![Kraken2 Classification Output](../../taxonomic-assignment/images/Kraken2_classification_screenshot.png "Kraken2 Classification Output.") > ### {% icon question %} Questions >Let's have a look at the first line of the Kraken2 classification output. @@ -148,7 +157,7 @@ Let's also have a look at the report file. It has 2 columns: 1. taxon name grouped into d_domain, p_phylum, c_class, o_order, f_family, g_genus, s_species 2. number of reads assigned to specific taxon -![Kraken2 Report Output](../../images/taxonomic-assignment/Kraken2_report_screenshot.PNG "Kraken2 Report Output.") +![Kraken2 Report Output](../../taxonomic-assignment/images/Kraken2_report_screenshot.png "Kraken2 Report Output.") > ### {% icon question %} Questions > @@ -168,7 +177,7 @@ Let's also have a look at the report file. It has 2 columns: As both files contain a lot of information, we will use __Krona__ {% cite Ondov.2011 %}to visualize the data. -# Analyze taxonomic assigment +# Visualization of taxonomic assignment Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the __Krona pie chart__ tool ({% cite Ondov.2011 %}). But first, we need to convert the output generated by Kraken2 so it can be used as an input from the Krona tool. @@ -207,7 +216,7 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 > ### {% icon question %} Questions > > 1. How many percent of the bacteria consists of the genus "paracoccus"? -> 2. Is there any Escherichia coli present? If yes, how many reads were found? +> 2. Is there any *Escherichia coli* present? If yes, how many reads were found? > 3. Where might the eukaryotic DNA come from? > > @@ -228,7 +237,7 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 # Discussion: Choosing the right tool -When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools and their results are presented in the following section, with focus on tools that are available in Galaxy ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}). +When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}) and their results are presented in the following section, with focus on tools that are available in Galaxy. When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. When benchmarking different classification tools, several metrics are used to compare their performance: @@ -238,19 +247,7 @@ When benchmarking different classification tools, several metrics are used to co 4. **L2 distance**: representation of abundance profiles → how accurately the abundance of each species or genera in the resulting classification reflects the abundance of each species in the original biological sample (“ground truth”) -## Binning tools (“Critical Assessment of Metagenome Interpretation (CAMI) – a benchmark of metagenomics software”) -As mentioned earlier, taxonomic binning is the classification of individual sequence reads to reference taxa by grouping sequences into bins with a taxonomic label attached. All investigated binning tools have some characteristics in common: -- Small bins are not reliable -- High purity can be reached for higher ranks -- Below family level, all programs performed poorly -(either assigning very little data -or assigning more, but substantial misclassification) -- Completeness degrades at lower ranks and for low-abundant bins -- At increasing taxonomic distances to the reference, purity and completeness drop substantially -- Presence of plasmids and viral sequences doesn’t have any effect on binning performance - -MEGAN and Kraken perform similarly, as they rely on similar algorithms but utilize different data properties. - + ## Profiling tools Profilers, which are tools that investigate relative abundances of taxa within a dataset, fall into three groups depending on their performance: @@ -270,7 +267,7 @@ MetaPhlAn 2.0 belongs to the group of precise profilers. On the basis of the ave -![CAMI II software ranking](../../images/taxonomic-assignment/CAMI_software_ranking.PNG "CAMI II software ranking") {% cite Meyer.2022 %} +![CAMI II software ranking](../../taxonomic-assignment/images/CAMI_software_ranking.png "CAMI II software ranking") {% cite Meyer.2022 %} From e823f73c374a739cf5bb3febe6588d49726a4de2 Mon Sep 17 00:00:00 2001 From: Sophia120199 Date: Tue, 11 Oct 2022 21:16:14 +0000 Subject: [PATCH 06/66] update tutorial --- .../taxonomic-assignment/tutorial.bib | 12 +++++++++ .../taxonomic-assignment/tutorial.md | 26 ++++++++++++++----- 2 files changed, 32 insertions(+), 6 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib index acd0eb230d4952..fb173c4a0ebf30 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib @@ -103,3 +103,15 @@ @article{Ye.2019 } +@article{whipps1988fungi, + title={Fungi biol control syst}, + author={Whipps, JM and Karen Lewis, RC}, + journal={Mycoparasitism and Plant Disease Control}, + pages={161--187}, + year={1988}, + publisher={Manchester University Press Manchester} +} + + + + diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 0e0f48b7d36d6d..c0178d3a36f8a3 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -29,7 +29,7 @@ contributors: {: .agenda} # Introduction -The term **"microbiome"** describes "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity" (Whipps et al. 1988). +The term **"microbiome"** describes "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity" ({% cite whipps1988fungi %}). Microbiome data can be gathered from different environments such as soil, water or the human gut. The biological interest lies in general in the question how the microbiome present at a specific site influences this environment. **Metagenomic samples** contain DNA from different organisms at a specific site, where the sample was collected. Metagenomic data can be used to find out which organisms coexist in that niche and which genes are present in the different organisms. @@ -68,7 +68,9 @@ After this theoretical introduction, let's now get hands on analyzing an actual # Background on data -The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas, that is studied because of its special environmental conditions {% cite Okie.2020 %}. It is a collection of paired-end data with R1 being the forward reads and R2 being the reverse reads. Additionally, the reads have been trimmed using [__cutadapt__ ](https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html#trim-and-filter---short-reads) +The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas, that is studied because of its special environmental conditions {% cite Okie.2020 %}. The researchers collected samples directly from the pond (= control mesocosm, called JC1A in this tutorial) and fertilized some of the samples later on to receive nutrient enrichment (=Lagunita Fertilized Pond, called JP4D in this tutorial). In this way, they investigated the impact of nutrient enchriment on the microbial community. +The datafiles are named according to the first four characters of the filenames. +It is a collection of paired-end data with R1 being the forward reads and R2 being the reverse reads. Additionally, the reads have been trimmed using [__cutadapt__ ](https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html#trim-and-filter---short-reads) > ### {% icon hands_on %} Hands-on: Data upload > @@ -264,10 +266,22 @@ Precision and recall of the taxonomic binners were comparable to that of the pro abundance estimation at higher ranks was more problematic for the binners MetaPhlAn 2.0 belongs to the group of precise profilers. On the basis of the average of precision and recall, over all samples and taxonomic ranks, MetaPhlAn 2.0 performed second best of all 10 profilers tested. - - -![CAMI II software ranking](../../taxonomic-assignment/images/CAMI_software_ranking.png "CAMI II software ranking") {% cite Meyer.2022 %} - + +> | Tool | Best method across metrics for* : | additional features | available in Galaxy | +> | ----------------- |-------------------------------------| +> | mOTUs 1.1.1 | - |most memory efficient | no | +> | mOTUs 2.5.1 |marine; plant-associated | - | no | +> | mOTUs v.cami1 |strain-madness | - | no | +> | MetaPhlAn 2.9.21 |plant-associated | - | yes +> | MetaPhlAn 2.9.22 |marine; strain-madness | - | yes +> | DUDes v.cami1 |strain-madness | - | no +> | FOCUS 1.5 | - |fastest; most memory efficient | no | +> | Bracken 2.2 | - |fastest | yes (version 2.7) | +> | Bracken 2.6 |plant-associated | - | yes (version 2.7) | +{: .matrix} + +*metagenome benchmark datasets created by {% cite Meyer.2022 %} representing a marine, a high strain diversity environment (‘strain-madness’) and a plant-associated environment including fungal genomes and host plant material + From be19d36c5d8d07d86290520ad2ce06809d58b064 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Thu, 8 Dec 2022 13:01:03 +0100 Subject: [PATCH 07/66] Update tutorial.bib --- .../tutorials/taxonomic-assignment/tutorial.bib | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib index fb173c4a0ebf30..74388871256a1e 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib @@ -113,5 +113,13 @@ @article{whipps1988fungi } - +@article{Lu.2017, + author = {Lu, Jennifer and Breitwieser, Florian P. and Thielen, Peter and Salzberg, Steven L.}, + year = {2017}, + title = {Bracken: estimating species abundance in metagenomics data}, + pages = {e104}, + volume = {3}, + journal = {PeerJ Computer Science}, + doi = {10.7717/peerj-cs.104} +} From f59cc326df2ab6a6aa18f3da6b831e73a32a3aee Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Thu, 8 Dec 2022 13:25:04 +0100 Subject: [PATCH 08/66] Update tutorial.md --- .../tutorials/taxonomic-assignment/tutorial.md | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index c0178d3a36f8a3..19df3b746b0e68 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -111,7 +111,6 @@ Our input data is the DNA reads of microbes present at Cuatro Ciénegas. To find > - *"Confidence"*: `0.1` > - In *"Create Report"*: > - *"Print a report with aggregrate counts/clade to file"*: `Yes` -> - *"Format report output like Kraken 1's kraken-mpa-report"*: `Yes` > - *"Select a Kraken2 database"*: `Standard plus protozoa & fungi (2021)` > > > ### {% icon comment %} Comment @@ -179,6 +178,18 @@ Let's also have a look at the report file. It has 2 columns: As both files contain a lot of information, we will use __Krona__ {% cite Ondov.2011 %}to visualize the data. +# Hands on: estimating species abundance + +A "simple and worthwile addition to Kraken for better abundance estimates" (xxx cite: benchmarking metagenomic tools for taxonomic classification; Ye et al) is called Bracken (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species." (xxx cite Bracken paper) + +> ### {% icon hands_on %} Hands-on: estimate species abundance with Bracken xxx +> +> 1. {% tool [Bracken](https://toolshed.g2.bx.psu.edu/view/iuc/bracken/79450f7fd718) %} with the following parameters: +> - * kmer distribution? xxx +> which kind of report? xxx +> +{: .hands_on} + # Visualization of taxonomic assignment Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the __Krona pie chart__ tool ({% cite Ondov.2011 %}). But first, we need to convert the output generated by Kraken2 so it can be used as an input from the Krona tool. From 0b35534bb498459af3cfff2f7e33a847ce41010e Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Tue, 13 Dec 2022 21:10:45 +0100 Subject: [PATCH 09/66] Update tutorial.md --- .../taxonomic-assignment/tutorial.md | 27 ++++++++++--------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 19df3b746b0e68..aa7e09316cbf31 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -271,26 +271,27 @@ Profilers, which are tools that investigate relative abundances of taxa within a However, some characteristics are common to all profilers: - Most profilers only perform well until the family level - Drastic decrease in performance between family and genus level, while little change between order and family level +- poorer performance of all profilers on CAMI datasets (only ~30%-40% of reads are simulated from known taxa, rest of the reads are from novel taxa, plasmids or simulated evolved strains) compared to International Metagenomics and Microbiome Standards Alliance (IMMSA) - Fidelity of abundance estimates decreases notably when viruses and plasmids were present +- high numbers of false positive calls at low abundance - Taxonomic profilers vs profiles from taxonomic binning: Precision and recall of the taxonomic binners were comparable to that of the profilers; abundance estimation at higher ranks was more problematic for the binners -MetaPhlAn 2.0 belongs to the group of precise profilers. On the basis of the average of precision and recall, over all samples and taxonomic ranks, MetaPhlAn 2.0 performed second best of all 10 profilers tested. -> | Tool | Best method across metrics for* : | additional features | available in Galaxy | -> | ----------------- |-------------------------------------| -> | mOTUs 1.1.1 | - |most memory efficient | no | -> | mOTUs 2.5.1 |marine; plant-associated | - | no | -> | mOTUs v.cami1 |strain-madness | - | no | -> | MetaPhlAn 2.9.21 |plant-associated | - | yes -> | MetaPhlAn 2.9.22 |marine; strain-madness | - | yes -> | DUDes v.cami1 |strain-madness | - | no -> | FOCUS 1.5 | - |fastest; most memory efficient | no | -> | Bracken 2.2 | - |fastest | yes (version 2.7) | -> | Bracken 2.6 |plant-associated | - | yes (version 2.7) | -{: .matrix} +| Tool | version | available in Galaxy | In CAMI challenge, best method across metrics for\* : | additional features | Ye et al. benchmarking | +| --------- | -------------------- | ------------------- | ----------------------------------------------------- | ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | +| mOTUs | 1.1.1 | no | \- | most memory efficient | | +| mOTUs | 2.5.1 | no | marine; plant-associated | \- | | +| mOTUs | v.cami1 | no | strain-madness | \- | | +| MetaPhlAn | 2.9.21 | yes | plant-associated | \- | recommended for low computational requirements (< 2 Gb of memory) | +| MetaPhlAn | 2.9.22 | yes | marine; strain-madness | \- | +| DUDes | v.cami1 | no | strain-madness | \- | | +| FOCUS 1.5 | 1.5 | no | \- | fastest; most memory efficient | | +| Bracken | 2.2 | yes (version 2.7) | \- | fastest | provide good performance metrics
very fast on large numbers of samples
allow custom databases
when high amounts of memory (>100 Gb) are available | +| Bracken | 2.6 | yes (version 2.7) | plant-associated | \- | +| Kraken | 2.0.8 beta (GSA,Sr) | yes | marine | fastest; most memory efficient | *metagenome benchmark datasets created by {% cite Meyer.2022 %} representing a marine, a high strain diversity environment (‘strain-madness’) and a plant-associated environment including fungal genomes and host plant material From 061b6c4c52c4ed9838f7e4998687cb3c85587e25 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Tue, 13 Dec 2022 21:11:48 +0100 Subject: [PATCH 10/66] Update tutorial.md --- topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index aa7e09316cbf31..8fd2eb219055fc 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -290,7 +290,7 @@ abundance estimation at higher ranks was more problematic for the binners | DUDes | v.cami1 | no | strain-madness | \- | | | FOCUS 1.5 | 1.5 | no | \- | fastest; most memory efficient | | | Bracken | 2.2 | yes (version 2.7) | \- | fastest | provide good performance metrics
very fast on large numbers of samples
allow custom databases
when high amounts of memory (>100 Gb) are available | -| Bracken | 2.6 | yes (version 2.7) | plant-associated | \- | +| Bracken | 2.6 | yes (version 2.7) | plant-associated | Kraken | 2.0.8 beta (GSA,Sr) | yes | marine | fastest; most memory efficient | *metagenome benchmark datasets created by {% cite Meyer.2022 %} representing a marine, a high strain diversity environment (‘strain-madness’) and a plant-associated environment including fungal genomes and host plant material From a11917869f5e5172a778e708a0788849eea70886 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Tue, 13 Dec 2022 21:12:46 +0100 Subject: [PATCH 11/66] Update tutorial.md --- .../metagenomics/tutorials/taxonomic-assignment/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 8fd2eb219055fc..2c0419779a843e 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -289,9 +289,9 @@ abundance estimation at higher ranks was more problematic for the binners | MetaPhlAn | 2.9.22 | yes | marine; strain-madness | \- | | DUDes | v.cami1 | no | strain-madness | \- | | | FOCUS 1.5 | 1.5 | no | \- | fastest; most memory efficient | | -| Bracken | 2.2 | yes (version 2.7) | \- | fastest | provide good performance metrics
very fast on large numbers of samples
allow custom databases
when high amounts of memory (>100 Gb) are available | +| Bracken | 2.2 | yes (version 2.7) | \- | fastest | | Bracken | 2.6 | yes (version 2.7) | plant-associated -| Kraken | 2.0.8 beta (GSA,Sr) | yes | marine | fastest; most memory efficient | +| Kraken | 2.0.8 beta (GSA,Sr) | yes | marine | fastest; most memory efficient | provide good performance metrics
very fast on large numbers of samples
allow custom databases
when high amounts of memory (>100 Gb) are available | *metagenome benchmark datasets created by {% cite Meyer.2022 %} representing a marine, a high strain diversity environment (‘strain-madness’) and a plant-associated environment including fungal genomes and host plant material From bde5a7a3d5871a246317d1af0b0e09417e5138ff Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Tue, 13 Dec 2022 21:31:34 +0100 Subject: [PATCH 12/66] Update tutorial.md --- .../tutorials/taxonomic-assignment/tutorial.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 2c0419779a843e..505fb55a0460e3 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -251,7 +251,7 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 # Discussion: Choosing the right tool When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}) and their results are presented in the following section, with focus on tools that are available in Galaxy. -When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. +The benchmarking papers present different methods for comparing the available tools: The CAMI challenge is based on results of different labs that each used the CAMI dataset to perform their analysis on and send it back to the authors. In contrast, {% cite Ye.2019 %} performed all the analysis themselves. Additionally, the datasets used for both benchmarking approaches differ: in the CAMI dataset only ~30%-40% of reads are simulated from known taxa while the rest of the reads are from novel taxa, plasmids or simulated evolved strains. In contrast, {% cite Ye.2019 %} used International Metagenomics and Microbiome Standards Alliance (IMMSA) datasets, wherein the taxa are described better. When benchmarking different classification tools, several metrics are used to compare their performance: 1. **Precision**: proportion of true positive species identified in the sample divided by number of total species identified by the method @@ -259,7 +259,8 @@ When benchmarking different classification tools, several metrics are used to co 3. Precision-recall curve: each point represents the precision and recall scores at a specific abundance threshold → **area under the precision-recall curve (AUPR)** 4. **L2 distance**: representation of abundance profiles → how accurately the abundance of each species or genera in the resulting classification reflects the abundance of each species in the original biological sample (“ground truth”) - + +When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. ## Profiling tools @@ -271,7 +272,7 @@ Profilers, which are tools that investigate relative abundances of taxa within a However, some characteristics are common to all profilers: - Most profilers only perform well until the family level - Drastic decrease in performance between family and genus level, while little change between order and family level -- poorer performance of all profilers on CAMI datasets (only ~30%-40% of reads are simulated from known taxa, rest of the reads are from novel taxa, plasmids or simulated evolved strains) compared to International Metagenomics and Microbiome Standards Alliance (IMMSA) +- poorer performance of all profilers on CAMI datasets compared to International Metagenomics and Microbiome Standards Alliance (IMMSA) - Fidelity of abundance estimates decreases notably when viruses and plasmids were present - high numbers of false positive calls at low abundance - Taxonomic profilers vs profiles from taxonomic binning: From 5d62e0ac4ce60cd195deea652674fe9369377585 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Wed, 21 Dec 2022 16:42:04 +0100 Subject: [PATCH 13/66] Update tutorial.md add phinch hands on --- .../taxonomic-assignment/tutorial.md | 22 ++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 505fb55a0460e3..6fb07ebe40f67c 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -247,7 +247,27 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 >
- +## Visualize the taxonomical classification with Phinch + +__Phinch__ offers the user to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. Additionally, several different samples can easily be compared. +As a first step, we need to convert the Kraken output file into a kraken-biom file to make it accessible for Phinch. Fot this, we need to add a metadata file, provided here. xxx +When generating a metadata file for your own data, you can take this as an example and find the general guidelines here. xxx + +> ### {% icon hands_on %} Hands-on: Phinch +>1. Use {% tool [Kraken-biom] (toolshed.g2.bx.psu.edu/repos/iuc/kraken_biom/kraken_biom/1.2.0+galaxy1) %} to convert Kraken2 report into the correct format for phinch with the following parameters. +> - *"Input"*: `Kraken2 on dataset collection` +> - *"Sample Metadata file"*: `metadata.txt` +> - *"Output format"*: `JSON` +> +> 2. **View** the file in Phinch +> - Use {% tool [Phinch Visualisation] (interactive_tool_phinch) %} with the following paramters: +> - *"Input"*: `Kraken-biom output file` + +Important note: don't wait for Galaxy to finish the job! Your results are available directly, as Phinch is an interactive tool. To see them, click on User in the blue part right at the top of the Galaxy page, there you can find "Active interactive tools". When you go there, you will find Phinch running and can visit the website with your results by following the link "Phinch Visualisation of Kraken-biom output file" which is provided as the name of the job. + +{: .hands_on} + + # Discussion: Choosing the right tool When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}) and their results are presented in the following section, with focus on tools that are available in Galaxy. From 5605e2197c29eee9dc86e6fc74d7004fdd653293 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Wed, 21 Dec 2022 17:07:51 +0100 Subject: [PATCH 14/66] Update tutorial.md --- .../tutorials/taxonomic-assignment/tutorial.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 6fb07ebe40f67c..3148a9fa070fac 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -78,7 +78,7 @@ It is a collection of paired-end data with R1 being the forward reads and R2 bei > > {% snippet faqs/galaxy/histories_create_new.md %} > -> 2. Import the FASTQ file pairs from [Zenodo]({{ page.zenodo_link }}) or a data library: +> 2. Import the FASTQ file pairs from [Zenodo]({{ page.zenodo_link }}) or a data library: xxx > - `JP4D_R1.fastq.gz` > - `JP4D_R2.fastq.gz` -`JC1A_R1.fastq.gz` @@ -129,7 +129,7 @@ Let's have a look at the classification file. It has 5 columns: 2. Sequence ID 3. Taxonomy ID 4. Length of sequence (read1|read2 for paired reads) -5. indicates LCA mapping of each k-mer in the sequence |:| indicates end of first read, start of second read for paired reads --> example: "n k-mers assigned to taxon xxx" +5. indicates LCA mapping of each k-mer in the sequence |:| indicates end of first read, start of second read for paired reads ![Kraken2 Classification Output](../../taxonomic-assignment/images/Kraken2_classification_screenshot.png "Kraken2 Classification Output.") @@ -180,7 +180,7 @@ As both files contain a lot of information, we will use __Krona__ {% cite Ondov. # Hands on: estimating species abundance -A "simple and worthwile addition to Kraken for better abundance estimates" (xxx cite: benchmarking metagenomic tools for taxonomic classification; Ye et al) is called Bracken (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species." (xxx cite Bracken paper) +A "simple and worthwile addition to Kraken for better abundance estimates" {% cite Ye.2019 %} is called Bracken (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species." {% cite Lu.2017 %} > ### {% icon hands_on %} Hands-on: estimate species abundance with Bracken xxx > @@ -200,7 +200,7 @@ __Convert Kraken__ tool is designed to translate results of the Kraken metagenom > ### {% icon hands_on %} Hands-on: Convert Kraken2 Output > -> 1. {% tool [Convert Kraken](xxx) %} with the following parameters: +> 1. {% tool [Convert Kraken](toolshed.g2.bx.psu.edu/repos/devteam/kraken2tax/Kraken2Tax/1.1) %} with the following parameters: > - *"Choose dataset to convert"*: Datset collection: Classification Output of Kraken2 > - *"Select a taxonomy database"*: `2022-03-08` > - *"Read name"*: `column:2` @@ -215,7 +215,7 @@ __Krona__ allows hierarchical data to be explored with zooming, multi-layered pi > ### {% icon hands_on %} Hands-on: Visualize data with Krona > -> 1. {% tool [Krona pie chart](xxx) %} with the following parameters: +> 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1) %} with the following parameters: > - *"Type of input data"*: `taxonomy` > - *"Input file"*: Dataset collection: Output file of Convert Kraken > - *"Combine data from multiple data sets?"*: yes @@ -301,7 +301,7 @@ abundance estimation at higher ranks was more problematic for the binners -| Tool | version | available in Galaxy | In CAMI challenge, best method across metrics for\* : | additional features | Ye et al. benchmarking | +| Tool | version | available in Galaxy | In CAMI challenge, best method across metrics for\* : | additional features | {% cite Ye.2019 %} benchmarking | | --------- | -------------------- | ------------------- | ----------------------------------------------------- | ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | | mOTUs | 1.1.1 | no | \- | most memory efficient | | | mOTUs | 2.5.1 | no | marine; plant-associated | \- | | From b4acc9fa4b0afb323a2d45850ece710b8bed02ed Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Wed, 21 Dec 2022 17:11:02 +0100 Subject: [PATCH 15/66] Update tutorial.md --- topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 3148a9fa070fac..911c7a6182637f 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -251,7 +251,7 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 __Phinch__ offers the user to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. Additionally, several different samples can easily be compared. As a first step, we need to convert the Kraken output file into a kraken-biom file to make it accessible for Phinch. Fot this, we need to add a metadata file, provided here. xxx -When generating a metadata file for your own data, you can take this as an example and find the general guidelines here. xxx +When generating a metadata file for your own data, you can take this as an example and find the general guidelines [here](http://qiime.org/documentation/file_formats.html#mapping-file-overview) > ### {% icon hands_on %} Hands-on: Phinch >1. Use {% tool [Kraken-biom] (toolshed.g2.bx.psu.edu/repos/iuc/kraken_biom/kraken_biom/1.2.0+galaxy1) %} to convert Kraken2 report into the correct format for phinch with the following parameters. From d7ae980496cbe8ebc65b202361992cc995b023e1 Mon Sep 17 00:00:00 2001 From: Sophia120199 Date: Sat, 7 Jan 2023 10:22:00 +0000 Subject: [PATCH 16/66] add pavian --- .../taxonomic-assignment/tutorial.md | 24 +++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 911c7a6182637f..fba06e350190a3 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -268,6 +268,30 @@ Important note: don't wait for Galaxy to finish the job! Your results are availa {: .hands_on} +## Visualize the taxonomical classification with Pavian + +Pavian (pathogen visualization and more) is an interactive visualization tool for metagenomic data. It was developed for the clinical metagenomic problem to find a disease-causing pathogen in a patient sample, but it is useful to analyze and visualize any kind of metagenomics data. + +> ### {% icon hands_on %} Hands-on: Pavian + +> - Use {% tool [Pavian] (interactive_tool_pavian) %} with the following paramters: +> - *"Kraken and MetaPhlAn-style reports"*: `Kraken report file` + +Important note: don't wait for Galaxy to finish the job! Your results are available directly, as Pavian is an interactive tool. To see them, click on User in the blue part right at the top of the Galaxy page, there you can find "Active interactive tools". When you go there, you will find Pavian running and can visit the website with your results by following the link "Pavian Visualisation" which is provided as the name of the job. + +{: .hands_on} + + + Following the provided link, you will find the Pavian webpage. When choosing ‘Use data on server’ as data source, you should find your selected report files from galaxy. If you wish you can choose several sample files at once to be able to compare them in Pavian. After selecting ‘Read selected directories’, Pavian will show you a classification summary of all samples. + +Select ‘Comparison’ in the sidebar to find a table with taxa as rows and samples as columns. Above the table you can change several displaying options like which rank you would like to see or if you want the Z-scores to be displayed. Furthermore, the data can be filtered to exclude for example human reads as a host genome. + +Select ‘Sample’ in the sidebar to zoom into one sample with a Sankey diagram, which shows different taxons as lines with their width based on the quantity of the taxon. The diagram is shown for one sample but when hovering over nodes, the abundance of that specific taxon compared in all samples shown in a bar plot will appear on the right-hand side. +One additional feature of Pavian is the ‘Alignment Viewer’ to investigate for example if reads of a taxon cover the genome. Genome coverage can provide an indication whether an assignment is artificial or not. + +For further information on how to use Pavian please have a look into the [Pavian Walktrough](https://github.com/fbreitwieser/pavian/blob/master/vignettes/pavian-walkthrough.pdf) provided by F. Breitwieser. + + # Discussion: Choosing the right tool When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}) and their results are presented in the following section, with focus on tools that are available in Galaxy. From 98a4d7aaeb077696d9fc358ea2acdef1283e38d7 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Sat, 7 Jan 2023 19:19:47 +0100 Subject: [PATCH 17/66] Update tutorial.md --- .../tutorials/taxonomic-assignment/tutorial.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index fba06e350190a3..f75e7cc9d4cbfc 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -267,6 +267,20 @@ Important note: don't wait for Galaxy to finish the job! Your results are availa {: .hands_on} +When you follow the link to the Phinch webpage, you first see an overview of your samples. Here, you have the possibility to further filter your data, for example by date or location, depending on which information you provided in your metadata file. +Next, you click on ‘proceed to gallery’ to see an overview of all visualization options. + +Let’s have a look at the **taxonomy bar chart**. Here, you can see the abundance of different taxa depicted in different colors in your samples. On top of the chart you can select which rank is supposed to be shown in the chart. You can also change the display options to for example switch between value und percentage. If you hover over a specific taxon, you get exact information of the taxon’s name and the taxonomy occurrence in the sample. Furthermore, there is a search bar that allows you to search for specific taxa (within the rank that you chose to depict) and gives you the option to hide specific taxons. + +Next, lets go back to the gallery and choose the **bubble chart**. Here, you can find the distribution of taxa across the whole dataset at the rank that you can choose above the chart. When hovering over the bubbles, you get additional information concerning the taxon. Clicking on one bubble gives you the direct comparison of the distribution of this taxon in the different samples. To oder the bubbles according to their size you can choose the ‘list’ option shown right next to the taxonomy level. + +Another displaying option is the **Sankey diagram**, that is depicting the abundance of taxonomies as a flow chart. Again, you can choose the taxonomy level that you want to show in your diagram. When clicking on one bar of the diagram, this part is enlarged for better view. + +The **donut partition** summarizes the microbial community according to non-numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘environmental medium’ to see the difference between sediment and water (It doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). When clicking on one part of the donut you will also find the distribution of the taxon across the samples. On the right hand side you can additionally choose if you’d like to have dynamic y axis or prefer standard y axis to compare different donuts with each other. + +The **attributes column chart** summarizes the microbial community according to numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘geographic location’ to (again, it doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). + + ## Visualize the taxonomical classification with Pavian From d3df31e875283dbfa6bc4281107ebf45cd5a88b2 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Wed, 18 Jan 2023 16:33:57 +0100 Subject: [PATCH 18/66] Update tutorial.bib --- .../taxonomic-assignment/tutorial.bib | 29 +++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib index 74388871256a1e..e6b8248494a8f8 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib @@ -123,3 +123,32 @@ @article{Lu.2017 doi = {10.7717/peerj-cs.104} } +@article{Breitwieser.2020, + abstract = {SUMMARY + +Pavian is a web application for exploring classification results from metagenomics experiments. With Pavian, researchers can analyze, visualize and transform results from various classifiers-such as Kraken, Centrifuge and MethaPhlAn-using interactive data tables, heatmaps and Sankey flow diagrams. An interactive alignment coverage viewer can help in the validation of matches to a particular genome, which can be crucial when using metagenomics experiments for pathogen detection. + +AVAILABILITY AND IMPLEMENTATION + +Pavian is implemented in the R language as a modular Shiny web app and is freely available under GPL-3 from http://github.com/fbreitwieser/pavian.}, + author = {Breitwieser, Florian P. and Salzberg, Steven L.}, + year = {2020}, + title = {Pavian: interactive analysis of metagenomics data for microbiome studies and pathogen identification}, + pages = {1303--1304}, + volume = {36}, + number = {4}, + journal = {Bioinformatics (Oxford, England)}, + doi = {10.1093/bioinformatics/btz715} +} + + +@book{Bik.2014, + author = {Bik, Holly M.}, + year = {2014}, + title = {Phinch: An interactive, exploratory data visualization framework for --Omic datasets}, + doi = {10.1101/009944} +} + + + + From 9d83cebfdfb69a05e3559755542eb5f5f6740c84 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Wed, 18 Jan 2023 16:37:25 +0100 Subject: [PATCH 19/66] Update tutorial.md --- .../metagenomics/tutorials/taxonomic-assignment/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index f75e7cc9d4cbfc..98de77741b2d06 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -249,7 +249,7 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 ## Visualize the taxonomical classification with Phinch -__Phinch__ offers the user to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. Additionally, several different samples can easily be compared. +__Phinch__ ({% cite Bik.2014 %})offers the user to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. Additionally, several different samples can easily be compared. As a first step, we need to convert the Kraken output file into a kraken-biom file to make it accessible for Phinch. Fot this, we need to add a metadata file, provided here. xxx When generating a metadata file for your own data, you can take this as an example and find the general guidelines [here](http://qiime.org/documentation/file_formats.html#mapping-file-overview) @@ -284,7 +284,7 @@ The **attributes column chart** summarizes the microbial community according to ## Visualize the taxonomical classification with Pavian -Pavian (pathogen visualization and more) is an interactive visualization tool for metagenomic data. It was developed for the clinical metagenomic problem to find a disease-causing pathogen in a patient sample, but it is useful to analyze and visualize any kind of metagenomics data. +__Pavian__ (pathogen visualization and more) ({% cite Breitwieser.2020 %}) is an interactive visualization tool for metagenomic data. It was developed for the clinical metagenomic problem to find a disease-causing pathogen in a patient sample, but it is useful to analyze and visualize any kind of metagenomics data. > ### {% icon hands_on %} Hands-on: Pavian From f84bd55a7a6b4f787d5df5c2c3d75de4c2b5ab95 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Wed, 18 Jan 2023 17:35:08 +0100 Subject: [PATCH 20/66] Update tutorial.md --- .../tutorials/taxonomic-assignment/tutorial.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 98de77741b2d06..d9c78e55e5315d 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -68,10 +68,13 @@ After this theoretical introduction, let's now get hands on analyzing an actual # Background on data -The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas, that is studied because of its special environmental conditions {% cite Okie.2020 %}. The researchers collected samples directly from the pond (= control mesocosm, called JC1A in this tutorial) and fertilized some of the samples later on to receive nutrient enrichment (=Lagunita Fertilized Pond, called JP4D in this tutorial). In this way, they investigated the impact of nutrient enchriment on the microbial community. +The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas {% cite Okie.2020 %}. The researchers were interested in genomic traits that affect the rates and costs of biochemical information processing within cells. They performed a whole-ecosystem experiment, thus fertilizing the pond to achieve nutrient enriched conditions. The microbe samples collected from the Lagunita Fertilized Pond is calles JP4D in this tutorial. As control they used samples from a control mesocosm, called JC1A in this tutorial. You will realize that the datasets differ in size, but according to the authors this doesn't matter for their analysis of genomic traits. Also, they underline that differences between the two samples reflect trait-mediated ecological dynamics instead of microevolutionary changes as the duration of the experiment was only 32 days. This means that depending on available nutrients, specific lineages within the pond grow more successfully than others because of their genomic traits. + The datafiles are named according to the first four characters of the filenames. It is a collection of paired-end data with R1 being the forward reads and R2 being the reverse reads. Additionally, the reads have been trimmed using [__cutadapt__ ](https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html#trim-and-filter---short-reads) +So let's get started with uploading the datasets! + > ### {% icon hands_on %} Hands-on: Data upload > > 1. Create a new history for this exercise @@ -83,9 +86,6 @@ It is a collection of paired-end data with R1 being the forward reads and R2 bei > - `JP4D_R2.fastq.gz` -`JC1A_R1.fastq.gz` - `JC1A_R2.fastq.gz` -> `` -> -> ``` > > {% snippet faqs/galaxy/datasets_import_via_link.md %} > {% snippet faqs/galaxy/datasets_import_from_data_library.md %} From c82e1a0bd285fbf41053a584b6965b02d1abcb7b Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Fri, 3 Feb 2023 16:54:54 +0100 Subject: [PATCH 21/66] Update tutorial.md --- .../taxonomic-assignment/tutorial.md | 37 +++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index d9c78e55e5315d..1cca95fb91dfe4 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -280,6 +280,25 @@ The **donut partition** summarizes the microbial community according to non-nume The **attributes column chart** summarizes the microbial community according to numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘geographic location’ to (again, it doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). +> ### {% icon question %} Questions +>Let's have a look at our data and try to answer some questions. +>1. How many percent of the sample reads are bacteria and how many are eukaryota? +>2. How do you filter the data from eukaryotic reads? +> +> +> +> > ### {% icon solution %} Solution +> >1. Sample 0: 75,65 % bacteria; 24,51 % eukaryota + Sample 1: 92,70 % bacteria; 6,87 % eukaryota + + (go to taxonomy bar chart, choose kingdom and hover over the bars to find "taxonomy occurence in this sample") + +> >2. xxx comment: Search function of phinch shows confusing results when clicking on “hide” +> > +> +> {: .solution} +> +{: .question} ## Visualize the taxonomical classification with Pavian @@ -303,6 +322,24 @@ Select ‘Comparison’ in the sidebar to find a table with taxa as rows and sam Select ‘Sample’ in the sidebar to zoom into one sample with a Sankey diagram, which shows different taxons as lines with their width based on the quantity of the taxon. The diagram is shown for one sample but when hovering over nodes, the abundance of that specific taxon compared in all samples shown in a bar plot will appear on the right-hand side. One additional feature of Pavian is the ‘Alignment Viewer’ to investigate for example if reads of a taxon cover the genome. Genome coverage can provide an indication whether an assignment is artificial or not. +> ### {% icon question %} Questions +>Let's have a look at our data and try to answer some questions. +>1. What is the taxonomy occurence in percent of the Family Rhodobacteraceae in the two samples? +>2. Regarding the question the authors had, when collecting the data: What could it biologically mean, if one bacteria family increases their occurence in fertilized pond (JP4D)? +> +> +> +> > ### {% icon solution %} Solution +> >1. Sample JC1A: 12,82 % + Sample JP4D: 36,06 % + +> >2. It could mean that they have a survival advantage in the new environment. According to the authors this correlates with specific genomic traits that enable them to cope better with high nutrient availability. +> > +> +> {: .solution} +> +{: .question} + For further information on how to use Pavian please have a look into the [Pavian Walktrough](https://github.com/fbreitwieser/pavian/blob/master/vignettes/pavian-walkthrough.pdf) provided by F. Breitwieser. From a81dc5385c0e9093874c435dffab140377e783c8 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Sat, 4 Feb 2023 17:24:30 +0100 Subject: [PATCH 22/66] Update tutorial.md --- .../tutorials/taxonomic-assignment/tutorial.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 1cca95fb91dfe4..d0bd58833feb3e 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -185,8 +185,9 @@ A "simple and worthwile addition to Kraken for better abundance estimates" {% ci > ### {% icon hands_on %} Hands-on: estimate species abundance with Bracken xxx > > 1. {% tool [Bracken](https://toolshed.g2.bx.psu.edu/view/iuc/bracken/79450f7fd718) %} with the following parameters: -> - * kmer distribution? xxx -> which kind of report? xxx +> - * whta is kmer distribution? xxx +> which kind of report? +> what to do with bracken report? xxx > {: .hands_on} @@ -194,7 +195,7 @@ A "simple and worthwile addition to Kraken for better abundance estimates" {% ci Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the __Krona pie chart__ tool ({% cite Ondov.2011 %}). But first, we need to convert the output generated by Kraken2 so it can be used as an input from the Krona tool. -## Convert output from Kraken2 so it can be used for Krona +## Convert output from Kraken2 so it can be used for Krona (xxx krakentools for converting doesn't work) __Convert Kraken__ tool is designed to translate results of the Kraken metagenomic classifier (see citations below) to the full representation of NCBI taxonomy. It does so by using Taxonomic ID field provided by Kraken. The output of this tool can be directly visualized by the Krona tool. @@ -293,7 +294,8 @@ The **attributes column chart** summarizes the microbial community according to (go to taxonomy bar chart, choose kingdom and hover over the bars to find "taxonomy occurence in this sample") -> >2. xxx comment: Search function of phinch shows confusing results when clicking on “hide” +> >2. xxx Search function of phinch shows confusing results when clicking on “hide” +> > xxx phnich states invalid date format but when I change the date format in the metadata file from 20120706 to 2012-07-06, kraken-biom doesnt work any more > > > > {: .solution} From 26d3cbb9fe02b9fec4f713063db1d1c9c0b79e68 Mon Sep 17 00:00:00 2001 From: Sophia120199 Date: Thu, 16 Feb 2023 15:14:48 +0000 Subject: [PATCH 23/66] bracken update --- .../taxonomic-assignment/tutorial.md | 41 ++++++++++--------- 1 file changed, 21 insertions(+), 20 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index d0bd58833feb3e..c20e5868e6b33d 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -15,7 +15,7 @@ objectives: level: Introductory key_points: - To do -time_estimation: 45M +time_estimation: 1H contributors: - Sophia120199 --- @@ -111,6 +111,7 @@ Our input data is the DNA reads of microbes present at Cuatro Ciénegas. To find > - *"Confidence"*: `0.1` > - In *"Create Report"*: > - *"Print a report with aggregrate counts/clade to file"*: `Yes` + - *"Format report output like Kraken 1's kraken-mpa-report"*: `No` > - *"Select a Kraken2 database"*: `Standard plus protozoa & fungi (2021)` > > > ### {% icon comment %} Comment @@ -176,7 +177,7 @@ Let's also have a look at the report file. It has 2 columns: {: .question} -As both files contain a lot of information, we will use __Krona__ {% cite Ondov.2011 %}to visualize the data. +As both files contain a lot of information, we will use __Krona__ {% cite Ondov.2011 %} to visualize the data. # Hands on: estimating species abundance @@ -185,9 +186,9 @@ A "simple and worthwile addition to Kraken for better abundance estimates" {% ci > ### {% icon hands_on %} Hands-on: estimate species abundance with Bracken xxx > > 1. {% tool [Bracken](https://toolshed.g2.bx.psu.edu/view/iuc/bracken/79450f7fd718) %} with the following parameters: -> - * whta is kmer distribution? xxx -> which kind of report? -> what to do with bracken report? xxx + - *"Kraken report file"*: Report: Kraken2 on dataset collection (! do not use the classification file) + - *"Select a kmer distribution"*: `PlusPF (2021-05-17)` (! it is important to choose the same database that you also chose for Kraken2) +> - *"Produce Kraken-Style Bracken report"*: `yes` > {: .hands_on} @@ -195,17 +196,16 @@ A "simple and worthwile addition to Kraken for better abundance estimates" {% ci Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the __Krona pie chart__ tool ({% cite Ondov.2011 %}). But first, we need to convert the output generated by Kraken2 so it can be used as an input from the Krona tool. -## Convert output from Kraken2 so it can be used for Krona (xxx krakentools for converting doesn't work) +## Convert output from Kraken2 or Bracken so it can be used for Krona -__Convert Kraken__ tool is designed to translate results of the Kraken metagenomic classifier (see citations below) to the full representation of NCBI taxonomy. It does so by using Taxonomic ID field provided by Kraken. The output of this tool can be directly visualized by the Krona tool. +__Krakentools: Convert kraken report file__ tool {% cite Lu.2017 %} is designed to translate results of the Kraken metagenomic classifier (see citations below) to the full representation of NCBI taxonomy. It does so by using Taxonomic ID field provided by Kraken. The output of this tool can be directly visualized by the Krona tool. -> ### {% icon hands_on %} Hands-on: Convert Kraken2 Output +> ### {% icon hands_on %} Hands-on: Krakentools: Convert kraken report file > -> 1. {% tool [Convert Kraken](toolshed.g2.bx.psu.edu/repos/devteam/kraken2tax/Kraken2Tax/1.1) %} with the following parameters: -> - *"Choose dataset to convert"*: Datset collection: Classification Output of Kraken2 -> - *"Select a taxonomy database"*: `2022-03-08` -> - *"Read name"*: `column:2` -> - *"Taxonomy ID field"*: `column:3` +> {% tool [Krakentools: Convert kraken report file](toolshed.g2.bx.psu.edu/repos/iuc/krakentools_kreport2krona/krakentools_kreport2krona/1.2+galaxy0) %} with the following parameters: +> - *"Kraken report file"*: Report: Kraken2 on dataset collection + (or: Estimate Abundance at Taxonomic Level on collection: Kraken style report for Bracken) + > {: .hands_on} @@ -217,18 +217,18 @@ __Krona__ allows hierarchical data to be explored with zooming, multi-layered pi > ### {% icon hands_on %} Hands-on: Visualize data with Krona > > 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1) %} with the following parameters: -> - *"Type of input data"*: `taxonomy` -> - *"Input file"*: Dataset collection: Output file of Convert Kraken -> - *"Combine data from multiple data sets?"*: yes +> - *"Type of input data"*: `tabular` +> - *"Input file"*: Dataset collection: Output file of Krakentools: Convert kraken report file > {: .hands_on} -Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765e92bccb62d7f6daa/display/?preview=True&dataset=0&node=0&collapse=true&color=false&depth=8&font=11&key=true). Using the search bar we can check if certain taxa are present. +Let's take a look at the [result for Kraken2](https://usegalaxy.eu/datasets/4838ba20a6d86765e1d95e6747f5e42d/display/?preview=True&dataset=0&node=0&collapse=true&color=false&depth=8&font=11&key=true)/([result for Bracken](https://usegalaxy.eu/datasets/4838ba20a6d867652a9ece4a5ae1276d/display/?preview=True&dataset=0&node=0&collapse=true&color=false&depth=8&font=11&key=true).) Using the search bar we can check if certain taxa are present. > ### {% icon question %} Questions > +>Choose the sample JC1A: > 1. How many percent of the bacteria consists of the genus "paracoccus"? > 2. Is there any *Escherichia coli* present? If yes, how many reads were found? > 3. Where might the eukaryotic DNA come from? @@ -236,8 +236,8 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 > > > ### {% icon solution %} Solution > > -> > 1. 5 %. -> > 2. It is present and 162 reads were found. +> > 1. 3 %. +> > 2. It is present and 73 reads were found. > > 3. It's probably human contamination. > > {: .solution} @@ -250,7 +250,7 @@ Let's take a look at the [result](https://usegalaxy.eu/datasets/4838ba20a6d86765 ## Visualize the taxonomical classification with Phinch -__Phinch__ ({% cite Bik.2014 %})offers the user to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. Additionally, several different samples can easily be compared. +__Phinch__ ({% cite Bik.2014 %}) offers the user to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. Additionally, several different samples can easily be compared. As a first step, we need to convert the Kraken output file into a kraken-biom file to make it accessible for Phinch. Fot this, we need to add a metadata file, provided here. xxx When generating a metadata file for your own data, you can take this as an example and find the general guidelines [here](http://qiime.org/documentation/file_formats.html#mapping-file-overview) @@ -311,6 +311,7 @@ __Pavian__ (pathogen visualization and more) ({% cite Breitwieser.2020 %}) is an > - Use {% tool [Pavian] (interactive_tool_pavian) %} with the following paramters: > - *"Kraken and MetaPhlAn-style reports"*: `Kraken report file` + (or: Estimate Abundance at Taxonomic Level on collection: Kraken style report for Bracken) Important note: don't wait for Galaxy to finish the job! Your results are available directly, as Pavian is an interactive tool. To see them, click on User in the blue part right at the top of the Galaxy page, there you can find "Active interactive tools". When you go there, you will find Pavian running and can visit the website with your results by following the link "Pavian Visualisation" which is provided as the name of the job. 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-4. **Filter variants** to keep only the pass and good quality variants using **SnpSift Filter** ({% cite Cingolani2012 %}) +4. **Filter variants** to keep only the pass and good quality variants using **SnpSift Filter** ({% cite Cingolani2012 %}) > > [__LoFreq filter__](https://csb5.github.io/lofreq/) can be also used instead, both tools performs equal and fast results. @@ -1198,9 +1190,9 @@ We use **Heatmap w ggplot** tool along with other tabular manipulating tools to > > 3. Both samples were spiked with the same pathogen species, _S. enterica_, but not the same strain: > > > > - `Barcode10_Spike2` sample is spiked with _S. enterica subsp. enterica_ strain -> > - `Barcode11_Spike2b` sample is spiked with _S. enterica subsp. houtenae_ strain. -> > -> > This can be the main cause of the big similarities and the few differences of the bacteria pathogen **VF** gene products found between both of the two samples. +> > - `Barcode11_Spike2b` sample is spiked with _S. enterica subsp. houtenae_ strain. +> > +> > This can be the main cause of the big similarities and the few differences of the bacteria pathogen **VF** gene products found between both of the two samples. > > Other factors such as the **time** and **location** of the sampling may cause other differences. By knowing the metadata of the samples inputted for the workflows in real life we can understand what actually happened. 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diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib index e6b8248494a8f8..67aa7b5b241bf5 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.bib @@ -1,4 +1,49 @@ -% This file was created with Citavi 6.14.0.0 + +@book{Bik.2014, + author = {Bik, Holly M.}, + year = {2014}, + title = {Phinch: An interactive, exploratory data visualization framework for --Omic datasets}, + doi = {10.1101/009944} +} + +@article{blanco2023extending, + title={Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4}, + author={Blanco-M{\'\i}guez, Aitor and Beghini, Francesco and Cumbo, Fabio and McIver, Lauren J and Thompson, Kelsey N and Zolfo, Moreno and Manghi, Paolo and Dubois, Leonard and Huang, Kun D and Thomas, Andrew Maltez and others}, + journal={Nature Biotechnology}, + pages={1--12}, + year={2023}, + publisher={Nature Publishing Group US New York}, + doi={10.1038/s41587-023-01688-w} +} + + +@article{Breitwieser.2020, + abstract = {SUMMARY + +Pavian is a web application for exploring classification results from metagenomics experiments. With Pavian, researchers can analyze, visualize and transform results from various classifiers-such as Kraken, Centrifuge and MethaPhlAn-using interactive data tables, heatmaps and Sankey flow diagrams. An interactive alignment coverage viewer can help in the validation of matches to a particular genome, which can be crucial when using metagenomics experiments for pathogen detection. + +AVAILABILITY AND IMPLEMENTATION + +Pavian is implemented in the R language as a modular Shiny web app and is freely available under GPL-3 from http://github.com/fbreitwieser/pavian.}, + author = {Breitwieser, Florian P. and Salzberg, Steven L.}, + year = {2020}, + title = {Pavian: interactive analysis of metagenomics data for microbiome studies and pathogen identification}, + pages = {1303--1304}, + volume = {36}, + number = {4}, + journal = {Bioinformatics (Oxford, England)}, + doi = {10.1093/bioinformatics/btz715} +} + +@article{Lu.2017, + author = {Lu, Jennifer and Breitwieser, Florian P. and Thielen, Peter and Salzberg, Steven L.}, + year = {2017}, + title = {Bracken: estimating species abundance in metagenomics data}, + pages = {e104}, + volume = {3}, + journal = {PeerJ Computer Science}, + doi = {10.7717/peerj-cs.104} +} @article{Meyer.2022, abstract = {Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses.}, @@ -58,6 +103,14 @@ @article{Sczyrba.2017 doi = {10.1038/nmeth.4458} } +@article{whipps1988fungi, + title={Fungi biol control syst}, + author={Whipps, JM and Karen Lewis, RC}, + journal={Mycoparasitism and Plant Disease Control}, + pages={161--187}, + year={1988}, + publisher={Manchester University Press Manchester} +} @article{Wood.2019, abstract = {Although Kraken's k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85{\%}, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.}, @@ -103,51 +156,13 @@ @article{Ye.2019 } -@article{whipps1988fungi, - title={Fungi biol control syst}, - author={Whipps, JM and Karen Lewis, RC}, - journal={Mycoparasitism and Plant Disease Control}, - pages={161--187}, - year={1988}, - publisher={Manchester University Press Manchester} -} - -@article{Lu.2017, - author = {Lu, Jennifer and Breitwieser, Florian P. and Thielen, Peter and Salzberg, Steven L.}, - year = {2017}, - title = {Bracken: estimating species abundance in metagenomics data}, - pages = {e104}, - volume = {3}, - journal = {PeerJ Computer Science}, - doi = {10.7717/peerj-cs.104} -} -@article{Breitwieser.2020, - abstract = {SUMMARY -Pavian is a web application for exploring classification results from metagenomics experiments. With Pavian, researchers can analyze, visualize and transform results from various classifiers-such as Kraken, Centrifuge and MethaPhlAn-using interactive data tables, heatmaps and Sankey flow diagrams. An interactive alignment coverage viewer can help in the validation of matches to a particular genome, which can be crucial when using metagenomics experiments for pathogen detection. -AVAILABILITY AND IMPLEMENTATION -Pavian is implemented in the R language as a modular Shiny web app and is freely available under GPL-3 from http://github.com/fbreitwieser/pavian.}, - author = {Breitwieser, Florian P. and Salzberg, Steven L.}, - year = {2020}, - title = {Pavian: interactive analysis of metagenomics data for microbiome studies and pathogen identification}, - pages = {1303--1304}, - volume = {36}, - number = {4}, - journal = {Bioinformatics (Oxford, England)}, - doi = {10.1093/bioinformatics/btz715} -} -@book{Bik.2014, - author = {Bik, Holly M.}, - year = {2014}, - title = {Phinch: An interactive, exploratory data visualization framework for --Omic datasets}, - doi = {10.1101/009944} -} diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 2cdad9e66be257..5e53eba59b647e 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -6,393 +6,696 @@ questions: - Which species (or genera, families, ...) are present in my sample? objectives: - -- explain what taxonomic assignment is -- explain how taxonomic assignment works -- apply Kraken2 to assign taxonomic labels -- apply Krona to visualize results of assignment and understand the output -- identify taxonomic classification tool that fits best depending on their data +- Explain what taxonomic assignment is +- Explain how taxonomic assignment works +- Apply Kraken to assign taxonomic labels +- Apply Krona to visualize results of assignment and understand the output +- Identify taxonomic classification tool that fits best depending on their data level: Introductory key_points: - To do time_estimation: 1H -contributors: -- Sophia120199 +contributions: + authorship: + - Sophia120199 + - bebatut --- -> ### Agenda -> -> In this tutorial, we will deal with: -> -> 1. TOC -> {:toc} -> -{: .agenda} + # Introduction - -The term **"microbiome"** describes "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity" ({% cite whipps1988fungi %}). -Microbiome data can be gathered from different environments such as soil, water or the human gut. The biological interest lies in general in the question how the microbiome present at a specific site influences this environment. **Metagenomic samples** contain DNA from different organisms at a specific site, where the sample was collected. Metagenomic data can be used to find out which organisms coexist in that niche and which genes are present in the different organisms. +The term **"microbiome"** describes "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity" ({% cite whipps1988fungi %}). + +Microbiome data can be gathered from different environments such as soil, water or the human gut. The biological interest lies in general in the question how the microbiome present at a specific site influences this environment. To study a microbiome, we need to use indirect methods like metagenomics or metatranscriptomics. +**Metagenomic samples** contain DNA from different organisms at a specific site, where the sample was collected. Metagenomic data can be used to find out which organisms coexist in that niche and which genes are present in the different organisms. Metatranscriptomic samples include the transcribed gene products, thus RNA, that therefore allow to not only study the presence of genes but additionally their expression in the given environment. The following tutorial will focus on metagenomics data, but the principle is the same for metatranscriptomics data. -The investigation of microbes present at a specific site and their relative abundance is also called **“microbial community profiling”**. -Basic for this is to find out which microbes are present in the sample. This can be achieved for all known microbes, where the DNA sequence specific for a certain species is known. The process of sorting single DNA reads derived from sequencing to a species (or other ranks) is called **taxonomic assignment**. +The investigation of microorganisms present at a specific site and their relative abundance is also called **"microbial community profiling"**. +Basic for this is to find out which microorganisms are present in the sample. This can be achieved for all known microbes, where the DNA sequence specific for a certain species is known. + +For that we try to **identify the taxon** to which each individual reads belong. + +{% snippet topics/metagenomics/faqs/taxon.md %} -When talking about taxonomic assignment or taxonomic classification, most of the time we actually talk about two methods, that in practice are often used interchangeably: while **taxonomic binning** is the classification of individual sequence reads to reference taxa, **taxonomic profiling** refers to investigating relative abundances of taxa within a dataset but not to the classification of individual reads. -Tools for taxonomic classification can be divided into three groups. Nevertheless, all of them require a pre-computed database based on previously sequenced microbial DNA or protein sequences. -1. **DNA-to-DNA** classification tools compare sequencing reads with genomic databases of DNA sequences (Bracken, Kraken, Kraken2, MegaBLAST) -2. **DNA-to-Protein** classification tools compare sequencing reads with genomic databases of protein sequences (more computationally intensive because of analysis of all six frames of potential DNA-to amino acid translation) (DIAMOND) -3. **Marker based** classification tools use a reference database that only includes a subset of gene sequences (e.g. 16S rRNA sequence), which is quick, but introduces bias (MetaPhlAn2) - -> ### {% icon details %} More details on taxonomy -> -> In general, taxonomy is the study of sorting organisms into different groups within a larger system according to similarities and differences. The groups are named, defined, classified and hierarchically ordered. The principal ranks from top to bottom (*with examples for the human being*) are domain (*eukarya*), kingdom (*animalia*), phylum (*chordata*), class (*mammalia*), order (*primates*), family (*hominidae*), genus (*homo*), and species (*sapiens*). From this classification, one can generate a tree of life, also known as a phylogenetic tree. It is a rooted tree that describes the relationship of all life on earth. At the root sits the “last universal common ancestor” and the three main branches (in taxonomy also called domains) are bacteria, archaea and eukaryotes. Most important for this is the idea that all life on earth is derived from a common ancestor and therefore when comparing two species, you will -sooner or later- find a common ancestor for all of them. -> -> {: .details} - When we talk about metagenomic data here, what we start with is sequences derived from DNA fragments that could be isolated from the sample of interest. Ideally, from all microbes present in the sample, we would also find DNA. The underlying idea of taxonomic assignment is to compare the DNA sequences found in the sample (reads) to DNA sequences of a database. When a read matches a database DNA sequence of a known microbe, we can derive a list with microbes present in the sample. -The comparison of reads to database sequences can be done in different ways, leading to three different types of taxonomic assignment: k-mer based, gene-based and genome-based analysis. - -- For the **k-mer based** analysis, databases as well as the samples DNA are broken into k-mers about 30 bp length for comparison. From all the genomes in the database, where a specific k-mer is found, a lowest common ancestor (LCA) tree is derived and the abundance of k-mers within the tree is counted. This is the basis for a root-to-leaf path calculation, where the path with the highest score is used for classification of the sample. By counting the abundance of k-mers, also an estimation of relative abundance of taxa is possible. The major advantage of k-mer based analysis is the low compute cost. Major disadvantages are the low detection accuracy, that the unclassified percentage is unknown and that there is no gene detection, no SNVs detection and no genomic comparison possible. An example for a k-mer based analysis tool is Kraken2, which will be used in this tutorial - -![Kraken functionality](../../taxonomic-assignment/images/Kraken_algorithm.png "Kraken functionality.") {% cite Wood.2014 %} - -- For the **gene based** analysis, reads are aligned to reference genes about 1 kbp length. Next, marker genes are used to estimate species abundance. Furthermore, genes can be analyzed in isolation for presence or absence in a specific condition. -The major advantage is the detection of the pangenome (entire set of genes within a species). Major disadvantages are the high compute cost, low detection accuracy and that the unclassified percentage is unknown. At least intragenic SNVs can be detected and low-resolution genomic comparison is possible. - -- For the **genome based** analysis, read pairs of 150 bp length are aligned to reference genomes of about 3 Mbp length. Considering the coverage and breadth, genomes are used to measure genome abundance. Furthermore, genes can be analyzed in genomic context. Advantages of this method are the high detection accuracy, that the unclassified percentage is known, that all SNVs can be detected and that high-resolution genomic comparisons are possible. This method takes medium compute cost. - -After this theoretical introduction, let's now get hands on analyzing an actual dataset! - - - + +When talking about taxonomic assignment or taxonomic classification, most of the time we actually talk about two methods, that in practice are often used interchangeably: +- **taxonomic binning**: the clustering of individual sequence reads based on similarities criteria and assignation of clusters to reference taxa +- **taxonomic profiling**: classification of individual reads to reference taxa to extract the relative abundances of the different taxa + +## Taxonomic classification + +Tools for taxonomic classification can be divided into three groups. Nevertheless, all of them require a pre-computed database based on previously sequenced microbial DNA or protein sequences. +1. **DNA-to-DNA**: comparison of sequencing reads with genomic databases of DNA sequences, with tools like Kraken ({% cite Wood2014 %}) +2. **DNA-to-Protein** : compare sequencing reads with protein databases (more computationally intensive because of analysis of all six frames of potential DNA-to amino acid translation, with tools like DIAMOND +3. **Marker based**: search for marker genes (e.g. 16S rRNA sequence) in reads, which is quick, but introduces bias, with tools like MetaPhlAn ({% cite blanco2023extending %}) + +The comparison of reads to database sequences can be done in different ways, leading to three different types of taxonomic assignment: + +- **Genome based** approach + + Reads are aligned to reference genomes. Considering the coverage and breadth, genomes are used to measure genome abundance. Furthermore, genes can be analyzed in genomic context. Advantages of this method are the high detection accuracy, that the unclassified percentage is known, that all SNVs can be detected and that high-resolution genomic comparisons are possible. This method takes medium compute cost. + +- **Gene based** approach + + Reads are aligned to reference genes. Next, marker genes are used to estimate species abundance. Furthermore, genes can be analyzed in isolation for presence or absence in a specific condition. The major advantage is the detection of the pangenome (entire set of genes within a species). Major disadvantages are the high compute cost, low detection accuracy and that the unclassified percentage is unknown. At least intragenic SNVs can be detected and low-resolution genomic comparison is possible. + +- **k-mer based** approach + + Databases as well as the samples DNA are broken into strings of length $$k$$ for comparison. From all the genomes in the database, where a specific k-mer is found, a lowest common ancestor (LCA) tree is derived and the abundance of k-mers within the tree is counted. This is the basis for a root-to-leaf path calculation, where the path with the highest score is used for classification of the sample. By counting the abundance of k-mers, also an estimation of relative abundance of taxa is possible. The major advantage of k-mer based analysis is the low compute cost. Major disadvantages are the low detection accuracy, that the unclassified percentage is unknown and that there is no gene detection, no SNVs detection and no genomic comparison possible. An example for a k-mer based analysis tool is Kraken, which will be used in this tutorial + +After this theoretical introduction, let's now get hands on analyzing an actual dataset! + # Background on data - -The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas {% cite Okie.2020 %}. The researchers were interested in genomic traits that affect the rates and costs of biochemical information processing within cells. They performed a whole-ecosystem experiment, thus fertilizing the pond to achieve nutrient enriched conditions. The microbe samples collected from the Lagunita Fertilized Pond is calles JP4D in this tutorial. As control they used samples from a control mesocosm, called JC1A in this tutorial. You will realize that the datasets differ in size, but according to the authors this doesn't matter for their analysis of genomic traits. Also, they underline that differences between the two samples reflect trait-mediated ecological dynamics instead of microevolutionary changes as the duration of the experiment was only 32 days. This means that depending on available nutrients, specific lineages within the pond grow more successfully than others because of their genomic traits. + +The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas {% cite Okie.2020 %}. The researchers were interested in genomic traits that affect the rates and costs of biochemical information processing within cells. They performed a whole-ecosystem experiment, thus fertilizing the pond to achieve nutrient enriched conditions. + +Here we will use 2 datasets: +- `JP4D`: a microbiome sample collected from the Lagunita Fertilized Pond +- `JC1A`: a **control** samples from a control mesocosm. + +The datasets differ in size, but according to the authors this doesn't matter for their analysis of genomic traits. Also, they underline that differences between the two samples reflect trait-mediated ecological dynamics instead of microevolutionary changes as the duration of the experiment was only 32 days. This means that depending on available nutrients, specific lineages within the pond grow more successfully than others because of their genomic traits. The datafiles are named according to the first four characters of the filenames. It is a collection of paired-end data with R1 being the forward reads and R2 being the reverse reads. Additionally, the reads have been trimmed using [__cutadapt__ ](https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html#trim-and-filter---short-reads) -So let's get started with uploading the datasets! +> +> +> In this tutorial, we will cover: +> +> 1. TOC +> {:toc} +> +{: .agenda} + +# Prepare Galaxy and data + +Any analysis should get its own Galaxy history. So let's start by creating a new one: -> ### {% icon hands_on %} Hands-on: Data upload +> Data upload > -> 1. Create a new history for this exercise +> 1. Create a new history for this analysis > > {% snippet faqs/galaxy/histories_create_new.md %} > -> 2. Import the FASTQ file pairs from [Zenodo]({{ page.zenodo_link }}) or a data library: xxx -> - `JP4D_R1.fastq.gz` -> - `JP4D_R2.fastq.gz` - -`JC1A_R1.fastq.gz` - - `JC1A_R2.fastq.gz` +> 2. Rename the history +> +> {% snippet faqs/galaxy/histories_rename.md %} +> +{: .hands_on} + +We need now to import the data + +> Import datasets +> +> 1. Import the following samples via link from [Zenodo]({{ page.zenodo_link }}) or Galaxy shared data libraries: +> +> ```text +> {{ page.zenodo_link }}/files/JC1A_R1.fastq.gz +> {{ page.zenodo_link }}/files/JC1A_R2.fastq.gz +> {{ page.zenodo_link }}/files/JP4D_R1.fastq.gz +> {{ page.zenodo_link }}/files/JP4D_R2.fastq.gz +> ``` > > {% snippet faqs/galaxy/datasets_import_via_link.md %} > {% snippet faqs/galaxy/datasets_import_from_data_library.md %} - > -> 3. Create a paired collection. +> 2. 3. Create a paired collection. > > {% snippet faqs/galaxy/collections_build_list_paired.md %} > {: .hands_on} +# k-mer based taxonomic assignment of metagenomic data +> +> +> 1. What are the percentage on unclassified for JC1A and JP4D? +> 2. What are the kindgoms found for JC1A and JP4D? +> 3. Where might the eukaryotic DNA come from? +> 4. How is the diversity of Proteobacteria in JC1A and JP4D? +> +> > +> > +> > 1. 78% for JC1A and 90% for JP4D +> > 2. Kindgoms: +> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus +> > - JP4D: 10% Bacteria, 0.7% Eukaryota +> > 3. It seems to be human contamination +> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. +> +> {: .solution} +> +{: .question} +Our input data is the DNA reads of microbes present at Cuatro Ciénegas. + +To find out which microorganisms are present, we will compare the reads of the sample to a reference database, i.e. sequences of known microorganisms stored in a database, using **Kraken2** ({% cite wood2019improved %}). + +{% snippet topics/metagenomics/faqs/kraken.md %} - -# Hands on: k-mer based taxonomic assignment of metagenomic data +For this tutorial, we will use the Standard (archaea, bacteria, viral, plasmid, human, UniVec_Core) plus protozoa & fungi database, which means: -Our input data is the DNA reads of microbes present at Cuatro Ciénegas. To find out which microbes are present, we will compare the reads of the sample to sequences of known microbes stored in a database using the algorithm Kraken2, which is k-mer based. For this tutorial, we will use the Standard plus protozoa & fungi (2021) database. The Standard database includes archaea, bacteria, viral, plasmid, human, and UniVec_Core data. - -> ### {% icon hands_on %} Hands-on: Assign taxonomic labels with Kraken2 +- archaea: RefSeq complete archaeal genomes/proteins +- bacteria: RefSeq complete bacterial genomes/proteins +- plasmid: RefSeq plasmid nucleotide/protein sequences +- viral: RefSeq complete viral genomes/proteins +- human: GRCh38 human genome/proteins +- fungi: RefSeq complete fungal genomes/proteins +- plant: RefSeq complete plant genomes/proteins +- protozoa: RefSeq complete protozoan genomes/proteins +- UniVec_Core: A subset of UniVec, NCBI-supplied database of vector, adapter, linker, and primer sequences that may be contaminating sequencing projects and/or assemblies, chosen to minimize false positive hits to the vector database + +> Assign taxonomic labels with Kraken2 > -> 1. {% tool [Kraken2](toolshed.g2.bx.psu.edu/repos/iuc/kraken2/kraken2/2.0.8_beta+galaxy0) %} with the following parameters: +> 1. {% tool [Kraken2](toolshed.g2.bx.psu.edu/repos/iuc/kraken2/kraken2/2.1.1+galaxy1) %} with the following parameters: > - *"Single or paired reads"*: `Paired Collection` -> - *"Print scientific names instead of just taxids"*: `No` +> - {% icon param-collection %} *"Collection of paired reads"*: Input paired collection > - *"Confidence"*: `0.1` +> +> A confidence score of 0.1 means that at least 10% of the k-mers should match entries in the database. This value can be reduced if a less restrictive taxonomic assignation is desired. +> > - In *"Create Report"*: > - *"Print a report with aggregrate counts/clade to file"*: `Yes` - - *"Format report output like Kraken 1's kraken-mpa-report"*: `No` -> - *"Select a Kraken2 database"*: `Standard plus protozoa & fungi (2021)` -> -> > ### {% icon comment %} Comment -> > -> > A confidence score of 0.1 means that at least 10% of the k-mers should match entries in the database. This value can be reduced if a less restrictive taxonomic assignation is desired. -> {: .comment} +> - *"Select a Kraken2 database"*: most recent `Prebuilt Refseq indexes: PlusPF` > {: .hands_on} - - -Kraken2 will create two output files called "Classification" and "Report". - -Let's have a look at the classification file. It has 5 columns: - -1. C/U: classified/unclassified -2. Sequence ID -3. Taxonomy ID -4. Length of sequence (read1|read2 for paired reads) -5. indicates LCA mapping of each k-mer in the sequence |:| indicates end of first read, start of second read for paired reads - - -![Kraken2 Classification Output](../../taxonomic-assignment/images/Kraken2_classification_screenshot.png "Kraken2 Classification Output.") - -> ### {% icon question %} Questions ->Let's have a look at the first line of the Kraken2 classification output. ->1. Is it classified or unclassified? ->2. How long is the sequenece? ->3. What is the taxonomy ID? -> -> -> -> > ### {% icon solution %} Solution -> >1. classified -> >2. 142 bp -> >3. 398580 -> > + +**Kraken2** will create two outputs for each dataset + +- **Classification**: tabular files with one line for each sequence classified by Kraken and 5 columns: + + 1. `C`/`U`: a one letter indicating if the sequence classified or unclassified + 2. Sequence ID as in the input file + 3. NCBI taxonomy ID assigned to the sequence, or 0 if unclassified + 4. Length of sequence in bp (read1|read2 for paired reads) + 5. A space-delimited list indicating the lowest common ancestor (LCA) mapping of each k-mer in the sequence + + For example, `562:13 561:4 A:31 0:1 562:3` would indicate that: + 1. The first 13 k-mers mapped to taxonomy ID #562 + 2. The next 4 k-mers mapped to taxonomy ID #561 + 3. The next 31 k-mers contained an ambiguous nucleotide + 4. The next k-mer was not in the database + 5. The last 3 k-mers mapped to taxonomy ID #562 + + `|:|` indicates end of first read, start of second read for paired reads + + For JC1A: + + ``` + Column 1 Column 2 Column 3 Column 4 Column 5 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:13417:1998 0 151|190 A:18 0:14 2055:5 0:1 2220095:5 0:74 |:| 0:3 A:54 2:1 0:32 204455:1 2823043:5 0:60 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:15782:2187 0 169|173 0:101 37329:1 0:33 |:| 0:10 2751189:5 0:30 1883:2 0:39 2609255:5 0:48 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:11745:2196 0 235|214 0:173 2282523:5 2746321:2 0:21 |:| 0:65 2746321:2 2282523:5 0:108 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:18358:2213 0 251|251 0:35 281093:5 0:3 651822:5 0:145 106591:3 0:21 |:| 0:64 106591:3 0:145 651822:5 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:14892:2226 0 68|59 0:34 |:| 0:25 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:18764:2247 0 146|146 0:112 |:| 0:112 + C MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:12147:2252 9606 220|220 9606:148 0:19 9606:19 |:| 9606:19 0:19 9606:148 + ``` + + > + > + > For JC1A sample + > 1. Is the first sequence in the file classified or unclassified? + > 2. What is the taxonomy ID assigned to the first classified sequence? + > 3. What is the corresponding taxon? + > + > > + > > 1. classified + > > 2. 9606, for the line 7 + > > 3. 9606 corresponds to Homo sapiens when looking at NCBI. + > + > {: .solution} + > + {: .question} + + +- **Report**: tabular files with one line per taxon and 6 columns or fields + + 1. Percentage of fragments covered by the clade rooted at this taxon + 2. Number of fragments covered by the clade rooted at this taxon + 3. Number of fragments assigned directly to this taxon + 4. A rank code, indicating + - (U)nclassified + - (R)oot + - (D)omain + - (K)ingdom + - (P)hylum + - (C)lass + - (O)rder + - (F)amily + - (G)enus, or + - (S)pecies + + Taxa that are not at any of these 10 ranks have a rank code that is formed by using the rank code of the closest ancestor rank with a number indicating the distance from that rank. E.g., `G2` is a rank code indicating a taxon is between genus and species and the grandparent taxon is at the genus rank. + + 5. NCBI taxonomic ID number + 6. Indented scientific name + + + ``` + Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 + 76.86 105399 105399 U 0 unclassified + 23.14 31740 1197 R 1 root + 22.20 30448 312 R1 131567 cellular organisms + 12.58 17254 3767 D 2 Bacteria + 8.77 12027 2867 P 1224 Proteobacteria + 4.94 6779 3494 C 28211 Alphaproteobacteria + 1.30 1782 1085 O 204455 Rhodobacterales + 0.43 593 461 F 31989 Rhodobacteraceae + 0.05 74 53 G 265 Paracoccus + ``` + + > > +> +> 1. What are the percentage on unclassified for JC1A and JP4D? +> 2. What are the kindgoms found for JC1A and JP4D? +> 3. Where might the eukaryotic DNA come from? +> 4. How is the diversity of Proteobacteria in JC1A and JP4D? +> +> > +> > +> > 1. 78% for JC1A and 90% for JP4D +> > 2. Kindgoms: +> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus +> > - JP4D: 10% Bacteria, 0.7% Eukaryota +> > 3. It seems to be human contamination +> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. > > {: .solution} > {: .question} + > + > 1. How much reads have not been classified? + > 2. How many taxons were found? + > 3. How much reads have been classified to Bacteria? + > 4. What are the different phylum identified? + > + > > + > > + > > 1. 76.86% of the reads + > > 2. There are 536 lines so 535 taxons + > > 3. 12.58% of reads have been assigned to Bacteria + > > 4. If we filter for P in column 4 (using {% tool [Filter](Filter1) %}), we can find 16 phylum: Proteobacteria, Actinobacteria, Firmicutes, Chloroflexi, Cyanobacteria, Bacteroidetes, Chlorobi, Planctomycetes, Verrucomicrobia, Spirochaetes, Acidobacteria, Chordata, Ascomycota, Apicomplexa, Negarnaviricota, Uroviricota + > + > {: .solution} + > + {: .question} + + +Getting an overview of the assignation is not straightforward with the **Kraken2** outputs directly. We can use visualisation tools for that. - - -Let's also have a look at the report file. It has 2 columns: -1. taxon name grouped into d_domain, p_phylum, c_class, o_order, f_family, g_genus, s_species -2. number of reads assigned to specific taxon - -![Kraken2 Report Output](../../taxonomic-assignment/images/Kraken2_report_screenshot.png "Kraken2 Report Output.") +A "simple and worthwile addition to Kraken for better abundance estimates" {% cite Ye.2019 %} is called __Bracken__ (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species." {% cite Lu.2017 %} -> ### {% icon question %} Questions +> estimate species abundance with Bracken (Name in Galaxy: Estimate Abundance at Taxonomic Level) > -> 1. What family does Paracoccus sp. MC1862 belong to? -> 2. How many reads were assigned to Paracoccus sp. MC1862 +> 1. {% tool [Bracken](toolshed.g2.bx.psu.edu/view/iuc/bracken/79450f7fd718) %} with the following parameters: +> - *"Kraken report file"*: Report: Kraken2 on dataset collection (! do not use the classification file) +> - *"Select a kmer distribution"*: `PlusPF (2021-05-17)` (! it is important to choose the same database that you also chose for Kraken2) +> - *"Produce Kraken-Style Bracken report"*: `yes` > +{: .hands_on} + +# Visualization of taxonomic assignment + +Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data. There are several tools for that: +- __Krona pie chart__ tool ({% cite Ondov.2011 %}) +- __Phinch__ ({% cite Bik.2014 %}) +- __Pavian__ ({% cite Breitwieser.2020 %}) + +## Visualisation using Krona + +__Krona__ creates an interactive HTML file allowing hierarchical data to be explored with zooming, multi-layered pie charts. With this tool, we can easily visualize the composition of the bacterial communities and compare how the populations of microorganisms are modified according to the conditions of the environment. + +Kraken outputs can not be given directly to **Kro> +> +> 1. What are the percentage on unclassified for JC1A and JP4D? +> 2. What are the kindgoms found for JC1A and JP4D? +> 3. Where might the eukaryotic DNA come from? +> 4. How is the diversity of Proteobacteria in JC1A and JP4D? > -> > ### {% icon solution %} Solution +> > > > -> > 1. Rhodobacteraceae -> > 2. 917 +> > 1. 78% for JC1A and 90% for JP4D +> > 2. Kindgoms: +> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus +> > - JP4D: 10% Bacteria, 0.7% Eukaryota +> > 3. It seems to be human contamination +> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. > > {: .solution} > -{: .question} +{: .question}na**, they first need to be converted. - -As both files contain a lot of information, we will use __Krona__ {% cite Ondov.2011 %} to visualize the data. - -# Hands on: estimating species abundance +__Krakentools__ ({% cite Lu.2017 %}) is a suite of tools to work on Kraken outputs. It include a tool designed to translate results of the Kraken metagenomic classifier to the full representation of NCBI taxonomy. The output of this tool can be directly visualized by the Krona tool. -A "simple and worthwile addition to Kraken for better abundance estimates" {% cite Ye.2019 %} is called __Bracken__ (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species." {% cite Lu.2017 %} - -> ### {% icon hands_on %} Hands-on: estimate species abundance with Bracken (Name in Galaxy: Estimate Abundance at Taxonomic Level) +> Convert Kraken report file > -> 1. {% tool [Bracken](https://toolshed.g2.bx.psu.edu/view/iuc/bracken/79450f7fd718) %} with the following parameters: - - *"Kraken report file"*: Report: Kraken2 on dataset collection (! do not use the classification file) - - *"Select a kmer distribution"*: `PlusPF (2021-05-17)` (! it is important to choose the same database that you also chose for Kraken2) -> - *"Produce Kraken-Style Bracken report"*: `yes` +> 1. {% tool [Krakentools: Convert kraken report file](toolshed.g2.bx.psu.edu/repos/iuc/krakentools_kreport2krona/krakentools_kreport2krona/1.2+galaxy0) %} with the following parameters: +> - {% icon param-collection %} *"Kraken report file"*: **Report** collection of **Kraken** > +> 2. Inspect the generated output for JC1A {: .hands_on} -# Visualization of taxonomic assignment - -Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data, for which we will use the __Krona pie chart__ tool ({% cite Ondov.2011 %}). But first, we need to convert the output generated by Kraken2 so it can be used as an input from the Krona tool. - -## Convert output from Kraken2 or Bracken so it can be used for Krona - -__Krakentools: Convert kraken report file__ tool {% cite Lu.2017 %} is designed to translate results of the Kraken metagenomic classifier (see citations below) to the full representation of NCBI taxonomy. It does so by using Taxonomic ID field provided by Kraken. The output of this tool can be directly visualized by the Krona tool. - -> ### {% icon hands_on %} Hands-on: Krakentools: Convert kraken report file -> -> {% tool [Krakentools: Convert kraken report file](toolshed.g2.bx.psu.edu/repos/iuc/krakentools_kreport2krona/krakentools_kreport2krona/1.2+galaxy0) %} with the following parameters: -> - *"Kraken report file"*: Report: Kraken2 on dataset collection - (or: Estimate Abundance at Taxonomic Level on collection: Kraken style report for Bracken) +> > -{: .hands_on} - - -## Visualize the taxonomical classification with Krona - -__Krona__ allows hierarchical data to be explored with zooming, multi-layered pie charts. With this tool, we can easily visualize the composition of the bacterial communities and compare how the populations of microorganisms are modified according to the conditions of the environment. - -> ### {% icon hands_on %} Hands-on: Visualize data with Krona -> -> 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1) %} with the following parameters: -> - *"Type of input data"*: `tabular` -> - *"Input file"*: Dataset collection: Output file of Krakentools: Convert kraken report file +> ```> > -{: .hands_on} - - -Let's take a look at the [result for Kraken2](https://usegalaxy.eu/datasets/4838ba20a6d86765e1d95e6747f5e42d/display/?preview=True&dataset=0&node=0&collapse=true&color=false&depth=8&font=11&key=true)/([result for Bracken](https://usegalaxy.eu/datasets/4838ba20a6d867652a9ece4a5ae1276d/display/?preview=True&dataset=0&node=0&collapse=true&color=false&depth=8&font=11&key=true).) Using the search bar we can check if certain taxa are present. - - -> ### {% icon question %} Questions -> ->Choose the sample JC1A: -> 1. How many percent of the bacteria consists of the genus "paracoccus"? -> 2. Is there any *Escherichia coli* present? If yes, how many reads were found? +> 1. What are the percentage on unclassified for JC1A and JP4D? +> 2. What are the kindgoms found for JC1A and JP4D? > 3. Where might the eukaryotic DNA come from? +> 4. How is the diversity of Proteobacteria in JC1A and JP4D? +> +> > +> > +> > 1. 78% for JC1A and 90% for JP4D +> > 2. Kindgoms: +> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus +> > - JP4D: 10% Bacteria, 0.7% Eukaryota +> > 3. It seems to be human contamination +> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. > +> {: .solution} > -> > ### {% icon solution %} Solution +{: .question} +> 3868 k__Bacteria +> 2867 k__Bacteria p__Proteobacteria +> 3494 k__Bacteria p__Proteobacteria c__Alphaproteobacteria +> 1085 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales +> 461 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae +> 53 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus +> 10 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_pantotrophus +> 6 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sanguinis +> 4 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sp._AK26 +> 1 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sp._MA +> ``` +> +> 1. What are the different columns? +> 2. What are the lines? +> +> > > > -> > 1. 3 %. -> > 2. It is present and 73 reads were found. -> > 3. It's probably human contamination. +> > 1. Column 1 seems to correspond to the number of fragments covered by a taxon, the columns after represent the different taxonomic level (from kingdom to species) +> > 2. A line is a taxon with its hierarchy and the number of reads assigned to it > > {: .solution} > {: .question} ->

->
- - -## Visualize the taxonomical classification with Phinch - -__Phinch__ ({% cite Bik.2014 %}) offers the user to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. Additionally, several different samples can easily be compared. -As a first step, we need to convert the Kraken output file into a kraken-biom file to make it accessible for Phinch. Fot this, we need to add a metadata file, provided here. xxx -When generating a metadata file for your own data, you can take this as an example and find the general guidelines [here](http://qiime.org/documentation/file_formats.html#mapping-file-overview) +Let's now run **Krona** -> ### {% icon hands_on %} Hands-on: Phinch ->1. Use {% tool [Kraken-biom] (toolshed.g2.bx.psu.edu/repos/iuc/kraken_biom/kraken_biom/1.2.0+galaxy1) %} to convert Kraken2 report into the correct format for phinch with the following parameters. -> - *"Input"*: `Kraken2 on dataset collection` -> - *"Sample Metadata file"*: `metadata.txt` -> - *"Output format"*: `JSON` +> Generate Krona visualisation +> 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1) %} with the following parameters: +> - *"Type of input data"*: `tabular` +> - {% icon param-collection %} *"Input file"*: output of **Krakentools** > -> 2. **View** the file in Phinch -> - Use {% tool [Phinch Visualisation] (interactive_tool_phinch) %} with the following paramters: -> - *"Input"*: `Kraken-biom output file` - -Important note: don't wait for Galaxy to finish the job! Your results are available directly, as Phinch is an interactive tool. To see them, click on User in the blue part right at the top of the Galaxy page, there you can find "Active interactive tools". When you go there, you will find Phinch running and can visit the website with your results by following the link "Phinch Visualisation of Kraken-biom output file" which is provided as the name of the job. +> 2. Inspect the generated file +{: .hands_on} -{: .hands_on} + -When you follow the link to the Phinch webpage, you first see an overview of your samples. Here, you have the possibility to further filter your data, for example by date or location, depending on which information you provided in your metadata file. -Next, you click on ‘proceed to gallery’ to see an overview of all visualization options. +> +> +> 1. What are the percentage on unclassified for JC1A and JP4D? +> 2. What are the kindgoms found for JC1A and JP4D? +> 3. Where might the eukaryotic DNA come from? +> 4. How is the diversity of Proteobacteria in JC1A and JP4D? +> +> > +> > +> > 1. 78% for JC1A and 90% for JP4D +> > 2. Kindgoms: +> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus +> > - JP4D: 10% Bacteria, 0.7% Eukaryota +> > 3. It seems to be human contamination +> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. +> +> {: .solution} +> +{: .question} -Let’s have a look at the **taxonomy bar chart**. Here, you can see the abundance of different taxa depicted in different colors in your samples. On top of the chart you can select which rank is supposed to be shown in the chart. You can also change the display options to for example switch between value und percentage. If you hover over a specific taxon, you get exact information of the taxon’s name and the taxonomy occurrence in the sample. Furthermore, there is a search bar that allows you to search for specific taxa (within the rank that you chose to depict) and gives you the option to hide specific taxons. -Next, lets go back to the gallery and choose the **bubble chart**. Here, you can find the distribution of taxa across the whole dataset at the rank that you can choose above the chart. When hovering over the bubbles, you get additional information concerning the taxon. Clicking on one bubble gives you the direct comparison of the distribution of this taxon in the different samples. To oder the bubbles according to their size you can choose the ‘list’ option shown right next to the taxonomy level. +## Visualization using Pavian -Another displaying option is the **Sankey diagram**, that is depicting the abundance of taxonomies as a flow chart. Again, you can choose the taxonomy level that you want to show in your diagram. When clicking on one bar of the diagram, this part is enlarged for better view. +__Pavian__ (pathogen visualization and more) ({% cite Breitwieser.2020 %}) is an interactive visualization tool for metagenomic data. It was developed for the clinical metagenomic problem to find a disease-causing pathogen in a patient sample, but it is useful to analyze and visualize any kind of metagenomics data. -The **donut partition** summarizes the microbial community according to non-numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘environmental medium’ to see the difference between sediment and water (It doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). When clicking on one part of the donut you will also find the distribution of the taxon across the samples. On the right hand side you can additionally choose if you’d like to have dynamic y axis or prefer standard y axis to compare different donuts with each other. +> Launch Pavian +> 1. {% tool [Pavian] (interactive_tool_pavian) %} with the following paramters: +> - {% icon param-collection %} *"Kraken and MetaPhlAn-style reports"*: **Report** collection of **Kraken** +> +{: .hands_on} -The **attributes column chart** summarizes the microbial community according to numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘geographic location’ to (again, it doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). +**Pavian** runs a Galaxy Interactive tool. You can access it when it become orange. -> ### {% icon question %} Questions ->Let's have a look at our data and try to answer some questions. ->1. How many percent of the sample reads are bacteria and how many are eukaryota? ->2. How do you filter the data from eukaryotic reads? -> +> Interact with Pavian +> +> 1. Open **Pavian** +> +> {% snippet faqs/galaxy/interactive_tools_open.md %} +> +> 2. Import data +> 1. Click on `Use data on server` +> 2. Select both samples +> 3. Click on `Read selected directories` +> 4. Check you have a table in `Available sample sets` looks like +> +> X | FormatOK | Include | Name | ReportFile | ReportFilePath +> --- | --- | --- | --- | --- | --- +> 1 | X | X | JP4AD | JP4AD | /home/shiny//JP4AD +> 2 | X | X | JC1A | JC1A | /home/shiny//JC1A +> +> 5. Click on `Save table` > +> 3. Click on `Results Overview` in the left panel > -> > ### {% icon solution %} Solution -> >1. Sample 0: 75,65 % bacteria; 24,51 % eukaryota - Sample 1: 92,70 % bacteria; 6,87 % eukaryota +{: .hands_on} + +This page shows the summary of the classifications in the selected sample set: - (go to taxonomy bar chart, choose kingdom and hover over the bars to find "taxonomy occurence in this sample") +![Screenshot of a table with 2 rows (JP4A and JC1A) and columns: Name, Number of raw reads, Classified reads, Chordate reads, Artificial reads, Unclassified reads, Microbial reads, Bacterial reads, Viral reads, Fungal reads, Protozoan reads. The cells have a barchart that shows the relation of the cell value to other cell values in the same category](.images/pavian-kraken-results-overview.png) -> >2. xxx Search function of phinch shows confusing results when clicking on “hide” -> > xxx phnich states invalid date format but when I change the date format in the metadata file from 20120706 to 2012-07-06, kraken-biom doesnt work any more -> > +> +> +> 1. Does both sample have same size? +> 1. What are the percentage of classified reads for JC1A and JP4D? +> 2. Are the percentage of bacterial reads similar? +> +> > +> > +> > 1. JP4D has much more reads than JC1A +> > 2. 10.2% for JP4D and 23.1% for JC1A +> > 3. 12.6% for JC1A and 9.44% for JP4D. So similar magnitude orders > > {: .solution} > {: .question} +Let's now inspect assignements to reads per sample. -## Visualize the taxonomical classification with Pavian - -__Pavian__ (pathogen visualization and more) ({% cite Breitwieser.2020 %}) is an interactive visualization tool for metagenomic data. It was developed for the clinical metagenomic problem to find a disease-causing pathogen in a patient sample, but it is useful to analyze and visualize any kind of metagenomics data. - -> ### {% icon hands_on %} Hands-on: Pavian +> Inspect samples with Pavian +> +> 1. Click on `Sample` in the left panel +> 2. Select `JC1A` in the `Select sample` drop-down on the top +> +> The first view gives a Sankey diagram for one sample: +> +> ![Sankey plot with taxonomy hierarchy from domain on the left to species on the right](.images/pavian-kraken-sankey-JC1A.png) +> +> > +> > +> > 1. What is a Sankey diagram? +> > 2. What are the different set of values represented as the horizontal axis? +> > +> > > +> > > +> > > 1. A sankey diagram is a visualization used to depict a flow from one set of values to another +> > > 2. The taxonomy hierarchy from domain on the left to species on the right +> > +> > {: .solution} +> > +> {: .question} +> +> 3. Click on `Proteobacteria` in the Sankey plot +> 4. Inspect the created graph on the right +> +> ![Bar plot with the number of reads across all samples for Proteobacteria. JP4A bar is much higher than JC1A one. The turqoise bar shows the number of reads that are identified at the specific taxon; the orange bar shows the number of reads identified at children of the specified taxon.](.images/pavian-kraken-proteobacteria.png) +> +> > +> > +> > 1. Are the number of reads assigned to Proteobacteria similar for both samples? +> > 2. Why? +> > +> > > +> > > +> > > 1. JP4A has many more reads assigned to Proteobacteria than JC1A +> > > 2. JP4A has many more reads initially +> > +> > {: .solution} +> > +> {: .question} +{: .hands_on} -> - Use {% tool [Pavian] (interactive_tool_pavian) %} with the following paramters: -> - *"Kraken and MetaPhlAn-style reports"*: `Kraken report file` - (or: Estimate Abundance at Taxonomic Level on collection: Kraken style report for Bracken) +We would like now to compare both samples. -Important note: don't wait for Galaxy to finish the job! Your results are available directly, as Pavian is an interactive tool. To see them, click on User in the blue part right at the top of the Galaxy page, there you can find "Active interactive tools". When you go there, you will find Pavian running and can visit the website with your results by following the link "Pavian Visualisation" which is provided as the name of the job. +> Inspect samples with Pavian +> +> 1. Click on `Comparison` in the left panel +> 2. Select `%` and unclick `Reads` in the blue area drop-down on the top +> 3. Click on `Domain` green button +> +> ![Screenshot of a table with 4 rows (Bacteria, Eukaryota, Archaea, Viruses) and / columns (Name, Rank, TID, Max, JP4A, JC1A, Lineage)](.images/pavian-kraken-sankey-JC1A.png) +> +> > +> > +> > Is there similar proportion of Bacteria in both samples? +> > +> > > +> > > +> > > JP4D has much higher proportion of Bacteria (> 93%>) than JC1A (57%), which contains quite a lot of Eukaryote +> > {: .solution} +> > +> {: .question} +> +> 4. Select `Homo sapiens` in the `Filter taxa` box below the green buttons +> +> > +> > +> > Is there similar proportion of Bacteria in both samples? +> > +> > > +> > > +> > > After human filtering, both samples have similar proportion of Bacteria +> > {: .solution} +> > +> {: .question} +> +> 3. Click on `Class` green button +> +> > +> > +> > 1. How are the diversities of classes in both samples? +> > 2. What could it biologically mean given that JC1A is a control and JP4D a sample from fertilized pond? +> > +> > > +> > > +> > > 1. JP4D seems highly dominated by a Alphaproteobacteria class. JC1A has also a majority of Alphaproteobacteria, but also significant proportions of Betaproteobacteria, Gammaproteobacteria, Flavobacteria, Actinomycetia +> > > 2. Alphaproteobacteria seems to have a survival advantage in the new environment. According to the authors this correlates with specific genomic traits that enable them to cope better with high nutrient availability. +> > {: .solution} +> > +> {: .question} +> +{: .hands_on} -{: .hands_on} +Once you are done with Pavian, you should delete it in your history so the corresponding job is killed. +> Visualize the taxonomical classification with Phinch +> +> __Phinch__ ({% cite Bik.2014 %}) is another tools to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. +> As a first step, we need to convert the Kraken output file into a kraken-biom file to make it accessible for Phinch. Fot this, we need to add a metadata file, provided here. xxx +When generating a metadata file for your own data, you can take this as an example and find the general guidelines [here](http://qiime.org/documentation/file_formats.html#mapping-file-overview) +> +> > Phinch +> > 1. Use {% tool [Kraken-biom] (toolshed.g2.bx.psu.edu/repos/iuc/kraken_biom/kraken_biom/1.2.0+galaxy1) %} to convert Kraken2 report into the correct format for phinch with the following parameters. +> > - *"Input"*: `Kraken2 on dataset collection` +> > - *"Sample Metadata file"*: `metadata.txt` +> > - *"Output format"*: `JSON` +> > +> > 2. **View** the file in Phinch +> > - Use {% tool [Phinch Visualisation] (interactive_tool_phinch) %} with the following paramters: +> > - *"Input"*: `Kraken-biom output file` +> {: .hands_on} +> +> Important note: don't wait for Galaxy to finish the job! Your results are available directly, as Phinch is an interactive tool. To see them, click on User in the blue part right at the top of the Galaxy page, there you can find "Active interactive tools". When you go there, you will find Phinch running and can visit the website with your results by following the link "Phinch Visualisation of Kraken-biom output file" which is provided as the name of the job. +> +> When you follow the link to the Phinch webpage, you first see an overview of your samples. Here, you have the possibility to further filter your data, for example by date or location, depending on which information you provided in your metadata file. +Next, you click on ‘proceed to gallery’ to see an overview of all visualization options. +> +> Let’s have a look at the **taxonomy bar chart**. Here, you can see the abundance of different taxa depicted in different colors in your samples. On top of the chart you can select which rank is supposed to be shown in the chart. You can also change the display options to for example switch between value und percentage. If you hover over a specific taxon, you get exact information of the taxon’s name and the taxonomy occurrence in the sample. Furthermore, there is a search bar that allows you to search for specific taxa (within the rank that you chose to depict) and gives you the option to hide specific taxons. +> +> Next, lets go back to the gallery and choose the **bubble chart**. Here, you can find the distribution of taxa across the whole dataset at the rank that you can choose above the chart. When hovering over the bubbles, you get additional information concerning the taxon. Clicking on one bubble gives you the direct comparison of the distribution of this taxon in the different samples. To oder the bubbles according to their size you can choose the ‘list’ option shown right next to the taxonomy level. +> +> Another displaying option is the **Sankey diagram**, that is depicting the abundance of taxonomies as a flow chart. Again, you can choose the taxonomy level that you want to show in your diagram. When clicking on one bar of the diagram, this part is enlarged for better view. +> +> The **donut partition** summarizes the microbial community according to non-numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘environmental medium’ to see the difference between sediment and water (It doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). When clicking on one part of the donut you will also find the distribution of the taxon across the samples. On the right hand side you can additionally choose if you’d like to have dynamic y axis or prefer standard y axis to compare different donuts with each other. +> +> The **attributes column chart** summarizes the microbial community according to numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘geographic location’ to (again, it doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). +> +> > +> > +> > 1. How many percent of the sample reads are bacteria and how many are eukaryota? +> > 2. How do you filter the data from eukaryotic reads? +> > +> > > +> > > 1. go to taxonomy bar chart, choose kingdom and hover over the bars to find "taxonomy occurence in this sample": +> > > - Sample 0: 75,65 % bacteria; 24,51 % eukaryota +> > > - Sample 1: 92,70 % bacteria; 6,87 % eukaryota +> > > +> > > 2. xxx Search function of phinch shows confusing results when clicking on “hide” +> > > +> > > xxx phnich states invalid date format but when I change the date format in the metadata file from 20120706 to > 2012-07-06, kraken-biom doesnt work any more +> > > +> > {: .solution} +> > +> {: .question} +{: .details} - Following the provided link, you will find the Pavian webpage. When choosing ‘Use data on server’ as data source, you should find your selected report files from galaxy. If you wish you can choose several sample files at once to be able to compare them in Pavian. After selecting ‘Read selected directories’, Pavian will show you a classification summary of all samples. -Select ‘Comparison’ in the sidebar to find a table with taxa as rows and samples as columns. Above the table you can change several displaying options like which rank you would like to see or if you want the Z-scores to be displayed. Furthermore, the data can be filtered to exclude for example human reads as a host genome. +# Choosing the right tool -Select ‘Sample’ in the sidebar to zoom into one sample with a Sankey diagram, which shows different taxons as lines with their width based on the quantity of the taxon. The diagram is shown for one sample but when hovering over nodes, the abundance of that specific taxon compared in all samples shown in a bar plot will appear on the right-hand side. -One additional feature of Pavian is the ‘Alignment Viewer’ to investigate for example if reads of a taxon cover the genome. Genome coverage can provide an indication whether an assignment is artificial or not. +When it comes to taxonomic assignment while analyzing metagenomic data, **Kraken2** is not the only tool available. Several papers do benchmarking of different tools ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}). -> ### {% icon question %} Questions ->Let's have a look at our data and try to answer some questions. ->1. What is the taxonomy occurence in percent of the Family Rhodobacteraceae in the two samples? ->2. Regarding the question the authors had, when collecting the data: What could it biologically mean, if one bacteria family increases their occurence in fertilized pond (JP4D)? -> +> Benchmarking taxonomic classification tools +> +> The benchmarking papers present different methods for comparing the available tools: +> - The **CAMI challenge** is based on results of different labs that each used the CAMI dataset to perform their analysis on and send it back to the authors. +> - {% cite Ye.2019 %} performed all the analysis themselves > +> Additionally, the datasets used for both benchmarking approaches differ: +> - **CAMI**: only ~30%-40% of reads are simulated from known taxa while the rest of the reads are from novel taxa, plasmids or simulated evolved strains. +> - {% cite Ye.2019 %} used International Metagenomics and Microbiome Standards Alliance (IMMSA) datasets, wherein the taxa are described better. > -> > ### {% icon solution %} Solution -> >1. Sample JC1A: 12,82 % - Sample JP4D: 36,06 % +> When benchmarking different classification tools, several metrics are used to compare their performance: +> 1. **Precision**: proportion of true positive species identified in the sample divided by number of total species identified by the method +> 2. **Recall**: proportion of true positive species divided by the number of distinct species actually in the sample +> 3. Precision-recall curve: each point represents the precision and recall scores at a specific abundance threshold, the **area under the precision-recall curve (AUPR)** +> 4. **L2 distance**: representation of abundance profiles → how accurately the abundance of each species or genera in the resulting classification reflects the abundance of each species in the original biological sample (“ground truth”) +> +> When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. +{: .details} -> >2. It could mean that they have a survival advantage in the new environment. According to the authors this correlates with specific genomic traits that enable them to cope better with high nutrient availability. -> > +> Profiling tools > -> {: .solution} +> Profilers, which are tools that investigate relative abundances of taxa within a dataset, fall into three groups depending on their performance: +> 1. Profilers, that correctly predict relative abundances +> 2. Precise profilers (suitable, when many false positives would increase cost and effort in downstream analysis) +> 3. Profilers with high recall (suitable for pathogen detection, when the failure of detecting an organism can have severe negative consequences) > -{: .question} +> However, some characteristics are common to all profilers: +> - Most profilers only perform well until the family level +> - Drastic decrease in performance between family and genus level, while little change between order and family level +> - poorer performance of all profilers on CAMI datasets compared to International Metagenomics and Microbiome Standards Alliance (IMMSA) +> - Fidelity of abundance estimates decreases notably when viruses and plasmids were present +> - high numbers of false positive calls at low abundance +> - Taxonomic profilers vs profiles from taxonomic binning: +> Precision and recall of the taxonomic binners were comparable to that of the profilers; +> abundance estimation at higher ranks was more problematic for the binners +{: .details} + +Tool | Version | Available in Galaxy | CAMI challenge | {% cite Ye.2019 %} +--- | --- | --- | --- | --- +mOTUs | 1.1.1 | No | most memory efficient | +mOTUs | 2.5.1 | No | Great on marine and plant-associated datasets | +mOTUs | v.cami1 | No | Great on strain-madness dataset | +MetaPhlAn | 2.9.21 | Yes | Great on plant-associated datasets | Recommended for low computational requirements (< 2 Gb of memory) +MetaPhlAn | 2.9.22 | Yes | Great on marine; strain-madness datasets | +DUDes | v.cami1 | No| Great on strain-madness dataset | +FOCUS | 1.5 | No | Fast; most memory efficient | +Bracken | 2.2 | Yes (version 2.7) | Fast | +Bracken | 2.6 | Yes (version 2.7) | Great on plant-associated dataset +Kraken | 2.0.8 beta (GSA,Sr) | Yes | Great on marine datase; fastest; most memory efficient | Good performance metrics; very fast on large numbers of samples; allow custom databases when high amounts of memory (>100 Gb) are available + + + + +# Conclusion -For further information on how to use Pavian please have a look into the [Pavian Walktrough](https://github.com/fbreitwieser/pavian/blob/master/vignettes/pavian-walkthrough.pdf) provided by F. Breitwieser. - - -# Discussion: Choosing the right tool - -When it comes to taxonomic assignment while analyzing metagenomic data, in this tutorial presented Kraken2 is not the only tool available. Several papers do benchmarking of different tools ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}) and their results are presented in the following section, with focus on tools that are available in Galaxy. -The benchmarking papers present different methods for comparing the available tools: The CAMI challenge is based on results of different labs that each used the CAMI dataset to perform their analysis on and send it back to the authors. In contrast, {% cite Ye.2019 %} performed all the analysis themselves. Additionally, the datasets used for both benchmarking approaches differ: in the CAMI dataset only ~30%-40% of reads are simulated from known taxa while the rest of the reads are from novel taxa, plasmids or simulated evolved strains. In contrast, {% cite Ye.2019 %} used International Metagenomics and Microbiome Standards Alliance (IMMSA) datasets, wherein the taxa are described better. - -When benchmarking different classification tools, several metrics are used to compare their performance: -1. **Precision**: proportion of true positive species identified in the sample divided by number of total species identified by the method -2. **Recall**: proportion of true positive species divided by the number of distinct species actually in the sample -3. Precision-recall curve: each point represents the precision and recall scores at a specific abundance threshold → **area under the precision-recall curve (AUPR)** - -4. **L2 distance**: representation of abundance profiles → how accurately the abundance of each species or genera in the resulting classification reflects the abundance of each species in the original biological sample (“ground truth”) - -When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. - -## Profiling tools - -Profilers, which are tools that investigate relative abundances of taxa within a dataset, fall into three groups depending on their performance: -1. Profilers, that correctly predict relative abundances -2. Precise profilers (suitable, when many false positives would increase cost and effort in downstream analysis) -3. Profilers with high recall (suitable for pathogen detection, when the failure of detecting an organism can have severe negative consequences) - -However, some characteristics are common to all profilers: -- Most profilers only perform well until the family level -- Drastic decrease in performance between family and genus level, while little change between order and family level -- poorer performance of all profilers on CAMI datasets compared to International Metagenomics and Microbiome Standards Alliance (IMMSA) -- Fidelity of abundance estimates decreases notably when viruses and plasmids were present -- high numbers of false positive calls at low abundance -- Taxonomic profilers vs profiles from taxonomic binning: -Precision and recall of the taxonomic binners were comparable to that of the profilers; -abundance estimation at higher ranks was more problematic for the binners - - - -| Tool | version | available in Galaxy | In CAMI challenge, best method across metrics for\* : | additional features | {% cite Ye.2019 %} benchmarking | -| --------- | -------------------- | ------------------- | ----------------------------------------------------- | ------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | -| mOTUs | 1.1.1 | no | \- | most memory efficient | | -| mOTUs | 2.5.1 | no | marine; plant-associated | \- | | -| mOTUs | v.cami1 | no | strain-madness | \- | | -| MetaPhlAn | 2.9.21 | yes | plant-associated | \- | recommended for low computational requirements (< 2 Gb of memory) | -| MetaPhlAn | 2.9.22 | yes | marine; strain-madness | \- | -| DUDes | v.cami1 | no | strain-madness | \- | | -| FOCUS 1.5 | 1.5 | no | \- | fastest; most memory efficient | | -| Bracken | 2.2 | yes (version 2.7) | \- | fastest | -| Bracken | 2.6 | yes (version 2.7) | plant-associated -| Kraken | 2.0.8 beta (GSA,Sr) | yes | marine | fastest; most memory efficient | provide good performance metrics
very fast on large numbers of samples
allow custom databases
when high amounts of memory (>100 Gb) are available | - -*metagenome benchmark datasets created by {% cite Meyer.2022 %} representing a marine, a high strain diversity environment (‘strain-madness’) and a plant-associated environment including fungal genomes and host plant material - - - + \ No newline at end of file From fe49ac281998ccd1a3d00441ac1176a3fd62d4a2 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Sun, 2 Apr 2023 21:23:18 +0200 Subject: [PATCH 28/66] Update tutorial.md --- .../taxonomic-assignment/tutorial.md | 83 ++++++++++++++----- 1 file changed, 61 insertions(+), 22 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 5e53eba59b647e..d88db2f4c373e0 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -604,39 +604,78 @@ When generating a metadata file for your own data, you can take this as an examp > > - *"Input"*: `Kraken-biom output file` > {: .hands_on} > -> Important note: don't wait for Galaxy to finish the job! Your results are available directly, as Phinch is an interactive tool. To see them, click on User in the blue part right at the top of the Galaxy page, there you can find "Active interactive tools". When you go there, you will find Phinch running and can visit the website with your results by following the link "Phinch Visualisation of Kraken-biom output file" which is provided as the name of the job. +**Phinch** runs a Galaxy Interactive tool. You can access it when it become orange. + +> Interact with Pavian > -> When you follow the link to the Phinch webpage, you first see an overview of your samples. Here, you have the possibility to further filter your data, for example by date or location, depending on which information you provided in your metadata file. -Next, you click on ‘proceed to gallery’ to see an overview of all visualization options. +> 1. Open **Phinch** > -> Let’s have a look at the **taxonomy bar chart**. Here, you can see the abundance of different taxa depicted in different colors in your samples. On top of the chart you can select which rank is supposed to be shown in the chart. You can also change the display options to for example switch between value und percentage. If you hover over a specific taxon, you get exact information of the taxon’s name and the taxonomy occurrence in the sample. Furthermore, there is a search bar that allows you to search for specific taxa (within the rank that you chose to depict) and gives you the option to hide specific taxons. +> {% snippet faqs/galaxy/interactive_tools_open.md %} + +{: .hands_on} + +> The first pages shows an overview of your samples. Here, you have the possibility to further filter your data, for example by date or location, depending on which information you provided in your metadata file. + +> > -> Next, lets go back to the gallery and choose the **bubble chart**. Here, you can find the distribution of taxa across the whole dataset at the rank that you can choose above the chart. When hovering over the bubbles, you get additional information concerning the taxon. Clicking on one bubble gives you the direct comparison of the distribution of this taxon in the different samples. To oder the bubbles according to their size you can choose the ‘list’ option shown right next to the taxonomy level. +> 1. How many sequence reads do the samples contain? > -> Another displaying option is the **Sankey diagram**, that is depicting the abundance of taxonomies as a flow chart. Again, you can choose the taxonomy level that you want to show in your diagram. When clicking on one bar of the diagram, this part is enlarged for better view. +> > +> > +> > 1. JC1A (Phinch name: 0) contains 56008 reads, while JP4D (Phinch name: 1) contains 242438 reads +> > -> The **donut partition** summarizes the microbial community according to non-numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘environmental medium’ to see the difference between sediment and water (It doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). When clicking on one part of the donut you will also find the distribution of the taxon across the samples. On the right hand side you can additionally choose if you’d like to have dynamic y axis or prefer standard y axis to compare different donuts with each other. +> {: .solution} > -> The **attributes column chart** summarizes the microbial community according to numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘geographic location’ to (again, it doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). +{: .question} + + +Next, you click on ‘proceed to gallery’ to see an overview of all visualization options. > +> Let’s have a look at the **taxonomy bar chart**. Here, you can see the abundance of different taxa depicted in different colors in your samples. On top of the chart you can select which rank is supposed to be shown in the chart. You can also change the display options to for example switch between value und percentage. +> > > +> +> 1. What information can you get from hovering over a sample? +> 2. How many percent of the sample reads are bacteria and how many are eukaryota? +> +> > > > -> > 1. How many percent of the sample reads are bacteria and how many are eukaryota? -> > 2. How do you filter the data from eukaryotic reads? -> > -> > > -> > > 1. go to taxonomy bar chart, choose kingdom and hover over the bars to find "taxonomy occurence in this sample": +> > 1. the taxon’s name and the taxonomy occurrence in the sample +> > 2. choose kingdom and hover over the bars to find "taxonomy occurence in this sample": > > > - Sample 0: 75,65 % bacteria; 24,51 % eukaryota > > > - Sample 1: 92,70 % bacteria; 6,87 % eukaryota -> > > -> > > 2. xxx Search function of phinch shows confusing results when clicking on “hide” -> > > -> > > xxx phnich states invalid date format but when I change the date format in the metadata file from 20120706 to > 2012-07-06, kraken-biom doesnt work any more -> > > -> > {: .solution} +> +> +> {: .solution} +> +{: .question} +> +Lets go back to the gallery and choose the **bubble chart**. Here, you can find the distribution of taxa across the whole dataset at the rank that you can choose above the chart. When hovering over the bubbles, you get additional information concerning the taxon. + +> > +> +> 1. Which is the most abundant Class and how many reads are found in both samples? +> +> > > > -> {: .question} -{: .details} +> > 1. To oder the bubbles according to their size you can choose the ‘list’ option shown right next to the taxonomy level. Clicking on one bubble gives you the direct comparison of the distribution of this taxon in the different samples. The most abundant Class is Alphaproteobacteria with 18.114 reads in Sample 0 and 153.230 reads in sample 1 +> +> +> {: .solution} +> +{: .question} + + +> +> Another displaying option is the **Sankey diagram**, that is depicting the abundance of taxonomies as a flow chart. Again, you can choose the taxonomy level that you want to show in your diagram. When clicking on one bar of the diagram, this part is enlarged for better view. + + +> The **donut partition** summarizes the microbial community according to non-numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘environmental medium’ to see the difference between sediment and water (It doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). When clicking on one part of the donut you will also find the distribution of the taxon across the samples. On the right hand side you can additionally choose if you’d like to have dynamic y axis or prefer standard y axis to compare different donuts with each other. +> +> The **attributes column chart** summarizes the microbial community according to numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘geographic location’ to (again, it doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). + +Once you are done with Phinch, you should delete it in your history so the corresponding job is killed. # Choosing the right tool @@ -698,4 +737,4 @@ Kraken | 2.0.8 beta (GSA,Sr) | Yes | Great on marine datase; fastest; most memor # Conclusion - \ No newline at end of file + From a2298f9dc74a83f3bcfff9e12ec807bb3294e725 Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Sun, 2 Apr 2023 21:57:08 +0200 Subject: [PATCH 29/66] Update tutorial.md --- topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index d88db2f4c373e0..94be70a12105a9 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -737,4 +737,4 @@ Kraken | 2.0.8 beta (GSA,Sr) | Yes | Great on marine datase; fastest; most memor # Conclusion - +In this tutorial, we apply Kraken2 software to assign taxonomic labels to two microbiome sample datasets. We then visualize the results using Krona, Pavian and Phinch to analyze and compare the datasets. Finally, we discuss important facts when it comes to choosing the right tool for taxonomic assignment. Additionally, we use MetaPhlAn on the same datsets and compare the results to Kraken2. From 592298838167df4f7e3c9670bbe870874d9350d4 Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Wed, 12 Apr 2023 21:21:39 +0000 Subject: [PATCH 30/66] fix title --- _plugins/gtn/images.rb | 2 +- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/_plugins/gtn/images.rb b/_plugins/gtn/images.rb index 2969beabbaecc9..ea79884e38e6c6 100644 --- a/_plugins/gtn/images.rb +++ b/_plugins/gtn/images.rb @@ -22,7 +22,7 @@ def self.get_image_dimensions(tuto_dir, url) self._get_image_dimensions(match[1].strip) elsif ! url.match(/https?:\/\//) img_path = File.absolute_path(File.join(tuto_dir, url)) - if File.exists?(img_path) + if File.exist?(img_path) self._get_image_dimensions(img_path) end else diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 6f52ac68e21132..b35a5d197c6788 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -1,7 +1,7 @@ --- layout: tutorial_hands_on -title: 'Trajectory Analysis: Monocle3 in R' +title: 'Inferring Trajectories using Monocle3 (R)' subtopic: single-cell-CS priority: 6 zenodo_link: 'https://zenodo.org/record/7455590' From f7a9c5b1f64b21449844d8891a8defd9562f6936 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Thu, 13 Apr 2023 18:04:31 +0100 Subject: [PATCH 31/66] title change --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 6f52ac68e21132..b5325c5ed113db 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -1,7 +1,7 @@ --- layout: tutorial_hands_on -title: 'Trajectory Analysis: Monocle3 in R' +title: 'Inferring Trajectories using Monocle3 (R)' subtopic: single-cell-CS priority: 6 zenodo_link: 'https://zenodo.org/record/7455590' @@ -320,7 +320,7 @@ plot_cells(cds_red_dim, color_cells_by="batch", label_cell_groups=FALSE) ![Left image showing dataset before batch correction: upper and lower right branches mostly consist of N705 and N706. Right image showing the dataset after batch correction: the cells from all the samples are evenly spread throughout the whole dataset.](../../images/scrna-casestudy-monocle/batch_correction.png "Comparison of the dataset before and after batch correction.") -Do you see this? That’s amazing! Batch correction did a great job here! Now the dataset is nicely aligned, and the cells from all the samples are evenly spread throughout the whole dataset. It is worth mentioning that removing batch effects was done using mutual nearest neighbor alignment, a technique introduced by John Marioni's lab ({% cite Haghverdi_2018 %}) and supported by Aaron Lun's package [batchelor](https://bioconductor.org/packages/release/bioc/html/batchelor.html). +Do you see this? That’s amazing! Batch correction did a great job here! Now the dataset is nicely aligned, and the cells from all the samples are evenly spread throughout the whole dataset. It is worth mentioning that removing batch effects was done using mutual nearest neighbor alignment, a technique introduced by John Marioni's lab ({% cite Haghverdi_2018 %}) and supported by Aaron Lun's package [batchelor](https://bioconductor.org/packages/release/bioc/html/batchelor.html). Now we can move to the next step and perform dimensionality reduction. From a59ed4d96d931a8ac3ab039dd83179d06fd855d2 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Thu, 13 Apr 2023 18:23:56 +0100 Subject: [PATCH 32/66] add data library faq and minor edits --- .../tutorials/scrna-case_monocle3-rstudio/preamble.md | 3 ++- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 4 ++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md index a62e5da3441ff4..9b94395fca633a 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md @@ -1,6 +1,6 @@ # Introduction -This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similarly to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch between R and Galaxy smoothly. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started! +This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch between R and Galaxy smoothly. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started! > > This tutorial is significantly based on the [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/introduction/). @@ -33,6 +33,7 @@ First, we need to retrieve the appropriate data. We will continue to work on the > {{ page.zenodo_link }}/files/AnnData_filtered.h5ad > ``` > +> {% snippet faqs/galaxy/datasets_import_from_data_library.md %} > {% snippet faqs/galaxy/datasets_import_via_link.md %} > > 3. Check that the datatype is `h5ad` diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index b5325c5ed113db..4f13ceb763d602 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -17,8 +17,8 @@ objectives: - Identify which operations are necessary to transform an AnnData object into the files needed for Monocle - Describe the Monocle3 functions in R - Execute tools and functions to switch between Galaxy and R fluently -- Recognise steps that can only be performed in R, but not with Galaxy tools -- Repeat the Monocle3 workflow and choose the right parameter values +- Recognise steps that can be performed in R, but not with current Galaxy tools +- Repeat the Monocle3 workflow and choose appropriate parameter values - Compare the outputs from Scanpy, Monocle in Galaxy and Monocle in R - Describe differential expression analysis methods From 161dac63725f96f3d75de3eecb2e44967dba51ec Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Thu, 13 Apr 2023 18:24:20 +0100 Subject: [PATCH 33/66] only available .eu --- .../tutorials/scrna-case_monocle3-rstudio/preamble.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md index 9b94395fca633a..cd07727ac82fb5 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md @@ -45,7 +45,7 @@ First, we need to retrieve the appropriate data. We will continue to work on the ## Launching JupyterLab -{% icon warning %} Please note: this is only currently available on the [usegalaxy.eu](https://usegalaxy.eu) and [usegalaxy.org](https://usegalaxy.org) sites. +{% icon warning %} Please note: this is only currently available on the [usegalaxy.eu](https://usegalaxy.eu) site. > Data uploads & JupyterLab > There are a few ways of importing and uploading data into JupyterLab. You might find yourself accidentally doing this differently than the tutorial, and that's ok. There are a few key steps where you will call files from a location - if these don't work from you, check that the file location is correct and change accordingly! From 2b9c9fa74b7c3c450f45d6a66e4f0539017b4291 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Thu, 13 Apr 2023 19:02:54 +0100 Subject: [PATCH 34/66] reduce head call --- .../scrna-case_monocle3-rstudio/preamble.md | 2 +- .../scrna-case_monocle3-rstudio/tutorial.md | 14 +++++++------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md index cd07727ac82fb5..fd1c9a8066cf2f 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md @@ -140,7 +140,7 @@ Installation will take a while, so in the meantime, when it's running, you can o >library(monocle3) >``` > > Installation errors -> > It may happen that you will encounter some problems with installation of monocle3 when using RStudio Galaxy instance or RStudio Cloud. It might be due to using older versions of R or required packages or lack of required dependencies. If it happens, you would need to carefully read the error messages and follow the suggestions. If you are facing any difficulties with installation process, it is recommended that you consult your problem with additional online resources. It is more likely that RStudio Cloud or Galaxy tool would fail rather than local RStudio. To make your analysis stress-free, you can follow the Jupyter Notebook instead, which should not give you installation issues. +> > It may happen that you will encounter some problems with installation of monocle3 when using RStudio Galaxy instance or RStudio Cloud. It might be due to using older versions of R or required packages or lack of required dependencies. If it happens, you would need to carefully read the error messages and follow the suggestions. If you are facing any difficulties with installation process, it is recommended that you consult additional online resources. It is more likely that RStudio Cloud or Galaxy tool would fail rather than local RStudio. To make your analysis stress-free, you can follow the Jupyter Notebook instead, which should not give you installation issues. > {: .warning} > {: .tip} diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 4f13ceb763d602..b5a85fcdb3465c 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -59,8 +59,8 @@ notebook: snippet: topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md --- -## Setting the environment and files upload -Once the installation is done, we should load the needed packages into our notebook. +## Setting up the environment and file upload +Once the installation is done, we should load the needed packages into our notebook. Navigate back to your `notebook`. If you are using our prepopulated notebook, you can follow the tutorial from there. Otherwise, input the following into your fresh notebook. ```r install.packages("Rcpp") # needed for reduce_dimension to avoid AnnoyAngular error; library(Rcpp) might work as well depending on the version @@ -88,7 +88,7 @@ Then we have to read in this h5ad file: ```r ann <- anndata::read_h5ad('AnnData.h5ad') ``` -Now we store all the information we need in AnnData object. However, Monocle uses *cell_data_set class* to hold expression data,which requires three input files: `expression_matrix`, `cell_metadata` and `gene_metadata`. Therefore, we have to somehow 'transfer' that information from our AnnData object to Monocle's cell_data_set (cds). AnnData stores a data matrix `X` together with annotations of observations `obs` and variables `var`, so we can extract those parameters and use them for further analysis. +Now we store all the information we need in AnnData object. However, Monocle uses *cell_data_set class* to hold expression data, which requires three input files: `expression_matrix`, `cell_metadata` and `gene_metadata`. Therefore, we have to somehow 'transfer' that information from our AnnData object to Monocle's cell_data_set (cds). AnnData stores a data matrix `X` together with annotations of observations `obs` and variables `var`, so we can extract those parameters and use them for further analysis. ```r expression_matrix <- ann$X @@ -98,7 +98,7 @@ gene_metadata <- ann$var > Uploading files from your computer is also an option! > -> If you already have files containing the expression matrix, genes and cells metadata, you can upload them to JupyLab and generate cds file from them! For example, if you first downloaded the files from Galaxy your files will have `.tabular` extension. In this case, we will use `read.delim()` function to read them in. In this function, the first argument is the file path - in our case, the files are in the same folder as the notebook, so the file path is the same as the file name. You can always check that by right-clicking on the file and choosing `Copy path`. The second argument, `row.names=1` takes the column number of the data file from which to take the row names. +> If you already have files containing the expression matrix, genes and cells metadata, you can upload them to JupyLab and generate a cds file from them instaed. For example, if you first downloaded the files from Galaxy, your files will have the `.tabular` extension. In this case, we will use the `read.delim()` function to read them in. In this function, the first argument is the file path - in our case, the files are in the same folder as the notebook, so the file path is the same as the file name. You can always check that by right-clicking on the file and choosing `Copy path`. The second argument, `row.names=1` takes the column number of the data file from which to take the row names. > ```r > # read in the files > cell_metadata <- read.delim('cells.tabular', row.names=1) @@ -112,7 +112,7 @@ gene_metadata <- ann$var > > > > > > > > -> > > This allows us to ensure that the expression value matrix has the same number of columns as the `cell_metadata` has rows and the same number of rows as the `gene_metadata` has rows. Importantly, row names of the `cell_metadata` object should match the column names of the expression matrix and row names of the `gene_metadata` object should match row names of the expression matrix. +> > > This allows us to ensure that the expression value matrix has the same number of columns as the `cell_metadata` has rows and the same number of rows as the `gene_metadata` has rows. Importantly, the row names of the `cell_metadata` object should match the column names of the expression matrix and the row names of the `gene_metadata` object should match the row names of the expression matrix. > > > > > {: .solution} > {: .question} @@ -147,9 +147,9 @@ gene_metadata <- ann$var {: .tip} -According to [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/starting/), `expression_matrix` should have genes as rows and cells as columns. Let's check if that's the case here. +According to the [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/starting/), the `expression_matrix` should have genes as rows and cells as columns. Let's check if that's the case here. ```r -expression_matrix # preview the content of the file by calling its name +head(expression_matrix,c(5, 5)) # preview the content of the file by calling its the first 5 rows by 5 columns ``` We can see that in our matrix rows are cells and genes are columns, so we have to transpose the matrix simply using function `t()`. But before doing so, we will change its type from dataframe to matrix - this is Monocle's requirement to generate cell_data_set afterwards. ```r From 4bfdb15fe9f9c32475e7010bc04353a24ed96d02 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Thu, 13 Apr 2023 19:04:18 +0100 Subject: [PATCH 35/66] shorten gene annotation preview --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index b5a85fcdb3465c..f329f68ceb0eef 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -156,9 +156,9 @@ We can see that in our matrix rows are cells and genes are columns, so we have t expression_matrix <- as.matrix(expression_matrix) # change the type to matrix expression_matrix <- t(expression_matrix) # transpose the matrix ``` -Another condition we have to satisfy if that one of the columns of the `gene_metadata` should be named "gene_short_name", which represents the gene symbol for each gene. Some functions won't work without that. Do we have such a column? Let's check. +Another condition we have to satisfy is that one of the columns of the `gene_metadata` should be named "gene_short_name", which represents the gene symbol for each gene. Some functions won't work without that. Do we have such a column? Let's check. ```r -gene_metadata # preview the content of the file to check the name of the column containing gene symbols +head(gene_metadata) # preview the top ten rows of the file to check the name of the column containing gene symbols ``` The second column indeed contains gene symbols, but is called "Symbol" instead of "gene_short_name". That can be easily changed by a simple assignment, as long as we know the number of the column that we want to modify. In our case the gene symbols are stored in column 2. We can access the column names by `colnames()`. From b0091be931c9c029366a6c142abca66a6a5bb5fe Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Fri, 14 Apr 2023 10:04:03 +0200 Subject: [PATCH 36/66] Update tutorial.md --- .../taxonomic-assignment/tutorial.md | 37 +++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 94be70a12105a9..f8a44aa64b43f9 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -677,6 +677,43 @@ Lets go back to the gallery and choose the **bubble chart**. Here, you can find Once you are done with Phinch, you should delete it in your history so the corresponding job is killed. +# MetaPhlAn +xxx introduction + +> Assign taxonomic labels with MetaPhlAn +> +> 1. {% tool [MetaPhlAn]([https://toolshed.g2.bx.psu.edu/repos/george-weingart/metaphlan]) %} with the following parameters: +> - *"Fasta/FastQ file(s) with microbiota reads"*: `Paired-end files` +> - *"Forward paired-end Fasta/FastQ file with microbiota reads"*: JP4D/JC1A_R1 +> - *"Reverse paired-end Fasta/FastQ file with microbiota reads"*: JP4D/JC1A_R2 +> +> - In *"Outputs"*: +> - *"Output for Krona?"*: `Yes` + +{: .hands_on} + +**MetaPhlAn** will create five outputs for each dataset: + + - Predicted taxon relative abundances + - Bowtie2 output + - SAM file + - BIOM file + - Predicted taxon relative abundances for Krona + + +Let's now run **Krona** + +> Generate Krona visualisation +> 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1) %} with the following parameters: +> - *"Type of input data"*: `tabular` +> - {% icon param-collection %} *"Input file"*: Krona output of **Metaphlan** +> +> 2. 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index f8a44aa64b43f9..0b83bb8e98d14f 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -684,8 +684,8 @@ xxx introduction > > 1. {% tool [MetaPhlAn]([https://toolshed.g2.bx.psu.edu/repos/george-weingart/metaphlan]) %} with the following parameters: > - *"Fasta/FastQ file(s) with microbiota reads"*: `Paired-end files` -> - *"Forward paired-end Fasta/FastQ file with microbiota reads"*: JP4D/JC1A_R1 -> - *"Reverse paired-end Fasta/FastQ file with microbiota reads"*: JP4D/JC1A_R2 +> - *"Forward paired-end Fasta/FastQ file with microbiota reads"*: JP4D_R1 +> - *"Reverse paired-end Fasta/FastQ file with microbiota reads"*: JP4D_R2 > > - In *"Outputs"*: > - *"Output for Krona?"*: `Yes` @@ -711,7 +711,21 @@ Let's now run **Krona** > 2. Inspect the generated file {: .hands_on} - + + + +> > +> +> As you can see, there is barely any assignment. What might be the reason for this? +> +> > +> > +> > Probably, the reference database is not complete enough yet to identify all taxons. +> +> +> {: .solution} +> +{: .question} From 208aebb5dc1f6e2779f6e77aff861dfedb171d9d Mon Sep 17 00:00:00 2001 From: Sophia120199 <105356774+Sophia120199@users.noreply.github.com> Date: Fri, 14 Apr 2023 10:55:17 +0200 Subject: [PATCH 41/66] Update tutorial.md --- .../taxonomic-assignment/tutorial.md | 66 ++++++++++++++++--- 1 file changed, 57 insertions(+), 9 deletions(-) diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md index 0b83bb8e98d14f..8a74e54d562f01 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -678,7 +678,21 @@ Lets go back to the gallery and choose the **bubble chart**. Here, you can find Once you are done with Phinch, you should delete it in your history so the corresponding job is killed. # MetaPhlAn -xxx introduction + +In this tutorial, we follow second approach using **MetaPhlAn** ({% cite truong2015metaphlan2 %}). MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) at species-level. MetaPhlAn 4 relies on ~5.1M unique clade-specific marker genes identified from ~1M microbial genomes (~236,600 references (bacterial, archeal, viral and eukaryotic) and 771,500 metagenomic assembled genomes) spanning 26,970 species-level genome bins. + +It allows +-unambiguous taxonomic assignments; +-accurate estimation of organismal relative abundance; +-species-level resolution for bacteria, archaea, eukaryotes and viruses; +-strain identification and tracking +-orders of magnitude speedups compared to existing methods. +-microbiota strain-level population genomics +-MetaPhlAn clade-abundance estimation + +The basic usage of MetaPhlAn consists in the identification of the clades (from phyla to species and strains in particular cases) present in the microbiota obtained from a microbiome sample and their relative abundance. + +It needs the reference database, which can be locally installed or customized using the dedicated tools. > Assign taxonomic labels with MetaPhlAn > @@ -692,13 +706,49 @@ xxx introduction {: .hands_on} -**MetaPhlAn** will create five outputs for each dataset: - - Predicted taxon relative abundances - - Bowtie2 output - - SAM file - - BIOM file - - Predicted taxon relative abundances for Krona +5 files and a collection are generated by **MetaPhlAn** {% icon tool %}: + +- The main output: A **tabular file** called `Predicted taxon relative abundances` with the **community profile* + + ``` + #2202671 reads processed +#SampleID Metaphlan_Analysis +#clade_name NCBI_tax_id relative_abundance additional_species +k__Bacteria 2 100.0 +k__Bacteria|p__Bacteroidetes 2|976 94.38814 +k__Bacteria|p__Proteobacteria 2|1224 5.61186 +k__Bacteria|p__Bacteroidetes|c__CFGB45935 2|976| 94.38814 +k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria 2|1224|28211 5.61186 +k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935 2|976|| 94.38814 +k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales 2|1224|28211|204455 5.61186 +k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935 2|976||| 94.38814 +k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae 2|1224|28211|204455|31989 5.61186 +k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609 2|976|||| 94.38814 +k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes 2|1224|28211|204455|31989|2873978 5.61186 +k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609|s__GGB56609_SGB78025 2|976||||| 94.38814 +k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes|s__Phycocomes_zhengii 2|1224|28211|204455|31989|2873978|2056810 5.61186 +k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609|s__GGB56609_SGB78025|t__SGB78025 2|976|||||| 94.38814 +k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes|s__Phycocomes_zhengii|t__SGB31485 2|1224|28211|204455|31989|2873978|2056810| 5.61186 + ``` + + Each line contains 4 columns: + 1. the lineage with different taxonomic levels + 2. the previous lineage with NCBI taxon id + 3. the relative abundance found for our sample for the lineage + 4. any additional species + + The file starts with high level taxa (kingdom: `k__`) and go to more precise taxa. + + +- A **collection** with the same information as in the tabular file but splitted into different files, one per taxonomic level +- A **tabular file** called `Predicted taxon relative abundances for Krona` with the same information as the previous file but formatted for visualization using Krona. We will use this file later +- A **BIOM file** with the same information as the previous file but in BIOM format + + BIOM format is quite common in microbiomics. This is standard, for example, as the input for tools like mothur or QIIME. + +- A **SAM file** with the results of the sequence mapping on the reference database. +- A **tabular file** called `Bowtie2 output` with similar information as the one in the **SAM file** Let's now run **Krona** @@ -784,8 +834,6 @@ Bracken | 2.6 | Yes (version 2.7) | Great on plant-associated dataset Kraken | 2.0.8 beta (GSA,Sr) | Yes | Great on marine datase; fastest; most memory efficient | Good performance metrics; very fast on large numbers of samples; allow custom databases when high amounts of memory (>100 Gb) are available - - # Conclusion In this tutorial, we apply Kraken2 software to assign taxonomic labels to two microbiome sample datasets. We then visualize the results using Krona, Pavian and Phinch to analyze and compare the datasets. Finally, we discuss important facts when it comes to choosing the right tool for taxonomic assignment. Additionally, we use MetaPhlAn on the same datsets and compare the results to Kraken2. From 298c5ac8c726b518369b90c8c3ccc5b69ae93f06 Mon Sep 17 00:00:00 2001 From: Helena Date: Tue, 18 Apr 2023 16:25:58 +0200 Subject: [PATCH 42/66] Add SECURITY.md Since github is maybe again changing the layout of the repo welcome and highlighting security policy, it can make sense to make this visible. --- SECURITY.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 SECURITY.md diff --git a/SECURITY.md b/SECURITY.md new file mode 100644 index 00000000000000..649ec88e232fd1 --- /dev/null +++ b/SECURITY.md @@ -0,0 +1,12 @@ +# Security Policy + +## Supported Versions + +The GTN does not necessarily have a notion of 'supported versions' as our site is completely static and does not go through any sort of managed release cycle, unlike Galaxy. +All dynamic features implemented in our site are strictly client-side, which generally provides acceptable security guarantees and low risk of user-facing exploitation. + +There are occasionally vulnerabilities in the tools we use to build the site, however those vulnerabilities generally do not affect us, nor the output produced by jekyll and related tools. + +## Reporting a Vulnerability + +Please report any vulnerabilities using [GitHub private reporting](https://github.com/galaxyproject/training-material/security/advisories/new). One of the maintainers will acknowledge your report within 5 business days (European). From 3dadfa7e49f1a8d5bdfcf4087dfac32a6a08fdc1 Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Wed, 26 Apr 2023 15:17:11 +0000 Subject: [PATCH 43/66] objectives - JupYLab R doesnt switch galaxy-R, omit --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 1 - 1 file changed, 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index f329f68ceb0eef..90daecd1692d0e 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -16,7 +16,6 @@ questions: objectives: - Identify which operations are necessary to transform an AnnData object into the files needed for Monocle - Describe the Monocle3 functions in R -- Execute tools and functions to switch between Galaxy and R fluently - Recognise steps that can be performed in R, but not with current Galaxy tools - Repeat the Monocle3 workflow and choose appropriate parameter values - Compare the outputs from Scanpy, Monocle in Galaxy and Monocle in R From fd321e144c45bcb986be063f9afb3d5ef99eefba Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Wed, 26 Apr 2023 15:41:53 +0000 Subject: [PATCH 44/66] change preamble and faq for jupylab --- .../interactive_tools_jupyter_launch.md | 2 +- .../scrna-case_monocle3-rstudio/preamble.md | 31 +++++++------------ 2 files changed, 13 insertions(+), 20 deletions(-) diff --git a/faqs/galaxy/interactive_tools_jupyter_launch.md b/faqs/galaxy/interactive_tools_jupyter_launch.md index 312425ea384b16..ec3d1bab59b99d 100644 --- a/faqs/galaxy/interactive_tools_jupyter_launch.md +++ b/faqs/galaxy/interactive_tools_jupyter_launch.md @@ -11,7 +11,7 @@ contributors: [annefou,shiltemann,nomadscientist] > > > Run JupyterLab > > -> > 1. {% tool [Interactive Jupyter Notebook](interactive_tool_jupyter_notebook) %}: +> > 1. {% tool [Interactive Jupyter Notebook](interactive_tool_jupyter_notebook) %}. Note that on some instances this is called {% tool [Interactive JupyTool and notebook](interactive_tool_jupyter_notebook) %}: > > 2. Click Run Tool > > 3. The tool will start running and will stay running permanently > > diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md index fd1c9a8066cf2f..b2ce2f61bb7da2 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md @@ -1,34 +1,25 @@ # Introduction -This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch between R and Galaxy smoothly. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started! +This tutorial is the next one in the [Single-cell RNA-seq: Case Study]({% link topics/single-cell/index.md %}) series. This tutorial focuses on trajectory analysis using [monocle3](https://cole-trapnell-lab.github.io/monocle3/), similar to the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}). However, in this tutorial we will use the R programming language that hides behind the user-friendly Galaxy tools. Sometimes you might encounter limitations when working with Galaxy tools, or you might want to make a wee modification that has to be done manually. It is therefore useful to be able to switch to R. If you do not feel confident using R, [this tutorial]({% link topics/data-science/tutorials/r-basics/tutorial.md %}) is a good place to start. However, our tutorial is quite straightforward to follow and at the end you will feel like a programmer! On the other hand, if you are not confident with the biological or statistical theory behind trajectory analysis, check out the [slide deck]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/slides.html %}). With those resources (including the previous case study tutorials) you are well-equipped to go through this tutorial with ease. Let’s get started! > > This tutorial is significantly based on the [Monocle3 documentation](https://cole-trapnell-lab.github.io/monocle3/docs/introduction/). {: .comment} -## Get data -In the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}), we showed that Monocle3 works great with annotated data, but what if your data is not annotated yet? Is it still possible to use Monocle? The answer is yes, Monocle can annotate cells according to their type, which you will perform in this tutorial. +## Optional: Get data into a Galaxy history +In the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}), we showed that Monocle3 works great with annotated data, but what if your data is not annotated yet? Is it still possible to use Monocle? The answer is yes, Monocle can annotate cells according to their type. -First, we need to retrieve the appropriate data. We will continue to work on the case study data from a mouse model of fetal growth restriction {% cite Bacon2018 %} (see [the study in Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6945/results/tsne) and [the project submission](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6945/)). We will use the filtered AnnData object, before normalisation and annotation, generated in the [filtering tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) as step `20: Filtered Object`. However for this tutorial, you don't have to download any files on your computer or even import files to Galaxy! We will show you the whole analysis in R, starting from AnnData object. If you wish, you can get this file to your Galaxy history, following the step below, but you can also jump directly into JupyLab. +First, we need to retrieve the appropriate data. We will continue to work on the case study data from a mouse model of fetal growth restriction {% cite Bacon2018 %} (see [the study in Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6945/results/tsne) and [the project submission](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6945/)). We will use the filtered AnnData object, before normalisation and annotation, generated in the [filtering tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) as step `20: Filtered Object`. However for this tutorial, you don't have to download any files on your computer or even import files to Galaxy! We will show you the whole analysis in R, starting from AnnData object. However, if you'd like to examine the datasets in a history, the instructions are below. -> Option 1: Data upload - Import history -> -> 1. Import history from: [input history](https://usegalaxy.eu/u/wendi.bacon.training/h/cs4trajectories--monocle3--rstudio---input) +> Optional: Data upload into Galaxy history > +> You have three options for importing the input data into a Galaxy history. +> +> 1. You can import a history from: [input history](https://usegalaxy.eu/u/wendi.bacon.training/h/cs4trajectories--monocle3--rstudio---input); Import the files from [Zenodo]({{ page.zenodo_link }}); or Import the files from the shared data library (`GTN - Material` -> `{{ page.topic_name }}` +> -> `{{ page.title }}`): > > {% snippet faqs/galaxy/histories_import.md %} > -> 2. **Rename** {% icon galaxy-pencil %} the the history to your name of choice. -> -{: .hands_on} - -> Option 2: Data upload - Add to history -> -> 1. Create a new history for this tutorial -> 2. Import the files from [Zenodo]({{ page.zenodo_link }}) or from -> the shared data library (`GTN - Material` -> `{{ page.topic_name }}` -> -> `{{ page.title }}`): -> > ``` > {{ page.zenodo_link }}/files/AnnData_filtered.h5ad > ``` @@ -36,10 +27,12 @@ First, we need to retrieve the appropriate data. We will continue to work on the > {% snippet faqs/galaxy/datasets_import_from_data_library.md %} > {% snippet faqs/galaxy/datasets_import_via_link.md %} > -> 3. Check that the datatype is `h5ad` +> 2. Check that the datatype is `h5ad` > > {% snippet faqs/galaxy/datasets_change_datatype.md datatype="h5ad" %} > +> 3. **Rename** {% icon galaxy-pencil %} the history to your name of choice. +> {: .hands_on} From 7497860ec31b0937575c2e7cd9ec5ea057116af4 Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Wed, 26 Apr 2023 20:49:33 +0000 Subject: [PATCH 45/66] separate install lines --- .../tutorials/scrna-case_monocle3-rstudio/preamble.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md index b2ce2f61bb7da2..e9f8a43cc5380a 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md @@ -11,7 +11,7 @@ In the [Monocle3 in Galaxy tutorial]({% link topics/single-cell/tutorials/scrna- First, we need to retrieve the appropriate data. We will continue to work on the case study data from a mouse model of fetal growth restriction {% cite Bacon2018 %} (see [the study in Single Cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-6945/results/tsne) and [the project submission](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6945/)). We will use the filtered AnnData object, before normalisation and annotation, generated in the [filtering tutorial]({% link topics/single-cell/tutorials/scrna-case_basic-pipeline/tutorial.md %}) as step `20: Filtered Object`. However for this tutorial, you don't have to download any files on your computer or even import files to Galaxy! We will show you the whole analysis in R, starting from AnnData object. However, if you'd like to examine the datasets in a history, the instructions are below. -> Optional: Data upload into Galaxy history +> Optional data upload into Galaxy history > > You have three options for importing the input data into a Galaxy history. > @@ -100,11 +100,17 @@ If you followed the {% icon tip %} tip above, you should already have your Jupyt >Installing the packages > > 1. Navigate back to the `Terminal` -> 2. In the Terminal tab open, write the following, preferably one line at a time: +> 2. In the Terminal tab open, write the following, one line at a time: > ``` >conda install -c conda-forge -c bioconda r-monocle3 +>``` +>``` >conda install -c conda-forge -c bioconda anndata +>``` +>``` >conda install -c conda-forge r-viridislite +>``` +>``` >conda install -c conda-forge bioconductor-biomart >``` > 3. If you are asked at any point `Proceed ([y]/n)?`, type `y` - surely we want to proceed! From 9d4ff0d4f0feea78a2072889b059e29fc8bb3e02 Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Wed, 26 Apr 2023 21:20:29 +0000 Subject: [PATCH 46/66] missing library --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 90daecd1692d0e..9bdee9766e933b 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -69,6 +69,7 @@ install.packages("Rcpp") # needed for reduce_dimension to avoid AnnoyAngular library(monocle3) library(biomaRt) library(magrittr) # needed for %>% +library(viridisLite) ``` ```r @@ -167,7 +168,7 @@ colnames(gene_metadata) # see the changes ``` ## Generating CDS object -Now let’s store our files in one object – the cell_data_set. This is the main class used by Monocle to hold single cell expression data. The class is derived from the Bioconductor SingleCellExperiment class. It's similar to Python's AnnData storing a data matrix together with annotations of observations and variables. There are three ways of creating CDS object in monocle: +Now let’s store our files in one object – the `cell_data_set`. This is the main class used by Monocle to hold single cell expression data. The class is derived from the Bioconductor SingleCellExperiment class. Similar to Python's AnnData, the cell_data_set stores a data matrix together with annotations of observations and variables. There are three ways of creating CDS object in monocle: - Using ```new_cell_data_set() ``` function with three data frames as arguments (not their paths!): expression matrix (can also be a sparseMatrix), cell metadata and gene metadata - Using ```load_cellranger_data() ``` function providing the path to the folder containing 10X Genomics Cell Ranger output files. This function takes an argument `umi_cutoff` that determines how many reads a cell must have to be included - Using ```load_mm_data() ``` function providing the paths to matrix file and two metadata files (features and cell information). @@ -181,7 +182,7 @@ We are now ready to process our data! > Format conversion > -> Since Monocle’s CDS object is analogous to Python's AnnData, why don’t we use some kind of conversion between those two formats? There is indeed a package called `sceasy` that helps easy conversion of different single-cell data formats to each other. However, when we tested this conversion on our dataset and then used Monocle to plot the expression of genes, the plots were not correct – the expression was shown to be ideantical throughout the sample. For comparison, Seurat did well when plotting gene expression of the same converted object! Although conversion functions are very handy, you have to be aware that their output might be interpreted differently by certain packages. Therefore, to make sure that the analysis is reliable, we decided to generate CDS object directly using Monocle’s function. +> Since Monocle’s CDS object is analogous to Python's AnnData, why don’t we use some kind of conversion between those two formats? There is indeed a package called `sceasy` that helps easy conversion of different single-cell data formats to each other. However, when we tested this conversion on our dataset and then used Monocle to plot the expression of genes, the plots were not correct – the expression was shown to be identical throughout the sample. For comparison, Seurat did well when plotting gene expression of the same converted object! Although conversion functions are very handy, you have to be aware that their output might be interpreted differently by certain packages. Therefore, to make sure that the analysis is reliable, we decided to generate CDS object directly using Monocle’s function. > ![Comparison between plots of gene expression generated by Monocle and Seurat using the CDS object that was converted from AnnData using SCEasy tool. Gene expression in Monocle plots is identical throughout the sample, while the expression of the same genes plotted by Seurat is noticeable only in specific clusters.](../../images/scrna-casestudy-monocle/monocle_seurat.png "Comparison between plots of gene expression generated by Monocle and Seurat using the CDS object that was converted from AnnData using SCEasy tool.") > {: .details} From b9c9a47c9544ef1c2a3791c70da72819f23e0e02 Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Thu, 27 Apr 2023 10:06:51 +0000 Subject: [PATCH 47/66] audomated yes conda --- .../tutorials/scrna-case_monocle3-rstudio/preamble.md | 8 ++++---- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md index e9f8a43cc5380a..065dd55abae62d 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/preamble.md @@ -102,16 +102,16 @@ If you followed the {% icon tip %} tip above, you should already have your Jupyt > 1. Navigate back to the `Terminal` > 2. In the Terminal tab open, write the following, one line at a time: > ``` ->conda install -c conda-forge -c bioconda r-monocle3 +>conda install -y -c conda-forge -c bioconda r-monocle3 >``` >``` ->conda install -c conda-forge -c bioconda anndata +>conda install -y -c conda-forge -c bioconda anndata >``` >``` ->conda install -c conda-forge r-viridislite +>conda install -y -c conda-forge r-viridislite >``` >``` ->conda install -c conda-forge bioconductor-biomart +>conda install -y -c conda-forge bioconductor-biomart >``` > 3. If you are asked at any point `Proceed ([y]/n)?`, type `y` - surely we want to proceed! > diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 9bdee9766e933b..f936b7553e47d4 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -193,7 +193,7 @@ We are now ready to process our data! > {: .warning} -If you remember the very first tutorial, we were starting with gene IDs and adding gene symbols based on the Ensembl GTF file. +If you remember the very first tutorial in the case study, we started with gene IDs and added gene symbols based on the Ensembl GTF file. But what if we didn’t have the genes symbols in our CDS object and wanted to add them now? Of course - it's possible! We will also base this annotation on Ensembl - the genome database – with the use of the library BioMart. We will use the same archive as in the Alevin tutorial (Genome assembly GRCm38) to get the gene names. Please note that the updated version (GRCm39) is available, but some of the gene IDs are not in that EnsEMBL database. The code below is written in a way that it will work for the updated dataset too, but will produce ‘NA’ where the corresponding gene name couldn’t be found. ```r cds_extra <- cds # assign our CDS to a new object for the demonstration purpose From e90ae00434fdd54319d7d48830ea10139b61d46d Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Thu, 27 Apr 2023 10:40:58 +0000 Subject: [PATCH 48/66] preprocessing tidy --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index f936b7553e47d4..55f5111ef0ae43 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -267,7 +267,9 @@ Do you remember the Monocle workflow introduced in the previous tutorial? Here i ![Monocle workflow: scRNA-seq dataset, pre-process data (normalise, remove batch effects), non-linear dimensionality reduction (t-SNE, UMAP), cluster cells, compare clusters (identify top markers, targeted contrasts), trajectory analysis](../../images/scrna-casestudy-monocle/monocle3_new_workflow.png "Workflow provided by Monocle3 documentation") ## Pre-processing -Let’s start with normalisation and pre-processing that can be performed using the function `preprocess_cds()`. The argument `num_dim` is the number of principal components that will be computed when using PCA during normalisation. Then you can check that you're using enough PCs to capture most of the variation in gene expression across all the cells in the data set. Note that “PCA” is the default method of pre-processing in Monocle3, so although we can specify this in our function, we don’t have to. +Let’s start with normalisation and pre-processing that can be performed using the function `preprocess_cds()`. The argument `num_dim` is the number of principal components that will be used. You can check that you're using enough PCs to capture most of the variation in gene expression across all the cells in the data set. Note that “PCA” is the default method of pre-processing in Monocle3, so although we can specify this in our function, we don’t have to. + +Note that this step can take awhile - around 5 or so minutes due to the high number of PCs calculated. Feel free to use a `num_dim` value around 100 to accelerate preprocessing and compare the results. ```r # PCA pre-processing with 210 principal components @@ -280,7 +282,7 @@ plot_pc_variance_explained(cds_preprocessing) ![Plot of variation in gene expression vs PCA components, decreasing exponentially.](../../images/scrna-casestudy-monocle/pca_plot.jpg " Plot of variation in gene expression vs PCA components.") -The plot shows that actually using more than ~100 PCs captures only a small amount of additional variation. However, if we look at how the cells are plotted on 2D graph when using different values of PCs, it is easier to imagine how the `num_dim` actually affects the output. Therefore, for this demonstration we will use the value of 210, which, compared to the results from the previous tutorial, makes the most sense for our dataset. However, it will take quite some time to run this, and this is why generally smaller PCs are preferable, so feel free to use `num_dim` value around 100 to accelerate preprocessing and compare the results. +The plot shows that actually using more than ~100 PCs captures only a small amount of additional variation. However, if we look at how the cells are plotted on 2D graph when using different values of PCs, it is easier to visualise how the `num_dim` actually affects the output. We will use the value of 210, which, compared to the results from the previous tutorial, makes the most sense for our dataset. ![Six plots showing only the shaded shape of how the cells are clustered depending on the num_dim argument. The general trend is maintained though.](../../images/scrna-casestudy-monocle/num_dim.jpg "The "shape" of the plot showing how the cells are clustered depending on the 'num_dim' argument.") From bccc455ea7cef207b6ecd16ece60462ba66f16ec Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Thu, 27 Apr 2023 13:00:12 +0000 Subject: [PATCH 49/66] batch correction text edits --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 55f5111ef0ae43..fc38b5576689b1 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -287,14 +287,14 @@ The plot shows that actually using more than ~100 PCs captures only a small amou ![Six plots showing only the shaded shape of how the cells are clustered depending on the num_dim argument. The general trend is maintained though.](../../images/scrna-casestudy-monocle/num_dim.jpg "The "shape" of the plot showing how the cells are clustered depending on the 'num_dim' argument.") ## Batch correction and Dimensionality reduction -Our dataset actually comprises data from 7 samples, so there is a risk that the batch effects can be observed. Those are systematic differences in the transcriptome of cells measured in different experimental batches. However, we can use Monocle to deal with that! -First, let’s check how our dataset looks like in terms of batch effects. We can do that by colouring the cells by batch. This information is stored in our CDS object from `cell_metadata` file. Before asking Monocle to plot anything, let’s check the exact column name of the batch information column. +Our dataset actually comprises data from 7 samples, so there is a risk that batch effects will impact analysis. Batch effects are systematic differences in the transcriptome of cells measured in different experimental batches. However, we can use Monocle to deal with that! +First, let’s check how our dataset looks in terms of batch effects. We can do that by colouring the cells by batch. This information is stored in our CDS object from the `cell_metadata` file. Before asking Monocle to plot anything, let’s check the exact column name of the batch information column. ```r colnames(colData(cds_preprocessing)) # check column names ``` In our case it’s indeed ‘batch’, but your data might have another name (eg. "plate", etc.), so make sure you put the correct argument value. -Then, we have to reduce dimension before attempting to plot. The [previous tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}) introduced the methods of dimensionality reduction in Monocle. Of course you can replicate what we did in Galaxy to compare the output of dimensionality reduction using different methods, simply by changing the `reduction_method` argument. Options currently supported by Monocle are "UMAP", "tSNE", "PCA", "LSI", and "Aligned". However, as for now, let’s just recall that UMAP gave the best results, so we will use UMAP here as well. +In order to plot our cells on a 2D graph, we need to reduce the numbers of dimensions. The [previous tutorial]({% link topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md %}) introduced the methods of dimensionality reduction in Monocle. You can replicate what we did in Galaxy to compare the output of dimensionality reduction using different methods, simply by changing the `reduction_method` argument. Options currently supported by Monocle are "UMAP", "tSNE", "PCA", "LSI", and "Aligned". However, as for now, let’s just recall that UMAP gave the best results, so we will use UMAP here as well. ```r # reduce dimension first cds_preprocessing_UMAP <- reduce_dimension(cds_preprocessing, reduction_method = "UMAP", preprocess_method = "PCA") @@ -310,7 +310,7 @@ We can see that upper and lower right branches mostly consist of N705 and N706, cds_batch <- align_cds(cds_preprocessing_UMAP, preprocess_method = "PCA", alignment_group = "batch") ``` -To see the changes, we have to run UMAP again, but this time on the aligned dataset, so we will specify that `preprocess_method ` is now "Aligned" and not “PCA”, however after having used `align_cds()` Monocle would use "Aligned" argument automatically if no `preprocess_method` was specified. +To see the changes, we have to run UMAP again, but this time on the aligned dataset. We will specify that `preprocess_method ` as "Aligned" and not “PCA". Monocle would use the "Aligned" argument automatically if no `preprocess_method` was specified. ```r # dimensionality reduction after alignment cds_red_dim <- reduce_dimension(cds_batch, preprocess_method = "Aligned", reduction_method = "UMAP") @@ -322,7 +322,8 @@ plot_cells(cds_red_dim, color_cells_by="batch", label_cell_groups=FALSE) ![Left image showing dataset before batch correction: upper and lower right branches mostly consist of N705 and N706. Right image showing the dataset after batch correction: the cells from all the samples are evenly spread throughout the whole dataset.](../../images/scrna-casestudy-monocle/batch_correction.png "Comparison of the dataset before and after batch correction.") -Do you see this? That’s amazing! Batch correction did a great job here! Now the dataset is nicely aligned, and the cells from all the samples are evenly spread throughout the whole dataset. It is worth mentioning that removing batch effects was done using mutual nearest neighbor alignment, a technique introduced by John Marioni's lab ({% cite Haghverdi_2018 %}) and supported by Aaron Lun's package [batchelor](https://bioconductor.org/packages/release/bioc/html/batchelor.html). +Do you see this? It’s amazing! Batch correction did a great job here! Now the dataset is nicely aligned, and the cells from all the samples are evenly spread throughout the whole dataset. It is worth mentioning that removing batch effects was done using mutual nearest neighbor alignment, a technique introduced by John Marioni's lab ({% cite Haghverdi_2018 %}) and supported by Aaron Lun's package [batchelor](https://bioconductor.org/packages/release/bioc/html/batchelor.html). Also, due to the machine learning elements of the code of this technique - as well as the fact that packages are updated regularly - your plots may not look identical to the ones pictured here. Nevertheless, the interpretation should be the same - the batch corrected plot should show better batch distribution than the uncorrected one. + Now we can move to the next step and perform dimensionality reduction. From 3563dfa81d9a09d8957f3e413e980fb65730f19d Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Thu, 27 Apr 2023 13:26:04 +0000 Subject: [PATCH 50/66] Allow plot variation in partition section --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index fc38b5576689b1..cb565455fd3914 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -376,13 +376,13 @@ plot_cells(cds_clustered, reduction_method = "UMAP", color_cells_by = 'cluster', ## Clustering: partitions -OK, what about partitions? They were also created during the clustering step and it’s important to check them before learning the trajectory because it is performed only within one partition, so it is essential that all the cells that we want to analyse in pseudotime belong to the same partition. +OK, what about partitions? They were also created during the clustering step and it’s important to check them before calculating the trajectory because it is performed only within one partition. It is essential that all the cells that we want to analyse in pseudotime belong to the same partition. ```r # see the partitions plot_cells(cds_clustered, reduction_method = "UMAP", color_cells_by = 'partition', label_cell_groups=FALSE) ``` -We can see that there are 3 partitions identified in `cds_clustered` object. Ideally, we would like to combine partitions 1 and 2 to draw a trajectory through all those cells (we can ignore cells in partition 3). Sometimes using the default values might result in multiple partitions while you only need one. Then you would have to change the q-value cutoff in `partition_qval`. The default is 0.05 and by increasing this value you can increase the span of partitions, meaning that you would get fewer partitions. When trying different values of q-value, you also have to check if the clusters didn't change. It's all about finding a balance between the value of `resolution` and `partition_qval` so that both clusters and partitions are satisfactory enough for downstream analysis. Let's try that on our dataset. +While your plot might be slightly different due to package updates, we can see that there are 3 partitions identified in `cds_clustered` object. Ideally, we would like to combine partitions 1 and 2 to draw a trajectory through all those cells (we can ignore cells in partition 3). Sometimes using the default values might result in multiple partitions while you only need one. Then you would have to change the q-value cutoff in `partition_qval`. The default is 0.05 and by increasing this value you can increase the span of partitions, meaning that you would get fewer partitions. When trying different values of q-value, you also have to check if the clusters didn't change. It's all about finding a balance between the value of `resolution` and `partition_qval` so that both clusters and partitions are satisfactory enough for downstream analysis. Let's try that on our dataset. ```r # changing the partition q-value cds_clustered <- cluster_cells(cds_red_dim, reduction_method = "UMAP", resolution = 0.0002, partition_qval = 1) @@ -395,7 +395,7 @@ plot_cells(cds_clustered, reduction_method = "UMAP", color_cells_by = 'partition # check if clusters didn't change plot_cells(cds_clustered, reduction_method = "UMAP", color_cells_by = 'cluster', label_cell_groups=FALSE) ``` -Voila - it worked as expected! Now we have cells from partition 1 and 2 in one partition, we still have 7 clusters, so we can learn the trajectory. However, in some cases even this method might not be enough. Then, there is a last resort… assigning cells to a partition manually. +Voila - it worked as expected! Now we have cells from partition 1 and 2 in one partition, we still have reasonable clusters, so now we can learn the trajectory. However, in some cases even this method might not be enough. Then, there is a last resort… assigning cells to a partition manually. ## Additional step: assigning cells to one partition > Additional step From 355c7ede056c24e234172501eeea22adf1ce8783 Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Thu, 27 Apr 2023 13:41:49 +0000 Subject: [PATCH 51/66] text in gene marker section --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index cb565455fd3914..e22ad77e04c694 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -446,7 +446,7 @@ plot_cells(cds_partitions_extra, reduction_method = "UMAP", color_cells_by = 'pa There are two main approaches to assigning cell types to clusters that we’ve just identified – supervised and unsupervised, both based on gene expression in each cluster. ## Supervised approach -Supervised approach relies on the fact that when having a reference, we know which cell types to expect and we can simply check the expression of marker genes specific to the expected cell types. Let’s then check the markers mentioned in the original paper {% cite Bacon2018 %}. +The supervised approach relies on prior knowledge of which cell types to expect. We can simply check the expression of marker genes specific to the expected cell types. Let’s then check the markers mentioned in the original paper {% cite Bacon2018 %}. | Marker | Cell type | |--------------------| @@ -471,6 +471,7 @@ plot_cells(cds_clustered, genes=c('Il2ra','Cd8b1','Cd8a','Cd4','Itm2a','Aif1','H > > > > > +> > Keep in mind that these results refer to our numbered clusters, while yours might be slightly different. > > - `Il2ra` (DN): mostly expressed in cluster 4 > > - `Cd8b1, Cd8a` (DP middle): expressed in clusters 1, 6, and highly in cluster 2 > > - `Cd4` (DP late): average expression in clusters 1, 6, 2 and high expression in cluster 5 @@ -483,7 +484,7 @@ If you remember, this gene was found to be expressed in the previous Scanpy tuto > {: .solution} {: .question} -Having identified which cluster corresponds to a specific cell type, we can finally run some code to add the annotation to our CDS object. First, we will create a new column called `cell_type` in `colData()` - this is where the information about the cells is stored (eg. batch, genotype, sex, etc) - and initialize it with the values of clusters. Then, we will get the `dplyr` package which will be used for clusters annotation. +Having identified which cluster corresponds to a specific cell type, we can finally run some code to add the annotation to our CDS object. First, we will create a new column called `cell_type` in `colData()` - this is where the information about the cells is stored (eg. batch, genotype, sex, etc) - and initialize it with the values of clusters. Then, we will get the `dplyr` package which will be used for cluster annotation. ```r # just to keep the objects tidy and not overwrite them so that you can come back to any point of the analysis @@ -512,7 +513,7 @@ plot_cells(cds_annotated, color_cells_by="cell_type", label_cell_groups=FALSE) ![A plot showing the identified clusters, now each coloured by the assigned cell type.](../../images/scrna-casestudy-monocle/annotated.png "Our annotated dataset.") ## Unsupervised approach -But what if we don’t have any reference that we can use to assign our clusters? In that case, we will turn to the mentioned unsupervised approach - we will check what are the specifically expressed genes for each cluster. Then we can identify the cell types by looking up what cell types the found genes are markers for. That’s a more tedious process, but sometimes can lead to exciting and unexpected results. +But what if we don’t have any reference that we can use to assign our clusters? In that case, we will turn to the mentioned unsupervised approach - we will check what are the specifically expressed genes for each cluster. Then we can identify the cell types by looking up what cell types contain those genes. That’s a more tedious process, but sometimes can lead to exciting and unexpected results. We will use Monocle’s function `top_markers()` and store the information about specifically expressed genes for each cluster in the data frame `marker_test`. ```r # find top marker genes in each cluster From f3212b9fedc49869f9666e5a8cdf78ebb5dc242a Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Thu, 27 Apr 2023 15:17:10 +0000 Subject: [PATCH 52/66] minor text --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index e22ad77e04c694..298a27e51e4a32 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -21,7 +21,7 @@ objectives: - Compare the outputs from Scanpy, Monocle in Galaxy and Monocle in R - Describe differential expression analysis methods -time_estimation: 2H +time_estimation: 3H key_points: - Being able to switch between Galaxy and R when using Monocle is useful, particularly when you need to modify the CDS object manually. @@ -648,7 +648,8 @@ plot_cells(cds_trajectory, We have to tell Monocle where to start ordering the cells, ie. when we expect the analysed biological process to begin. Thanks to our biological knowledge, we know that the beginning of the trajectory should be at DN cluster. There are a couple of ways to specify the root cells: -1. Use `root_pr_nodes` argument in `order_cells()` function. +## Option 1: Root nodes +Here, you will use `root_pr_nodes` argument in `order_cells()` function. To find the names of the principal points, you have to plot the learned trajectory again, specifying `label_principal_points = TRUE` ```r @@ -694,7 +695,8 @@ DN_node_id # check the node found cds_order_1_helper <- order_cells(cds_trajectory, root_pr_nodes = DN_node_id) ``` -2. Use `root_cells` argument in `order_cells()` function. +## Option 2: Root cells +Here, you will use `root_cells` argument in `order_cells()` function. Specify a vector of starting cell IDs. You can provide only one cell as well as all cells of a given type. @@ -724,6 +726,8 @@ cds_order_2 <- order_cells(cds_trajectory, root_cells = DN_cells) > {: .tip} +# Plotting in pseudotime + You can use any `cds_order` object for the downstream analysis. Let’s pick one and assign it to an object with a shorter and more general name. ```r cds_order <- cds_order_1_helper From 6ed7cb8fc4b6ba1e98cb77ef8ea13e18479a8aca Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Thu, 27 Apr 2023 15:45:40 +0000 Subject: [PATCH 53/66] update title --- .../tutorials/scrna-case_monocle3-trajectories/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md index 82d53c7e978978..5585fbdd9dada2 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-trajectories/tutorial.md @@ -1,7 +1,7 @@ --- layout: tutorial_hands_on -title: 'Trajectory Analysis using Monocle3' +title: 'Inferring trajectories using Monocle3' subtopic: single-cell-CS priority: 5 zenodo_link: 'https://zenodo.org/record/7078524' From 2d4d5c730e52523f5c9f651b48858ec1eff4a34a Mon Sep 17 00:00:00 2001 From: Wendi Bacon Date: Thu, 27 Apr 2023 15:53:01 +0000 Subject: [PATCH 54/66] remove switching to and from galaxy --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 1 - 1 file changed, 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 298a27e51e4a32..35a93ec59f6aa3 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -24,7 +24,6 @@ objectives: time_estimation: 3H key_points: -- Being able to switch between Galaxy and R when using Monocle is useful, particularly when you need to modify the CDS object manually. - Monocle3 in R gives more flexibility when it comes to differential expression analysis and plotting, but Galaxy offers great reproducibility and ease of analysis. - Comparing the output of several different methods applied on the same dataset might be useful to confirm the results, to ensure that the findings are reliable and even sometimes to find a new piece of information. From 10fb6d55f171710a05d787c7fdc974c20c3a7c52 Mon Sep 17 00:00:00 2001 From: wee-snufkin <44121095+wee-snufkin@users.noreply.github.com> Date: Mon, 1 May 2023 10:36:44 +0100 Subject: [PATCH 55/66] changes after testing Added: - saving the plots section - exporting to galaxy history - solution how to fix broken dotplot - some minor changes to increase robustness --- .../scrna-case_monocle3-rstudio/tutorial.md | 141 ++++++++++++++++-- 1 file changed, 130 insertions(+), 11 deletions(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 35a93ec59f6aa3..2b50fd13b5eb14 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -196,7 +196,7 @@ If you remember the very first tutorial in the case study, we started with gene But what if we didn’t have the genes symbols in our CDS object and wanted to add them now? Of course - it's possible! We will also base this annotation on Ensembl - the genome database – with the use of the library BioMart. We will use the same archive as in the Alevin tutorial (Genome assembly GRCm38) to get the gene names. Please note that the updated version (GRCm39) is available, but some of the gene IDs are not in that EnsEMBL database. The code below is written in a way that it will work for the updated dataset too, but will produce ‘NA’ where the corresponding gene name couldn’t be found. ```r cds_extra <- cds # assign our CDS to a new object for the demonstration purpose -rownames(fData(cds_extra)) # preview of the gene IDs as rownames +head(rownames(fData(cds_extra))) # preview of the gene IDs as rownames ``` ```r # get relevant gene names @@ -222,7 +222,7 @@ genes <- getBM(attributes=c('ensembl_gene_id','external_gene_name'), ``` ```r # see the resulting data -genes +head(genes) ``` ```r # replace IDs for gene names @@ -321,6 +321,18 @@ plot_cells(cds_red_dim, color_cells_by="batch", label_cell_groups=FALSE) ![Left image showing dataset before batch correction: upper and lower right branches mostly consist of N705 and N706. Right image showing the dataset after batch correction: the cells from all the samples are evenly spread throughout the whole dataset.](../../images/scrna-casestudy-monocle/batch_correction.png "Comparison of the dataset before and after batch correction.") +> +> +> Does your plot look the same as the one in the Figure? +> +> > +> > +> > Your plot might be slightly different to the one shown in the Figure but this is fine, as long as you see analogical patterns. Some libraries that you're using might have been updated, giving non-identical output. However, the principle behind the analysis is still the same, so you can peacefully follow the tutorial. Just keep your eyes open and... THINK! +> > +> {: .solution} +> +{: .question} + Do you see this? It’s amazing! Batch correction did a great job here! Now the dataset is nicely aligned, and the cells from all the samples are evenly spread throughout the whole dataset. It is worth mentioning that removing batch effects was done using mutual nearest neighbor alignment, a technique introduced by John Marioni's lab ({% cite Haghverdi_2018 %}) and supported by Aaron Lun's package [batchelor](https://bioconductor.org/packages/release/bioc/html/batchelor.html). Also, due to the machine learning elements of the code of this technique - as well as the fact that packages are updated regularly - your plots may not look identical to the ones pictured here. Nevertheless, the interpretation should be the same - the batch corrected plot should show better batch distribution than the uncorrected one. Now we can move to the next step and perform dimensionality reduction. @@ -381,7 +393,7 @@ OK, what about partitions? They were also created during the clustering step and plot_cells(cds_clustered, reduction_method = "UMAP", color_cells_by = 'partition', label_cell_groups=FALSE) ``` -While your plot might be slightly different due to package updates, we can see that there are 3 partitions identified in `cds_clustered` object. Ideally, we would like to combine partitions 1 and 2 to draw a trajectory through all those cells (we can ignore cells in partition 3). Sometimes using the default values might result in multiple partitions while you only need one. Then you would have to change the q-value cutoff in `partition_qval`. The default is 0.05 and by increasing this value you can increase the span of partitions, meaning that you would get fewer partitions. When trying different values of q-value, you also have to check if the clusters didn't change. It's all about finding a balance between the value of `resolution` and `partition_qval` so that both clusters and partitions are satisfactory enough for downstream analysis. Let's try that on our dataset. +While your plot might be slightly different due to package updates, we can see that there are 3 partitions identified in `cds_clustered` object. Ideally, we would like to combine partitions 1 and 2 to draw a trajectory through all those cells (we can ignore cells in outlier partition). Sometimes using the default values might result in multiple partitions while you only need one. Then you would have to change the q-value cutoff in `partition_qval`. The default is 0.05 and by increasing this value you can increase the span of partitions, meaning that you would get fewer partitions. When trying different values of q-value, you also have to check if the clusters didn't change. It's all about finding a balance between the value of `resolution` and `partition_qval` so that both clusters and partitions are satisfactory enough for downstream analysis. Let's try that on our dataset. ```r # changing the partition q-value cds_clustered <- cluster_cells(cds_red_dim, reduction_method = "UMAP", resolution = 0.0002, partition_qval = 1) @@ -394,7 +406,20 @@ plot_cells(cds_clustered, reduction_method = "UMAP", color_cells_by = 'partition # check if clusters didn't change plot_cells(cds_clustered, reduction_method = "UMAP", color_cells_by = 'cluster', label_cell_groups=FALSE) ``` -Voila - it worked as expected! Now we have cells from partition 1 and 2 in one partition, we still have reasonable clusters, so now we can learn the trajectory. However, in some cases even this method might not be enough. Then, there is a last resort… assigning cells to a partition manually. + +> +> +> Have the clusters change after changing the partition q-value? +> +> > +> > +> > It might be the case that after changing partition q-value, you will notice that additional clusters appeared. In that situation, you might either play around the `resolution` and `partition_qval` values, go forward with the current clustering (adjusting the parameters accordingly), or check the other method of assigning cells to one partition given below. +> > +> {: .solution} +> +{: .question} + +Now we have cells of interest in one partition, we still have reasonable clusters, so now we can learn the trajectory. However, in some cases even this method might not be enough. Then, there is a last resort… assigning cells to a partition manually. ## Additional step: assigning cells to one partition > Additional step @@ -503,6 +528,7 @@ colData(cds_annotated)$cell_type <- dplyr::recode(colData(cds_annotated)$cell_ty '5'='DP-L', # double positive – late middle T-cell '6'='DP-M3', # double positive – middle T-cell (3rd cluster) '7'='Unknown') # no info for now, so call it ‘Unknown’ + '8'='Unknown') # no info for now, so call it ‘Unknown’ ``` ```r # check the annotation @@ -542,14 +568,14 @@ You can group the cells by any categorical variable in `colData(cds_clustered)`. > {: .question} -We can now use data in `marker_test` to rank the cells based on one of the specificity metrics and take the top gene(s) for each cluster. We will filter the expressing cells that constitute more than 10% of the cell group and we will take 2 genes in each cluster with the highest `pseudo_R2` value (you can of course modify this value and choose more genes to be selected). +We can now use data in `marker_test` to rank the cells based on one of the specificity metrics and take the top gene(s) for each cluster. We will filter the expressing cells that constitute more than 10% of the cell group and we will take one gene in each cluster with the highest `pseudo_R2` value (you can of course modify this value and choose more genes to be selected). ```r # filter the ‘marker_test’ data frame top_specific_markers <- marker_test %>% - dplyr::filter(fraction_expressing >= 0.10) %>% - dplyr::group_by(cell_group) %>% - dplyr::top_n(2, pseudo_R2) + dplyr::filter(fraction_expressing >= 0.10) %>% # set the fraction of expressing cells + dplyr::group_by(cell_group) %>% # set a group to which the cells belong + dplyr::top_n(1, pseudo_R2) # set the number of top genes and the variable from 'marker_test' to rank by ``` ```r # store the names of the marker genes @@ -566,12 +592,25 @@ plot_genes_by_group(cds_clustered, # our CDS object ordering_type="maximal_on_diag") # how to order the genes / groups on the dot plot ``` -![A dot plot showing the expression of genes and fraction of cells that express found markers in each group. On the diagonal there are two genes corresponding to each cluster with the highest pseudo_R2 score in their group.](../../images/scrna-casestudy-monocle/gene_group.png "A plot of the expression and fraction of cells that express found markers in each group.") +> Unexpectedly too many genes on y-axis? +> +> If you notice that on your dot plot any cluster has more genes than you specified (here we set one gene per cluster `top_n(1, pseudo_R2)`), go back to the code and create a new cell, right after assigning `top_specific_markers` object and just before `top_marker_names`. In the new cell, paste and execute the following: +> +> ```r +> top_specific_markers <- top_specific_markers %>% +> dplyr::distinct(pseudo_R2, .keep_all=TRUE) # select only one row if there are multiple rows with the same value in 'pseudo_R2' column +> ``` +> Then you can execute `top_marker_names` again and see how the plot looks like now. Better, right? +> This problem may arise when several genes in one cluster have the same values of specific variable from 'marker_test' (in our case we chose `pseudo_R2`). It might likely happen in small and quite unsignificant clusters. +{: .tip} + + +![A dot plot showing the expression of genes and fraction of cells that express found markers in each group. On the diagonal there are two genes corresponding to each cluster with the highest pseudo_R2 score in their group.](../../images/scrna-casestudy-monocle/gene_group.png "A plot of the expression and fraction of cells that express found markers in each group. Here, the two top genes from each cluster were plotted on diagonal beased on the highest pseudo_R2 score. Shown is the result of changing the value from 1 to 2 in "top_n(1, pseudo_R2)" in the code above.") Look at this – we have identified some more marker genes specific to each cluster! However, sometimes it happens that the found genes are not as specific as one would expect, and they appear across the whole sample. Therefore, it is a good idea to plot all those marker genes and check how they appear in the bigger picture. ```r -# plot all the identified genes to check their expression +# plot the identified genes to check their expression plot_cells(cds_clustered, genes=c('Pcdhgc4','Pcdhga5','Gm5559','Gm10359','Ccr9','Cd8b1','Plac8', 'Il2ra', 'Cd52', 'Tmsb10', 'Mki67', 'Hmgb2', 'Pclaf', 'Npm1'), reduction_method = "UMAP") ``` @@ -859,6 +898,19 @@ pheatmap::pheatmap(agg_mat, cluster_rows=TRUE, cluster_cols=TRUE, ![A heatmap showing modules of co-regulated genes across the clusters. Modules listed vertically while clusters horizontally. Some modules are highly specific to certain partitions of cells, while others are shared across multiple partitions.](../../images/scrna-casestudy-monocle/heatmap.png "Heatmap showing modules of co-regulated genes across the clusters.") You can also visualise the modules using `plot_cells()` function. We've chosen some modules to see how the differences on a heatmap correlate with the expression shown on our starting plot. + +> +> +> Which modules to plot? +> +> > +> > +> > This is totally up to you! It might be the case that you got different numbering of modules, so then using the numbers specified in the code below won't make much sense. Just look at your heatmap, compare the differences between modules and think which ones would be the most interesing to visualise. +> > +> {: .solution} +> +{: .question} + ```r # see the chosen modules across the whole sample plot_cells(cds_order, @@ -867,7 +919,7 @@ plot_cells(cds_order, color_cells_by="cluster", show_trajectory_graph=FALSE) ``` -![Plots showing expression of the genes belonging to specified modules across whole sample. Module 19 highly expressed in cluster 2 but also low expression thtoughout the sample, module 38 low expression but throughout the sample, module 41 highly expressed in cluster 5 only, module 42 almost no expression, except cluster 6.](../../images/scrna-casestudy-monocle/modules_plot.png "Plots of expression of the genes belonging to specified modules across whole sample.") +![Plots showing expression of the genes belonging to specified modules across whole sample.](../../images/scrna-casestudy-monocle/modules_plot.png "Plots of expression of the genes belonging to specified modules across whole sample.") With the visualisation methods above, you can now come back to the generated data frame `gene_module_df`, filter genes that belong to the module of interest and check their functions to get some more evidence for the correct biological interpretation. @@ -885,6 +937,73 @@ cds_3d <- reduce_dimension(cds_order, preprocess_method = 'Aligned', max_compone plot_cells_3d(cds_3d, color_cells_by="cell_type") ``` +# Export your data, figures, and notebook + +Don’t forget to save and export your data! First, we will get Jupyter to see those as files. + +## Export plots +If you want to export your plot, you have to make sure that you assigned it to an object. For example, if you want to save the plot of cells in pseudotime, simply assign the function you used to generate this plot to an object. Here we call this object `plot_pseudotime`, like so: + +```r +plot_pseudotime <- plot_cells(cds_order, + color_cells_by = "pseudotime", + label_cell_groups=FALSE, + label_leaves=FALSE, + label_branch_points=FALSE) +``` + +Then, if you want to save the plot as PDF: + +```r +pdf("plot_pseudotime.pdf") # open the graphical device and specify the directory and the name of the output pdf file +plot_pseudotime # specify the object that your plot is assigned to +dev.off() # close the graphical device +``` + +The procedure is very similar if you want to export the file as PNG (or analogically JPEG – just replace png with jpeg): +```r +png("plot_pseudotime.png", # open the graphical device and specify the directory and the name of the output png file +width=600, height=400) # optionally you can specify the width and height of the final plot +plot_pseudotime # specify the object that your plot is assigned to +dev.off() # close the graphical device +``` + +However, it often happens that the quality of the exported PNG and JPEG files is not perfect. For best results, we recommend exporting to SVG: +```r +svg("plot_pseudotime.svg") # open the graphical device and specify the directory and the name of the output svg file +plot_pseudotime # specify the object that your plot is assigned to +dev.off() # close the graphical device +``` + +You can do the same with any plot that you want to save! You will find the saved figures in the left panel of your JupyterLab. You can right-click on them and download directly onto your computer. You can also push them into your Galaxy history. To do so, you have to change Kernel to Python3 (either click on `Kernel` -> `Change Kernel...` in the upper left corner of your JupyterLab or click on the displayed current kernel in the upper right corner and change it). +![Figure showing the JupyterLab interface with an arrow pointing to the left corner, showing the option `Kernel` -> `Change Kernel...` and another arrow pointing to the right corner, showing the icon of the current kernel. The pop-up window asks which kernel should be chosen instead.](../../images/scrna-casestudy-monocle/switch_kernel.jpg "Two ways of switching kernel.") + +Check in the upper right corner that selected kernel is Python3, and run the following: +```python +put("marker_file.txt") +put("plot_pseudotime.pdf") +put("plot_pseudotime.png") +``` + +In this way you can push all the files you've saved into your Galaxy history. You can also do the same with this notebook. The cell below will only work if you haven’t changed the name of the notebook. If you renamed it, simply type its new name in the parenthesis. +```python +put("single-cell-scrna-case_monocle3-rstudio.ipynb") +``` + +Now you can go check your Galaxy history to make sure your files have all made it back into your Galaxy history. + + +# After Jupyter + +Congratulations! You've made it through Jupyter! + +> Closing JupyterLab +> 1. Click **User**: **Active Interactive Tools** +> 2. Tick the box of your Jupyter Interactive Tool, and click **Stop** +{: .hands_on} + +If you want to run this notebook again, or share it with others, it now exists in your history. 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Clean tutorial, add worklow + test, add zenodo --- CONTRIBUTORS.yaml | 4 + topics/metagenomics/faqs/kraken.md | 6 +- .../nanopore-16S-metagenomics/tutorial.md | 8 +- .../taxonomic-assignment/tutorial.md | 839 ------------------ .../Taxonomic_Assignment_workflow.ga | 568 ------------ .../images/CAMI_software_ranking.png | Bin .../images/krona-kraken.png | Bin .../images/krona-metaphlan.png | Bin .../images/krona_bacteria.png | Bin .../pavian-kraken-comparison-domain.png | Bin .../images/pavian-kraken-proteobacteria.png | Bin .../images/pavian-kraken-results-overview.png | Bin .../images/pavian-kraken-sankey-JC1A.png | Bin .../images/pavian-kraken-sankey-JP4D.png | Bin .../krona-kraken.html | 0 .../tutorial.bib | 13 +- .../tutorials/taxonomic-profiling/tutorial.md | 768 ++++++++++++++++ .../workflows/index.md | 0 .../workflows/main-workflow-test.yml | 66 ++ .../workflows/main-workflow.ga | 499 +++++++++++ 20 files changed, 1351 insertions(+), 1420 deletions(-) delete mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md delete mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/workflows/Taxonomic_Assignment_workflow.ga rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/CAMI_software_ranking.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/krona-kraken.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/krona-metaphlan.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/krona_bacteria.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/pavian-kraken-comparison-domain.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/pavian-kraken-proteobacteria.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/pavian-kraken-results-overview.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/pavian-kraken-sankey-JC1A.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/images/pavian-kraken-sankey-JP4D.png (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/krona-kraken.html (100%) rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/tutorial.bib (98%) create mode 100644 topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md rename topics/metagenomics/tutorials/{taxonomic-assignment => taxonomic-profiling}/workflows/index.md (100%) create mode 100644 topics/metagenomics/tutorials/taxonomic-profiling/workflows/main-workflow-test.yml create mode 100644 topics/metagenomics/tutorials/taxonomic-profiling/workflows/main-workflow.ga diff --git a/CONTRIBUTORS.yaml b/CONTRIBUTORS.yaml index 714d6c362fab70..272745e6d2c1b6 100644 --- a/CONTRIBUTORS.yaml +++ b/CONTRIBUTORS.yaml @@ -1296,6 +1296,10 @@ Sofokli5: orcid: 0000-0002-4833-4726 joined: 2022-05 +sophia120199: + name: Sophia Hampe + joined: 2023-04 + stephanierobin: name: Stéphanie Robin email: stephanie.robin@inrae.fr diff --git a/topics/metagenomics/faqs/kraken.md b/topics/metagenomics/faqs/kraken.md index 1c4426a00626e6..043a1d09395f19 100644 --- a/topics/metagenomics/faqs/kraken.md +++ b/topics/metagenomics/faqs/kraken.md @@ -10,4 +10,8 @@ In the $$k$$-mer approach for taxonomy classification, we use a database contain **Kraken** examines the $$k$$-mers within the query sequence, searches for them in the database, looks for where these are placed within the taxonomy tree inside the database, makes the classification with the most probable position, then maps $$k$$-mers to the lowest common ancestor (LCA) of all genomes known to contain the given $$k$$-mer. -![Kraken2](./images/kmers-kraken.jpg "Kraken sequence classification algorithm. To classify a sequence, each k-mer in the sequence is mapped to the lowest common ancestor (LCA, i.e. the lowest node) of the genomes that contain that k-mer in the database. The taxa associated with the sequence's k-mers, as well as the taxa's ancestors, form a pruned subtree of the general taxonomy tree, which is used for classification. In the classification tree, each node has a weight equal to the number of k-mers in the sequence associated with the node's taxon. Each root-to-leaf (RTL) path in the classification tree is scored by adding all weights in the path, and the maximal RTL path in the classification tree is the classification path (nodes highlighted in yellow). The leaf of this classification path (the orange, leftmost leaf in the classification tree) is the classification used for the query sequence. Source: {% cite Wood2014 %}") +![Kraken2]({{site.baseurl}}/topics/metagenomics/faqs/images/kmers-kraken.jpg "Kraken sequence classification algorithm. To classify a sequence, each k-mer in the sequence is mapped to the lowest common ancestor (LCA, i.e. the lowest node) of the genomes that contain that k-mer in the database. The taxa associated with the sequence's k-mers, as well as the taxa's ancestors, form a pruned subtree of the general taxonomy tree, which is used for classification. In the classification tree, each node has a weight equal to the number of k-mers in the sequence associated with the node's taxon. Each root-to-leaf (RTL) path in the classification tree is scored by adding all weights in the path, and the maximal RTL path in the classification tree is the classification path (nodes highlighted in yellow). The leaf of this classification path (the orange, leftmost leaf in the classification tree) is the classification used for the query sequence. Source: {% cite Wood2014 %}") + +__Kraken2__ uses a compact hash table, a probabilistic data structure that allows for faster queries and lower memory requirements. It applies a spaced seed mask of _s_ spaces to the minimizer and calculates a compact hash code, which is then used as a search query in its compact hash table; the lowest common ancestor (LCA) taxon associated with the compact hash code is then assigned to the k-mer. + +You can find more information about the __Kraken2__ algorithm in the paper [_Improved metagenomic analysis with Kraken 2_](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1891-0). \ No newline at end of file diff --git a/topics/metagenomics/tutorials/nanopore-16S-metagenomics/tutorial.md b/topics/metagenomics/tutorials/nanopore-16S-metagenomics/tutorial.md index ceebbdf7b77586..33f5b5f3c2995d 100644 --- a/topics/metagenomics/tutorials/nanopore-16S-metagenomics/tutorial.md +++ b/topics/metagenomics/tutorials/nanopore-16S-metagenomics/tutorial.md @@ -264,13 +264,7 @@ One of the key steps in metagenomic data analysis is to identify the taxon to wh To perform the taxonomic classification we will use __Kraken2__ ({% cite Wood2019 %}). This tool uses the minimizer method to sample the k-mers (all the read's subsequences of length _k_) in a deterministic fashion in order to reduce memory constumption and processing time. In addition, it masks low-complexity sequences from reference sequences by using __dustmasker__. - -> -> __Kraken2__ uses a compact hash table, a probabilistic data structure that allows for faster queries and lower memory requirements. It applies a spaced seed mask of _s_ spaces to the minimizer and calculates a compact hash code, which is then used as a search query in its compact hash table; the lowest common ancestor (LCA) taxon associated with the compact hash code is then assigned to the k-mer. -> You can find more information about the __Kraken2__ algorithm in the paper [_Improved metagenomic analysis with Kraken 2_](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1891-0). -{: .comment} - -![Taxonomic classification](../../images/metagenomics-nanopore/kmers-kraken.jpg "Kraken2 sequence classification algorithm. To classify a sequence, each l-mer is mapped to the lowest common ancestor (LCA) of the genomes that contain that l-mer in a database. In the classification tree, each node has a weight equal to the number of l-mers in the sequence associated with the node’s taxon. Image originally published in {% cite Wood2014 %}.") +{% snippet topics/metagenomics/faqs/kraken.md %} For this tutorial, we will use the __SILVA database__ ({% cite Quast2012 %}). It includes over 3.2 million 16S rRNA sequences from the _Bacteria_, _Archaea_ and _Eukaryota_ domains. diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md deleted file mode 100644 index 8a74e54d562f01..00000000000000 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ /dev/null @@ -1,839 +0,0 @@ ---- -layout: tutorial_hands_on -title: Taxonomic Assignment of Metagenomic Data -zenodo_link: still needs to be created -questions: -- Which species (or genera, families, ...) are present in my sample? - -objectives: -- Explain what taxonomic assignment is -- Explain how taxonomic assignment works -- Apply Kraken to assign taxonomic labels -- Apply Krona to visualize results of assignment and understand the output -- Identify taxonomic classification tool that fits best depending on their data -level: Introductory -key_points: -- To do -time_estimation: 1H -contributions: - authorship: - - Sophia120199 - - bebatut ---- - -# Introduction - -The term **"microbiome"** describes "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity" ({% cite whipps1988fungi %}). - -Microbiome data can be gathered from different environments such as soil, water or the human gut. The biological interest lies in general in the question how the microbiome present at a specific site influences this environment. To study a microbiome, we need to use indirect methods like metagenomics or metatranscriptomics. - -**Metagenomic samples** contain DNA from different organisms at a specific site, where the sample was collected. Metagenomic data can be used to find out which organisms coexist in that niche and which genes are present in the different organisms. -Metatranscriptomic samples include the transcribed gene products, thus RNA, that therefore allow to not only study the presence of genes but additionally their expression in the given environment. The following tutorial will focus on metagenomics data, but the principle is the same for metatranscriptomics data. - -The investigation of microorganisms present at a specific site and their relative abundance is also called **"microbial community profiling"**. -Basic for this is to find out which microorganisms are present in the sample. This can be achieved for all known microbes, where the DNA sequence specific for a certain species is known. - -For that we try to **identify the taxon** to which each individual reads belong. - -{% snippet topics/metagenomics/faqs/taxon.md %} - -When we talk about metagenomic data here, what we start with is sequences derived from DNA fragments that could be isolated from the sample of interest. Ideally, from all microbes present in the sample, we would also find DNA. The underlying idea of taxonomic assignment is to compare the DNA sequences found in the sample (reads) to DNA sequences of a database. When a read matches a database DNA sequence of a known microbe, we can derive a list with microbes present in the sample. - -When talking about taxonomic assignment or taxonomic classification, most of the time we actually talk about two methods, that in practice are often used interchangeably: -- **taxonomic binning**: the clustering of individual sequence reads based on similarities criteria and assignation of clusters to reference taxa -- **taxonomic profiling**: classification of individual reads to reference taxa to extract the relative abundances of the different taxa - -## Taxonomic classification - -Tools for taxonomic classification can be divided into three groups. Nevertheless, all of them require a pre-computed database based on previously sequenced microbial DNA or protein sequences. -1. **DNA-to-DNA**: comparison of sequencing reads with genomic databases of DNA sequences, with tools like Kraken ({% cite Wood2014 %}) -2. **DNA-to-Protein** : compare sequencing reads with protein databases (more computationally intensive because of analysis of all six frames of potential DNA-to amino acid translation, with tools like DIAMOND -3. **Marker based**: search for marker genes (e.g. 16S rRNA sequence) in reads, which is quick, but introduces bias, with tools like MetaPhlAn ({% cite blanco2023extending %}) - -The comparison of reads to database sequences can be done in different ways, leading to three different types of taxonomic assignment: - -- **Genome based** approach - - Reads are aligned to reference genomes. Considering the coverage and breadth, genomes are used to measure genome abundance. Furthermore, genes can be analyzed in genomic context. Advantages of this method are the high detection accuracy, that the unclassified percentage is known, that all SNVs can be detected and that high-resolution genomic comparisons are possible. This method takes medium compute cost. - -- **Gene based** approach - - Reads are aligned to reference genes. Next, marker genes are used to estimate species abundance. Furthermore, genes can be analyzed in isolation for presence or absence in a specific condition. The major advantage is the detection of the pangenome (entire set of genes within a species). Major disadvantages are the high compute cost, low detection accuracy and that the unclassified percentage is unknown. At least intragenic SNVs can be detected and low-resolution genomic comparison is possible. - -- **k-mer based** approach - - Databases as well as the samples DNA are broken into strings of length $$k$$ for comparison. From all the genomes in the database, where a specific k-mer is found, a lowest common ancestor (LCA) tree is derived and the abundance of k-mers within the tree is counted. This is the basis for a root-to-leaf path calculation, where the path with the highest score is used for classification of the sample. By counting the abundance of k-mers, also an estimation of relative abundance of taxa is possible. The major advantage of k-mer based analysis is the low compute cost. Major disadvantages are the low detection accuracy, that the unclassified percentage is unknown and that there is no gene detection, no SNVs detection and no genomic comparison possible. An example for a k-mer based analysis tool is Kraken, which will be used in this tutorial - -After this theoretical introduction, let's now get hands on analyzing an actual dataset! - -# Background on data - -The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas {% cite Okie.2020 %}. The researchers were interested in genomic traits that affect the rates and costs of biochemical information processing within cells. They performed a whole-ecosystem experiment, thus fertilizing the pond to achieve nutrient enriched conditions. - -Here we will use 2 datasets: -- `JP4D`: a microbiome sample collected from the Lagunita Fertilized Pond -- `JC1A`: a **control** samples from a control mesocosm. - -The datasets differ in size, but according to the authors this doesn't matter for their analysis of genomic traits. Also, they underline that differences between the two samples reflect trait-mediated ecological dynamics instead of microevolutionary changes as the duration of the experiment was only 32 days. This means that depending on available nutrients, specific lineages within the pond grow more successfully than others because of their genomic traits. - -The datafiles are named according to the first four characters of the filenames. -It is a collection of paired-end data with R1 being the forward reads and R2 being the reverse reads. Additionally, the reads have been trimmed using [__cutadapt__ ](https://training.galaxyproject.org/training-material/topics/sequence-analysis/tutorials/quality-control/tutorial.html#trim-and-filter---short-reads) - -> -> -> In this tutorial, we will cover: -> -> 1. TOC -> {:toc} -> -{: .agenda} - -# Prepare Galaxy and data - -Any analysis should get its own Galaxy history. So let's start by creating a new one: - -> Data upload -> -> 1. Create a new history for this analysis -> -> {% snippet faqs/galaxy/histories_create_new.md %} -> -> 2. Rename the history -> -> {% snippet faqs/galaxy/histories_rename.md %} -> -{: .hands_on} - -We need now to import the data - -> Import datasets -> -> 1. Import the following samples via link from [Zenodo]({{ page.zenodo_link }}) or Galaxy shared data libraries: -> -> ```text -> {{ page.zenodo_link }}/files/JC1A_R1.fastq.gz -> {{ page.zenodo_link }}/files/JC1A_R2.fastq.gz -> {{ page.zenodo_link }}/files/JP4D_R1.fastq.gz -> {{ page.zenodo_link }}/files/JP4D_R2.fastq.gz -> ``` -> -> {% snippet faqs/galaxy/datasets_import_via_link.md %} -> {% snippet faqs/galaxy/datasets_import_from_data_library.md %} -> -> 2. 3. Create a paired collection. -> -> {% snippet faqs/galaxy/collections_build_list_paired.md %} -> -{: .hands_on} - -# k-mer based taxonomic assignment of metagenomic data -> -> -> 1. What are the percentage on unclassified for JC1A and JP4D? -> 2. What are the kindgoms found for JC1A and JP4D? -> 3. Where might the eukaryotic DNA come from? -> 4. How is the diversity of Proteobacteria in JC1A and JP4D? -> -> > -> > -> > 1. 78% for JC1A and 90% for JP4D -> > 2. Kindgoms: -> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus -> > - JP4D: 10% Bacteria, 0.7% Eukaryota -> > 3. It seems to be human contamination -> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. -> -> {: .solution} -> -{: .question} -Our input data is the DNA reads of microbes present at Cuatro Ciénegas. - -To find out which microorganisms are present, we will compare the reads of the sample to a reference database, i.e. sequences of known microorganisms stored in a database, using **Kraken2** ({% cite wood2019improved %}). - -{% snippet topics/metagenomics/faqs/kraken.md %} - -For this tutorial, we will use the Standard (archaea, bacteria, viral, plasmid, human, UniVec_Core) plus protozoa & fungi database, which means: - -- archaea: RefSeq complete archaeal genomes/proteins -- bacteria: RefSeq complete bacterial genomes/proteins -- plasmid: RefSeq plasmid nucleotide/protein sequences -- viral: RefSeq complete viral genomes/proteins -- human: GRCh38 human genome/proteins -- fungi: RefSeq complete fungal genomes/proteins -- plant: RefSeq complete plant genomes/proteins -- protozoa: RefSeq complete protozoan genomes/proteins -- UniVec_Core: A subset of UniVec, NCBI-supplied database of vector, adapter, linker, and primer sequences that may be contaminating sequencing projects and/or assemblies, chosen to minimize false positive hits to the vector database - -> Assign taxonomic labels with Kraken2 -> -> 1. {% tool [Kraken2](toolshed.g2.bx.psu.edu/repos/iuc/kraken2/kraken2/2.1.1+galaxy1) %} with the following parameters: -> - *"Single or paired reads"*: `Paired Collection` -> - {% icon param-collection %} *"Collection of paired reads"*: Input paired collection -> - *"Confidence"*: `0.1` -> -> A confidence score of 0.1 means that at least 10% of the k-mers should match entries in the database. This value can be reduced if a less restrictive taxonomic assignation is desired. -> -> - In *"Create Report"*: -> - *"Print a report with aggregrate counts/clade to file"*: `Yes` -> - *"Select a Kraken2 database"*: most recent `Prebuilt Refseq indexes: PlusPF` -> -{: .hands_on} - -**Kraken2** will create two outputs for each dataset - -- **Classification**: tabular files with one line for each sequence classified by Kraken and 5 columns: - - 1. `C`/`U`: a one letter indicating if the sequence classified or unclassified - 2. Sequence ID as in the input file - 3. NCBI taxonomy ID assigned to the sequence, or 0 if unclassified - 4. Length of sequence in bp (read1|read2 for paired reads) - 5. A space-delimited list indicating the lowest common ancestor (LCA) mapping of each k-mer in the sequence - - For example, `562:13 561:4 A:31 0:1 562:3` would indicate that: - 1. The first 13 k-mers mapped to taxonomy ID #562 - 2. The next 4 k-mers mapped to taxonomy ID #561 - 3. The next 31 k-mers contained an ambiguous nucleotide - 4. The next k-mer was not in the database - 5. The last 3 k-mers mapped to taxonomy ID #562 - - `|:|` indicates end of first read, start of second read for paired reads - - For JC1A: - - ``` - Column 1 Column 2 Column 3 Column 4 Column 5 - U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:13417:1998 0 151|190 A:18 0:14 2055:5 0:1 2220095:5 0:74 |:| 0:3 A:54 2:1 0:32 204455:1 2823043:5 0:60 - U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:15782:2187 0 169|173 0:101 37329:1 0:33 |:| 0:10 2751189:5 0:30 1883:2 0:39 2609255:5 0:48 - U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:11745:2196 0 235|214 0:173 2282523:5 2746321:2 0:21 |:| 0:65 2746321:2 2282523:5 0:108 - U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:18358:2213 0 251|251 0:35 281093:5 0:3 651822:5 0:145 106591:3 0:21 |:| 0:64 106591:3 0:145 651822:5 - U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:14892:2226 0 68|59 0:34 |:| 0:25 - U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:18764:2247 0 146|146 0:112 |:| 0:112 - C MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:12147:2252 9606 220|220 9606:148 0:19 9606:19 |:| 9606:19 0:19 9606:148 - ``` - - > - > - > For JC1A sample - > 1. Is the first sequence in the file classified or unclassified? - > 2. What is the taxonomy ID assigned to the first classified sequence? - > 3. What is the corresponding taxon? - > - > > - > > 1. classified - > > 2. 9606, for the line 7 - > > 3. 9606 corresponds to Homo sapiens when looking at NCBI. - > - > {: .solution} - > - {: .question} - - -- **Report**: tabular files with one line per taxon and 6 columns or fields - - 1. Percentage of fragments covered by the clade rooted at this taxon - 2. Number of fragments covered by the clade rooted at this taxon - 3. Number of fragments assigned directly to this taxon - 4. A rank code, indicating - - (U)nclassified - - (R)oot - - (D)omain - - (K)ingdom - - (P)hylum - - (C)lass - - (O)rder - - (F)amily - - (G)enus, or - - (S)pecies - - Taxa that are not at any of these 10 ranks have a rank code that is formed by using the rank code of the closest ancestor rank with a number indicating the distance from that rank. E.g., `G2` is a rank code indicating a taxon is between genus and species and the grandparent taxon is at the genus rank. - - 5. NCBI taxonomic ID number - 6. Indented scientific name - - - ``` - Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 - 76.86 105399 105399 U 0 unclassified - 23.14 31740 1197 R 1 root - 22.20 30448 312 R1 131567 cellular organisms - 12.58 17254 3767 D 2 Bacteria - 8.77 12027 2867 P 1224 Proteobacteria - 4.94 6779 3494 C 28211 Alphaproteobacteria - 1.30 1782 1085 O 204455 Rhodobacterales - 0.43 593 461 F 31989 Rhodobacteraceae - 0.05 74 53 G 265 Paracoccus - ``` - - > > -> -> 1. What are the percentage on unclassified for JC1A and JP4D? -> 2. What are the kindgoms found for JC1A and JP4D? -> 3. Where might the eukaryotic DNA come from? -> 4. How is the diversity of Proteobacteria in JC1A and JP4D? -> -> > -> > -> > 1. 78% for JC1A and 90% for JP4D -> > 2. Kindgoms: -> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus -> > - JP4D: 10% Bacteria, 0.7% Eukaryota -> > 3. It seems to be human contamination -> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. -> -> {: .solution} -> -{: .question} - > - > 1. How much reads have not been classified? - > 2. How many taxons were found? - > 3. How much reads have been classified to Bacteria? - > 4. What are the different phylum identified? - > - > > - > > - > > 1. 76.86% of the reads - > > 2. There are 536 lines so 535 taxons - > > 3. 12.58% of reads have been assigned to Bacteria - > > 4. If we filter for P in column 4 (using {% tool [Filter](Filter1) %}), we can find 16 phylum: Proteobacteria, Actinobacteria, Firmicutes, Chloroflexi, Cyanobacteria, Bacteroidetes, Chlorobi, Planctomycetes, Verrucomicrobia, Spirochaetes, Acidobacteria, Chordata, Ascomycota, Apicomplexa, Negarnaviricota, Uroviricota - > - > {: .solution} - > - {: .question} - - -Getting an overview of the assignation is not straightforward with the **Kraken2** outputs directly. We can use visualisation tools for that. - -A "simple and worthwile addition to Kraken for better abundance estimates" {% cite Ye.2019 %} is called __Bracken__ (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species." {% cite Lu.2017 %} - -> estimate species abundance with Bracken (Name in Galaxy: Estimate Abundance at Taxonomic Level) -> -> 1. {% tool [Bracken](toolshed.g2.bx.psu.edu/view/iuc/bracken/79450f7fd718) %} with the following parameters: -> - *"Kraken report file"*: Report: Kraken2 on dataset collection (! do not use the classification file) -> - *"Select a kmer distribution"*: `PlusPF (2021-05-17)` (! it is important to choose the same database that you also chose for Kraken2) -> - *"Produce Kraken-Style Bracken report"*: `yes` -> -{: .hands_on} - -# Visualization of taxonomic assignment - -Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data. There are several tools for that: -- __Krona pie chart__ tool ({% cite Ondov.2011 %}) -- __Phinch__ ({% cite Bik.2014 %}) -- __Pavian__ ({% cite Breitwieser.2020 %}) - -## Visualisation using Krona - -__Krona__ creates an interactive HTML file allowing hierarchical data to be explored with zooming, multi-layered pie charts. With this tool, we can easily visualize the composition of the bacterial communities and compare how the populations of microorganisms are modified according to the conditions of the environment. - -Kraken outputs can not be given directly to **Kro> -> -> 1. What are the percentage on unclassified for JC1A and JP4D? -> 2. What are the kindgoms found for JC1A and JP4D? -> 3. Where might the eukaryotic DNA come from? -> 4. How is the diversity of Proteobacteria in JC1A and JP4D? -> -> > -> > -> > 1. 78% for JC1A and 90% for JP4D -> > 2. Kindgoms: -> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus -> > - JP4D: 10% Bacteria, 0.7% Eukaryota -> > 3. It seems to be human contamination -> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. -> -> {: .solution} -> -{: .question}na**, they first need to be converted. - -__Krakentools__ ({% cite Lu.2017 %}) is a suite of tools to work on Kraken outputs. It include a tool designed to translate results of the Kraken metagenomic classifier to the full representation of NCBI taxonomy. The output of this tool can be directly visualized by the Krona tool. - -> Convert Kraken report file -> -> 1. {% tool [Krakentools: Convert kraken report file](toolshed.g2.bx.psu.edu/repos/iuc/krakentools_kreport2krona/krakentools_kreport2krona/1.2+galaxy0) %} with the following parameters: -> - {% icon param-collection %} *"Kraken report file"*: **Report** collection of **Kraken** -> -> 2. Inspect the generated output for JC1A -{: .hands_on} - - -> -> -> ```> -> -> 1. What are the percentage on unclassified for JC1A and JP4D? -> 2. What are the kindgoms found for JC1A and JP4D? -> 3. Where might the eukaryotic DNA come from? -> 4. How is the diversity of Proteobacteria in JC1A and JP4D? -> -> > -> > -> > 1. 78% for JC1A and 90% for JP4D -> > 2. Kindgoms: -> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus -> > - JP4D: 10% Bacteria, 0.7% Eukaryota -> > 3. It seems to be human contamination -> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. -> -> {: .solution} -> -{: .question} -> 3868 k__Bacteria -> 2867 k__Bacteria p__Proteobacteria -> 3494 k__Bacteria p__Proteobacteria c__Alphaproteobacteria -> 1085 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales -> 461 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae -> 53 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus -> 10 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_pantotrophus -> 6 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sanguinis -> 4 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sp._AK26 -> 1 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sp._MA -> ``` -> -> 1. What are the different columns? -> 2. What are the lines? -> -> > -> > -> > 1. Column 1 seems to correspond to the number of fragments covered by a taxon, the columns after represent the different taxonomic level (from kingdom to species) -> > 2. A line is a taxon with its hierarchy and the number of reads assigned to it -> -> {: .solution} -> -{: .question} - -Let's now run **Krona** - -> Generate Krona visualisation -> 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1) %} with the following parameters: -> - *"Type of input data"*: `tabular` -> - {% icon param-collection %} *"Input file"*: output of **Krakentools** -> -> 2. Inspect the generated file -{: .hands_on} - - - -> -> -> 1. What are the percentage on unclassified for JC1A and JP4D? -> 2. What are the kindgoms found for JC1A and JP4D? -> 3. Where might the eukaryotic DNA come from? -> 4. How is the diversity of Proteobacteria in JC1A and JP4D? -> -> > -> > -> > 1. 78% for JC1A and 90% for JP4D -> > 2. Kindgoms: -> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus -> > - JP4D: 10% Bacteria, 0.7% Eukaryota -> > 3. It seems to be human contamination -> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. -> -> {: .solution} -> -{: .question} - - -## Visualization using Pavian - -__Pavian__ (pathogen visualization and more) ({% cite Breitwieser.2020 %}) is an interactive visualization tool for metagenomic data. It was developed for the clinical metagenomic problem to find a disease-causing pathogen in a patient sample, but it is useful to analyze and visualize any kind of metagenomics data. - -> Launch Pavian -> 1. {% tool [Pavian] (interactive_tool_pavian) %} with the following paramters: -> - {% icon param-collection %} *"Kraken and MetaPhlAn-style reports"*: **Report** collection of **Kraken** -> -{: .hands_on} - -**Pavian** runs a Galaxy Interactive tool. You can access it when it become orange. - -> Interact with Pavian -> -> 1. Open **Pavian** -> -> {% snippet faqs/galaxy/interactive_tools_open.md %} -> -> 2. Import data -> 1. Click on `Use data on server` -> 2. Select both samples -> 3. Click on `Read selected directories` -> 4. Check you have a table in `Available sample sets` looks like -> -> X | FormatOK | Include | Name | ReportFile | ReportFilePath -> --- | --- | --- | --- | --- | --- -> 1 | X | X | JP4AD | JP4AD | /home/shiny//JP4AD -> 2 | X | X | JC1A | JC1A | /home/shiny//JC1A -> -> 5. Click on `Save table` -> -> 3. Click on `Results Overview` in the left panel -> -{: .hands_on} - -This page shows the summary of the classifications in the selected sample set: - -![Screenshot of a table with 2 rows (JP4A and JC1A) and columns: Name, Number of raw reads, Classified reads, Chordate reads, Artificial reads, Unclassified reads, Microbial reads, Bacterial reads, Viral reads, Fungal reads, Protozoan reads. The cells have a barchart that shows the relation of the cell value to other cell values in the same category](.images/pavian-kraken-results-overview.png) - -> -> -> 1. Does both sample have same size? -> 1. What are the percentage of classified reads for JC1A and JP4D? -> 2. Are the percentage of bacterial reads similar? -> -> > -> > -> > 1. JP4D has much more reads than JC1A -> > 2. 10.2% for JP4D and 23.1% for JC1A -> > 3. 12.6% for JC1A and 9.44% for JP4D. So similar magnitude orders -> -> {: .solution} -> -{: .question} - -Let's now inspect assignements to reads per sample. - -> Inspect samples with Pavian -> -> 1. Click on `Sample` in the left panel -> 2. Select `JC1A` in the `Select sample` drop-down on the top -> -> The first view gives a Sankey diagram for one sample: -> -> ![Sankey plot with taxonomy hierarchy from domain on the left to species on the right](.images/pavian-kraken-sankey-JC1A.png) -> -> > -> > -> > 1. What is a Sankey diagram? -> > 2. What are the different set of values represented as the horizontal axis? -> > -> > > -> > > -> > > 1. A sankey diagram is a visualization used to depict a flow from one set of values to another -> > > 2. The taxonomy hierarchy from domain on the left to species on the right -> > -> > {: .solution} -> > -> {: .question} -> -> 3. Click on `Proteobacteria` in the Sankey plot -> 4. Inspect the created graph on the right -> -> ![Bar plot with the number of reads across all samples for Proteobacteria. JP4A bar is much higher than JC1A one. The turqoise bar shows the number of reads that are identified at the specific taxon; the orange bar shows the number of reads identified at children of the specified taxon.](.images/pavian-kraken-proteobacteria.png) -> -> > -> > -> > 1. Are the number of reads assigned to Proteobacteria similar for both samples? -> > 2. Why? -> > -> > > -> > > -> > > 1. JP4A has many more reads assigned to Proteobacteria than JC1A -> > > 2. JP4A has many more reads initially -> > -> > {: .solution} -> > -> {: .question} -{: .hands_on} - -We would like now to compare both samples. - -> Inspect samples with Pavian -> -> 1. Click on `Comparison` in the left panel -> 2. Select `%` and unclick `Reads` in the blue area drop-down on the top -> 3. Click on `Domain` green button -> -> ![Screenshot of a table with 4 rows (Bacteria, Eukaryota, Archaea, Viruses) and / columns (Name, Rank, TID, Max, JP4A, JC1A, Lineage)](.images/pavian-kraken-sankey-JC1A.png) -> -> > -> > -> > Is there similar proportion of Bacteria in both samples? -> > -> > > -> > > -> > > JP4D has much higher proportion of Bacteria (> 93%>) than JC1A (57%), which contains quite a lot of Eukaryote -> > {: .solution} -> > -> {: .question} -> -> 4. Select `Homo sapiens` in the `Filter taxa` box below the green buttons -> -> > -> > -> > Is there similar proportion of Bacteria in both samples? -> > -> > > -> > > -> > > After human filtering, both samples have similar proportion of Bacteria -> > {: .solution} -> > -> {: .question} -> -> 3. Click on `Class` green button -> -> > -> > -> > 1. How are the diversities of classes in both samples? -> > 2. What could it biologically mean given that JC1A is a control and JP4D a sample from fertilized pond? -> > -> > > -> > > -> > > 1. JP4D seems highly dominated by a Alphaproteobacteria class. JC1A has also a majority of Alphaproteobacteria, but also significant proportions of Betaproteobacteria, Gammaproteobacteria, Flavobacteria, Actinomycetia -> > > 2. Alphaproteobacteria seems to have a survival advantage in the new environment. According to the authors this correlates with specific genomic traits that enable them to cope better with high nutrient availability. -> > {: .solution} -> > -> {: .question} -> -{: .hands_on} - -Once you are done with Pavian, you should delete it in your history so the corresponding job is killed. - -> Visualize the taxonomical classification with Phinch -> -> __Phinch__ ({% cite Bik.2014 %}) is another tools to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. -> As a first step, we need to convert the Kraken output file into a kraken-biom file to make it accessible for Phinch. Fot this, we need to add a metadata file, provided here. xxx -When generating a metadata file for your own data, you can take this as an example and find the general guidelines [here](http://qiime.org/documentation/file_formats.html#mapping-file-overview) -> -> > Phinch -> > 1. Use {% tool [Kraken-biom] (toolshed.g2.bx.psu.edu/repos/iuc/kraken_biom/kraken_biom/1.2.0+galaxy1) %} to convert Kraken2 report into the correct format for phinch with the following parameters. -> > - *"Input"*: `Kraken2 on dataset collection` -> > - *"Sample Metadata file"*: `metadata.txt` -> > - *"Output format"*: `JSON` -> > -> > 2. **View** the file in Phinch -> > - Use {% tool [Phinch Visualisation] (interactive_tool_phinch) %} with the following paramters: -> > - *"Input"*: `Kraken-biom output file` -> {: .hands_on} -> -**Phinch** runs a Galaxy Interactive tool. You can access it when it become orange. - -> Interact with Pavian -> -> 1. Open **Phinch** -> -> {% snippet faqs/galaxy/interactive_tools_open.md %} - -{: .hands_on} - -> The first pages shows an overview of your samples. Here, you have the possibility to further filter your data, for example by date or location, depending on which information you provided in your metadata file. - -> -> -> 1. How many sequence reads do the samples contain? -> -> > -> > -> > 1. JC1A (Phinch name: 0) contains 56008 reads, while JP4D (Phinch name: 1) contains 242438 reads -> -> -> {: .solution} -> -{: .question} - - -Next, you click on ‘proceed to gallery’ to see an overview of all visualization options. -> -> Let’s have a look at the **taxonomy bar chart**. Here, you can see the abundance of different taxa depicted in different colors in your samples. On top of the chart you can select which rank is supposed to be shown in the chart. You can also change the display options to for example switch between value und percentage. -> -> > -> -> 1. What information can you get from hovering over a sample? -> 2. How many percent of the sample reads are bacteria and how many are eukaryota? -> -> > -> > -> > 1. the taxon’s name and the taxonomy occurrence in the sample -> > 2. choose kingdom and hover over the bars to find "taxonomy occurence in this sample": -> > > - Sample 0: 75,65 % bacteria; 24,51 % eukaryota -> > > - Sample 1: 92,70 % bacteria; 6,87 % eukaryota -> -> -> {: .solution} -> -{: .question} -> -Lets go back to the gallery and choose the **bubble chart**. Here, you can find the distribution of taxa across the whole dataset at the rank that you can choose above the chart. When hovering over the bubbles, you get additional information concerning the taxon. - -> > -> -> 1. Which is the most abundant Class and how many reads are found in both samples? -> -> > -> > -> > 1. To oder the bubbles according to their size you can choose the ‘list’ option shown right next to the taxonomy level. Clicking on one bubble gives you the direct comparison of the distribution of this taxon in the different samples. The most abundant Class is Alphaproteobacteria with 18.114 reads in Sample 0 and 153.230 reads in sample 1 -> -> -> {: .solution} -> -{: .question} - - -> -> Another displaying option is the **Sankey diagram**, that is depicting the abundance of taxonomies as a flow chart. Again, you can choose the taxonomy level that you want to show in your diagram. When clicking on one bar of the diagram, this part is enlarged for better view. - - -> The **donut partition** summarizes the microbial community according to non-numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘environmental medium’ to see the difference between sediment and water (It doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). When clicking on one part of the donut you will also find the distribution of the taxon across the samples. On the right hand side you can additionally choose if you’d like to have dynamic y axis or prefer standard y axis to compare different donuts with each other. -> -> The **attributes column chart** summarizes the microbial community according to numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the ‘geographic location’ to (again, it doesn’t really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). - -Once you are done with Phinch, you should delete it in your history so the corresponding job is killed. - -# MetaPhlAn - -In this tutorial, we follow second approach using **MetaPhlAn** ({% cite truong2015metaphlan2 %}). MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) at species-level. MetaPhlAn 4 relies on ~5.1M unique clade-specific marker genes identified from ~1M microbial genomes (~236,600 references (bacterial, archeal, viral and eukaryotic) and 771,500 metagenomic assembled genomes) spanning 26,970 species-level genome bins. - -It allows --unambiguous taxonomic assignments; --accurate estimation of organismal relative abundance; --species-level resolution for bacteria, archaea, eukaryotes and viruses; --strain identification and tracking --orders of magnitude speedups compared to existing methods. --microbiota strain-level population genomics --MetaPhlAn clade-abundance estimation - -The basic usage of MetaPhlAn consists in the identification of the clades (from phyla to species and strains in particular cases) present in the microbiota obtained from a microbiome sample and their relative abundance. - -It needs the reference database, which can be locally installed or customized using the dedicated tools. - -> Assign taxonomic labels with MetaPhlAn -> -> 1. {% tool [MetaPhlAn]([https://toolshed.g2.bx.psu.edu/repos/george-weingart/metaphlan]) %} with the following parameters: -> - *"Fasta/FastQ file(s) with microbiota reads"*: `Paired-end files` -> - *"Forward paired-end Fasta/FastQ file with microbiota reads"*: JP4D_R1 -> - *"Reverse paired-end Fasta/FastQ file with microbiota reads"*: JP4D_R2 -> -> - In *"Outputs"*: -> - *"Output for Krona?"*: `Yes` - -{: .hands_on} - - -5 files and a collection are generated by **MetaPhlAn** {% icon tool %}: - -- The main output: A **tabular file** called `Predicted taxon relative abundances` with the **community profile* - - ``` - #2202671 reads processed -#SampleID Metaphlan_Analysis -#clade_name NCBI_tax_id relative_abundance additional_species -k__Bacteria 2 100.0 -k__Bacteria|p__Bacteroidetes 2|976 94.38814 -k__Bacteria|p__Proteobacteria 2|1224 5.61186 -k__Bacteria|p__Bacteroidetes|c__CFGB45935 2|976| 94.38814 -k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria 2|1224|28211 5.61186 -k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935 2|976|| 94.38814 -k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales 2|1224|28211|204455 5.61186 -k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935 2|976||| 94.38814 -k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae 2|1224|28211|204455|31989 5.61186 -k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609 2|976|||| 94.38814 -k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes 2|1224|28211|204455|31989|2873978 5.61186 -k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609|s__GGB56609_SGB78025 2|976||||| 94.38814 -k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes|s__Phycocomes_zhengii 2|1224|28211|204455|31989|2873978|2056810 5.61186 -k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609|s__GGB56609_SGB78025|t__SGB78025 2|976|||||| 94.38814 -k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes|s__Phycocomes_zhengii|t__SGB31485 2|1224|28211|204455|31989|2873978|2056810| 5.61186 - ``` - - Each line contains 4 columns: - 1. the lineage with different taxonomic levels - 2. the previous lineage with NCBI taxon id - 3. the relative abundance found for our sample for the lineage - 4. any additional species - - The file starts with high level taxa (kingdom: `k__`) and go to more precise taxa. - - -- A **collection** with the same information as in the tabular file but splitted into different files, one per taxonomic level -- A **tabular file** called `Predicted taxon relative abundances for Krona` with the same information as the previous file but formatted for visualization using Krona. We will use this file later -- A **BIOM file** with the same information as the previous file but in BIOM format - - BIOM format is quite common in microbiomics. This is standard, for example, as the input for tools like mothur or QIIME. - -- A **SAM file** with the results of the sequence mapping on the reference database. -- A **tabular file** called `Bowtie2 output` with similar information as the one in the **SAM file** - - -Let's now run **Krona** - -> Generate Krona visualisation -> 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1) %} with the following parameters: -> - *"Type of input data"*: `tabular` -> - {% icon param-collection %} *"Input file"*: Krona output of **Metaphlan** -> -> 2. Inspect the generated file -{: .hands_on} - - - - -> > -> -> As you can see, there is barely any assignment. What might be the reason for this? -> -> > -> > -> > Probably, the reference database is not complete enough yet to identify all taxons. -> -> -> {: .solution} -> -{: .question} - - - -# Choosing the right tool - -When it comes to taxonomic assignment while analyzing metagenomic data, **Kraken2** is not the only tool available. Several papers do benchmarking of different tools ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}). - -> Benchmarking taxonomic classification tools -> -> The benchmarking papers present different methods for comparing the available tools: -> - The **CAMI challenge** is based on results of different labs that each used the CAMI dataset to perform their analysis on and send it back to the authors. -> - {% cite Ye.2019 %} performed all the analysis themselves -> -> Additionally, the datasets used for both benchmarking approaches differ: -> - **CAMI**: only ~30%-40% of reads are simulated from known taxa while the rest of the reads are from novel taxa, plasmids or simulated evolved strains. -> - {% cite Ye.2019 %} used International Metagenomics and Microbiome Standards Alliance (IMMSA) datasets, wherein the taxa are described better. -> -> When benchmarking different classification tools, several metrics are used to compare their performance: -> 1. **Precision**: proportion of true positive species identified in the sample divided by number of total species identified by the method -> 2. **Recall**: proportion of true positive species divided by the number of distinct species actually in the sample -> 3. Precision-recall curve: each point represents the precision and recall scores at a specific abundance threshold, the **area under the precision-recall curve (AUPR)** -> 4. **L2 distance**: representation of abundance profiles → how accurately the abundance of each species or genera in the resulting classification reflects the abundance of each species in the original biological sample (“ground truth”) -> -> When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. -{: .details} - -> Profiling tools -> -> Profilers, which are tools that investigate relative abundances of taxa within a dataset, fall into three groups depending on their performance: -> 1. Profilers, that correctly predict relative abundances -> 2. Precise profilers (suitable, when many false positives would increase cost and effort in downstream analysis) -> 3. Profilers with high recall (suitable for pathogen detection, when the failure of detecting an organism can have severe negative consequences) -> -> However, some characteristics are common to all profilers: -> - Most profilers only perform well until the family level -> - Drastic decrease in performance between family and genus level, while little change between order and family level -> - poorer performance of all profilers on CAMI datasets compared to International Metagenomics and Microbiome Standards Alliance (IMMSA) -> - Fidelity of abundance estimates decreases notably when viruses and plasmids were present -> - high numbers of false positive calls at low abundance -> - Taxonomic profilers vs profiles from taxonomic binning: -> Precision and recall of the taxonomic binners were comparable to that of the profilers; -> abundance estimation at higher ranks was more problematic for the binners -{: .details} - -Tool | Version | Available in Galaxy | CAMI challenge | {% cite Ye.2019 %} ---- | --- | --- | --- | --- -mOTUs | 1.1.1 | No | most memory efficient | -mOTUs | 2.5.1 | No | Great on marine and plant-associated datasets | -mOTUs | v.cami1 | No | Great on strain-madness dataset | -MetaPhlAn | 2.9.21 | Yes | Great on plant-associated datasets | Recommended for low computational requirements (< 2 Gb of memory) -MetaPhlAn | 2.9.22 | Yes | Great on marine; strain-madness datasets | -DUDes | v.cami1 | No| Great on strain-madness dataset | -FOCUS | 1.5 | No | Fast; most memory efficient | -Bracken | 2.2 | Yes (version 2.7) | Fast | -Bracken | 2.6 | Yes (version 2.7) | Great on plant-associated dataset -Kraken | 2.0.8 beta (GSA,Sr) | Yes | Great on marine datase; fastest; most memory efficient | Good performance metrics; very fast on large numbers of samples; allow custom databases when high amounts of memory (>100 Gb) are available - - -# Conclusion - -In this tutorial, we apply Kraken2 software to assign taxonomic labels to two microbiome sample datasets. 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title={Fungi biol control syst}, - author={Whipps, JM and Karen Lewis, RC}, - journal={Mycoparasitism and Plant Disease Control}, +@misc{whipps1988mycoparasitism, + title={Mycoparasitism and plant disease control}, + author={Whipps, John M and Lewis, Karen and Cooke, RC}, + journal={Fungi in biological control systems}, + volume={1988}, pages={161--187}, year={1988}, - publisher={Manchester University Press Manchester} + publisher={Manchester University Press Manchester}, + url={https://www.jstor.org/stable/2434885} } + @article{Wood.2019, abstract = {Although Kraken's k-mer-based approach provides a fast taxonomic classification of metagenomic sequence data, its large memory requirements can be limiting for some applications. Kraken 2 improves upon Kraken 1 by reducing memory usage by 85{\%}, allowing greater amounts of reference genomic data to be used, while maintaining high accuracy and increasing speed fivefold. Kraken 2 also introduces a translated search mode, providing increased sensitivity in viral metagenomics analysis.}, author = {Wood, Derrick E. and Lu, Jennifer and Langmead, Ben}, diff --git a/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md b/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md new file mode 100644 index 00000000000000..0a32f00e3c9e67 --- /dev/null +++ b/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md @@ -0,0 +1,768 @@ +--- +layout: tutorial_hands_on +title: Taxonomic Profiling and Visualization of Metagenomic Data +zenodo_link: https://zenodo.org/record/7871630 +questions: +- Which species (or genera, families, ...) are present in my sample? +- What are the different approaches and tools to get the community profile of my sample? +- How can we visualize and compare community profiles? +objectives: +- Explain what taxonomic assignment is +- Explain how taxonomic assignment works +- Apply Kraken to assign taxonomic labels +- Apply Krona and Pavian to visualize results of assignment and understand the output +- Identify taxonomic classification tool that fits best depending on their data +level: Introductory +key_points: +- There are 3 different types of taxonomic assignment (genome, gene or k-mer based) +- Kraken or MetaPhlAn can be used in Galaxy for taxonomic assignment +- Visualization of community profiles can be done using Krona or Pavian +- The taxonomic classification tool to use depends on the data +time_estimation: 2H +contributions: + authorship: + - sophia120199 + - bebatut +tags: +- metagenomics +- taxonomic profiling +--- + +# Introduction + +The term **"microbiome"** describes "a characteristic microbial community occupying a reasonably well-defined habitat which has distinct physio-chemical properties. The term thus not only refers to the microorganisms involved but also encompasses their theatre of activity" ({% cite whipps1988mycoparasitism %}). + +Microbiome data can be gathered from different environments such as soil, water or the human gut. The biological interest lies in general in the question how the microbiome present at a specific site influences this environment. To study a microbiome, we need to use indirect methods like metagenomics or metatranscriptomics. + +**Metagenomic samples** contain DNA from different organisms at a specific site, where the sample was collected. Metagenomic data can be used to find out which organisms coexist in that niche and which genes are present in the different organisms. +Metatranscriptomic samples include the transcribed gene products, thus RNA, that therefore allow to not only study the presence of genes but additionally their expression in the given environment. The following tutorial will focus on metagenomics data, but the principle is the same for metatranscriptomics data. + +The investigation of microorganisms present at a specific site and their relative abundance is also called **"microbial community profiling"**. +Basic for this is to find out which microorganisms are present in the sample. This can be achieved for all known microbes, where the DNA sequence specific for a certain species is known. + +For that we try to **identify the taxon** to which each individual reads belong. + +{% snippet topics/metagenomics/faqs/taxon.md %} + +When we talk about metagenomic data here, what we start with is sequences derived from DNA fragments that could be isolated from the sample of interest. Ideally, from all microbes present in the sample, we would also find DNA. The underlying idea of taxonomic assignment is to compare the DNA sequences found in the sample (reads) to DNA sequences of a database. When a read matches a database DNA sequence of a known microbe, we can derive a list with microbes present in the sample. + +When talking about taxonomic assignment or taxonomic classification, most of the time we actually talk about two methods, that in practice are often used interchangeably: +- **taxonomic binning**: the clustering of individual sequence reads based on similarities criteria and assignation of clusters to reference taxa +- **taxonomic profiling**: classification of individual reads to reference taxa to extract the relative abundances of the different taxa + +## Taxonomic profiling + +Tools for taxonomic profiling can be divided into three groups. Nevertheless, all of them require a pre-computed database based on previously sequenced microbial DNA or protein sequences. +1. **DNA-to-DNA**: comparison of sequencing reads with genomic databases of DNA sequences, with tools like Kraken ({% cite Wood2014 %}) +2. **DNA-to-Protein** : compare sequencing reads with protein databases (more computationally intensive because of analysis of all six frames of potential DNA-to amino acid translation, with tools like DIAMOND +3. **Marker based**: search for marker genes (e.g. 16S rRNA sequence) in reads, which is quick, but introduces bias, with tools like MetaPhlAn ({% cite blanco2023extending %}) + +The comparison of reads to database sequences can be done in different ways, leading to three different types of taxonomic assignment: + +- **Genome based** approach + + Reads are aligned to reference genomes. Considering the coverage and breadth, genomes are used to measure genome abundance. Furthermore, genes can be analyzed in genomic context. Advantages of this method are the high detection accuracy, that the unclassified percentage is known, that all SNVs can be detected and that high-resolution genomic comparisons are possible. This method takes medium compute cost. + +- **Gene based** approach + + Reads are aligned to reference genes. Next, marker genes are used to estimate species abundance. Furthermore, genes can be analyzed in isolation for presence or absence in a specific condition. The major advantage is the detection of the pangenome (entire set of genes within a species). Major disadvantages are the high compute cost, low detection accuracy and that the unclassified percentage is unknown. At least intragenic SNVs can be detected and low-resolution genomic comparison is possible. + +- **k-mer based** approach + + Databases as well as the samples DNA are broken into strings of length $$k$$ for comparison. From all the genomes in the database, where a specific k-mer is found, a lowest common ancestor (LCA) tree is derived and the abundance of k-mers within the tree is counted. This is the basis for a root-to-leaf path calculation, where the path with the highest score is used for classification of the sample. By counting the abundance of k-mers, also an estimation of relative abundance of taxa is possible. The major advantage of k-mer based analysis is the low compute cost. Major disadvantages are the low detection accuracy, that the unclassified percentage is unknown and that there is no gene detection, no SNVs detection and no genomic comparison possible. An example for a k-mer based analysis tool is Kraken, which will be used in this tutorial + +After this theoretical introduction, let's now get hands on analyzing an actual dataset! + +## Background on data + +The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas ({% cite Okie.2020 %}). The researchers were interested in genomic traits that affect the rates and costs of biochemical information processing within cells. They performed a whole-ecosystem experiment, thus fertilizing the pond to achieve nutrient enriched conditions. + +Here we will use 2 datasets: +- `JP4D`: a microbiome sample collected from the Lagunita Fertilized Pond +- `JC1A`: a **control** samples from a control mesocosm. + +The datasets differ in size, but according to the authors this doesn't matter for their analysis of genomic traits. Also, they underline that differences between the two samples reflect trait-mediated ecological dynamics instead of microevolutionary changes as the duration of the experiment was only 32 days. This means that depending on available nutrients, specific lineages within the pond grow more successfully than others because of their genomic traits. + +The datafiles are named according to the first four characters of the filenames. +It is a collection of paired-end data with R1 being the forward reads and R2 being the reverse reads. Additionally, the reads have been trimmed using __cutadapt__ as explained in the [Quality control tutorial]({% link topics/sequence-analysis/tutorials/quality-control/tutorial.md %}) + +> +> +> In this tutorial, we will cover: +> +> 1. TOC +> {:toc} +> +{: .agenda} + +# Prepare Galaxy and data + +Any analysis should get its own Galaxy history. So let's start by creating a new one: + +> Data upload +> +> 1. Create a new history for this analysis +> +> {% snippet faqs/galaxy/histories_create_new.md %} +> +> 2. Rename the history +> +> {% snippet faqs/galaxy/histories_rename.md %} +> +{: .hands_on} + +We need now to import the data + +> Import datasets +> +> 1. Import the following samples via link from [Zenodo]({{ page.zenodo_link }}) or Galaxy shared data libraries: +> +> ```text +> {{ page.zenodo_link }}/files/JC1A_R1.fastq.gz +> {{ page.zenodo_link }}/files/JC1A_R2.fastq.gz +> {{ page.zenodo_link }}/files/JP4D_R1.fastq.gz +> {{ page.zenodo_link }}/files/JP4D_R2.fastq.gz +> ``` +> +> {% snippet faqs/galaxy/datasets_import_via_link.md %} +> {% snippet faqs/galaxy/datasets_import_from_data_library.md %} +> +> 2. 3. Create a paired collection. +> +> {% snippet faqs/galaxy/collections_build_list_paired.md %} +> +{: .hands_on} + +# k-mer based taxonomic assignment of metagenomic data + +Our input data is the DNA reads of microbes present at Cuatro Ciénegas. + +To find out which microorganisms are present, we will compare the reads of the sample to a reference database, i.e. sequences of known microorganisms stored in a database, using **Kraken2** ({% cite wood2019improved %}). + +{% snippet topics/metagenomics/faqs/kraken.md %} + +For this tutorial, we will use the Standard (archaea, bacteria, viral, plasmid, human, UniVec_Core) plus protozoa & fungi database, which means: + +- archaea: RefSeq complete archaeal genomes/proteins +- bacteria: RefSeq complete bacterial genomes/proteins +- plasmid: RefSeq plasmid nucleotide/protein sequences +- viral: RefSeq complete viral genomes/proteins +- human: GRCh38 human genome/proteins +- fungi: RefSeq complete fungal genomes/proteins +- plant: RefSeq complete plant genomes/proteins +- protozoa: RefSeq complete protozoan genomes/proteins +- UniVec_Core: A subset of UniVec, NCBI-supplied database of vector, adapter, linker, and primer sequences that may be contaminating sequencing projects and/or assemblies, chosen to minimize false positive hits to the vector database + +> Assign taxonomic labels with Kraken2 +> +> 1. {% tool [Kraken2](toolshed.g2.bx.psu.edu/repos/iuc/kraken2/kraken2/2.1.1+galaxy1) %} with the following parameters: +> - *"Single or paired reads"*: `Paired Collection` +> - {% icon param-collection %} *"Collection of paired reads"*: Input paired collection +> - *"Confidence"*: `0.1` +> +> A confidence score of 0.1 means that at least 10% of the k-mers should match entries in the database. This value can be reduced if a less restrictive taxonomic assignation is desired. +> +> - In *"Create Report"*: +> - *"Print a report with aggregrate counts/clade to file"*: `Yes` +> - *"Select a Kraken2 database"*: most recent `Prebuilt Refseq indexes: PlusPF` +> +{: .hands_on} + +**Kraken2** will create two outputs for each dataset + +- **Classification**: tabular files with one line for each sequence classified by Kraken and 5 columns: + + 1. `C`/`U`: a one letter indicating if the sequence classified or unclassified + 2. Sequence ID as in the input file + 3. NCBI taxonomy ID assigned to the sequence, or 0 if unclassified + 4. Length of sequence in bp (`read1|read2` for paired reads) + 5. A space-delimited list indicating the lowest common ancestor (LCA) mapping of each k-mer in the sequence + + For example, `562:13 561:4 A:31 0:1 562:3` would indicate that: + 1. The first 13 k-mers mapped to taxonomy ID #562 + 2. The next 4 k-mers mapped to taxonomy ID #561 + 3. The next 31 k-mers contained an ambiguous nucleotide + 4. The next k-mer was not in the database + 5. The last 3 k-mers mapped to taxonomy ID #562 + + `|:|` indicates end of first read, start of second read for paired reads + + For JC1A: + + ``` + Column 1 Column 2 Column 3 Column 4 Column 5 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:13417:1998 0 151|190 A:18 0:14 2055:5 0:1 2220095:5 0:74 |:| 0:3 A:54 2:1 0:32 204455:1 2823043:5 0:60 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:15782:2187 0 169|173 0:101 37329:1 0:33 |:| 0:10 2751189:5 0:30 1883:2 0:39 2609255:5 0:48 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:11745:2196 0 235|214 0:173 2282523:5 2746321:2 0:21 |:| 0:65 2746321:2 2282523:5 0:108 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:18358:2213 0 251|251 0:35 281093:5 0:3 651822:5 0:145 106591:3 0:21 |:| 0:64 106591:3 0:145 651822:5 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:14892:2226 0 68|59 0:34 |:| 0:25 + U MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:18764:2247 0 146|146 0:112 |:| 0:112 + C MISEQ-LAB244-W7:91:000000000-A5C7L:1:1101:12147:2252 9606 220|220 9606:148 0:19 9606:19 |:| 9606:19 0:19 9606:148 + ``` + + > + > + > For JC1A sample + > 1. Is the first sequence in the file classified or unclassified? + > 2. What is the taxonomy ID assigned to the first classified sequence? + > 3. What is the corresponding taxon? + > + > > + > > 1. classified + > > 2. 9606, for the line 7 + > > 3. 9606 corresponds to Homo sapiens when looking at NCBI. + > {: .solution} + {: .question} + +- **Report**: tabular files with one line per taxon and 6 columns or fields + + 1. Percentage of fragments covered by the clade rooted at this taxon + 2. Number of fragments covered by the clade rooted at this taxon + 3. Number of fragments assigned directly to this taxon + 4. A rank code, indicating + - (U)nclassified + - (R)oot + - (D)omain + - (K)ingdom + - (P)hylum + - (C)lass + - (O)rder + - (F)amily + - (G)enus, or + - (S)pecies + + Taxa that are not at any of these 10 ranks have a rank code that is formed by using the rank code of the closest ancestor rank with a number indicating the distance from that rank. E.g., `G2` is a rank code indicating a taxon is between genus and species and the grandparent taxon is at the genus rank. + + 5. NCBI taxonomic ID number + 6. Indented scientific name + + ``` + Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 + 76.86 105399 105399 U 0 unclassified + 23.14 31740 1197 R 1 root + 22.20 30448 312 R1 131567 cellular organisms + 12.58 17254 3767 D 2 Bacteria + 8.77 12027 2867 P 1224 Proteobacteria + 4.94 6779 3494 C 28211 Alphaproteobacteria + 1.30 1782 1085 O 204455 Rhodobacterales + 0.43 593 461 F 31989 Rhodobacteraceae + 0.05 74 53 G 265 Paracoccus + ``` + + > + > + > 1. What are the percentage on unclassified for JC1A and JP4D? + > 2. What are the kindgoms found for JC1A and JP4D? + > 3. Where might the eukaryotic DNA come from? + > 4. How is the diversity of Proteobacteria in JC1A and JP4D? + > + > > + > > + > > 1. 78% for JC1A and 90% for JP4D + > > 2. Kindgoms: + > > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus + > > - JP4D: 10% Bacteria, 0.7% Eukaryota + > > 3. It seems to be human contamination + > > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. + > {: .solution} + > + {: .question} + +sGetting an overview of the assignation is not straightforward with the **Kraken2** outputs directly. We can use visualisation tools for that. + +A "simple and worthwile addition to Kraken for better abundance estimates" ({% cite Ye.2019 %}) is called __Bracken__ (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species" ({% cite Lu.2017 %}). + +> Estimate species abundance with Bracken +> +> 1. {% tool [Estimate Abundance at Taxonomic Level](toolshed.g2.bx.psu.edu/repos/iuc/bracken/est_abundance/2.7+galaxy1) %} with the following parameters: +> - {% icon param-collection %} *"Kraken report file"*: **Report** output of **Kraken** +> - *"Select a kmer distribution"*: `PlusPF (2021-05-17)` +> +> It is important to choose the same database that you also chose for Kraken2 +> +> - *"Produce Kraken-Style Bracken report"*: `yes` +> +{: .hands_on} + +# Visualization of taxonomic assignment + +Once we have assigned the corresponding taxa to each sequence, the next step is to properly visualize the data. There are several tools for that: +- __Krona pie chart__ tool ({% cite Ondov.2011 %}) +- __Phinch__ ({% cite Bik.2014 %}) +- __Pavian__ ({% cite Breitwieser.2020 %}) + +## Visualisation using Krona + +__Krona__ creates an interactive HTML file allowing hierarchical data to be explored with zooming, multi-layered pie charts. With this tool, we can easily visualize the composition of the bacterial communities and compare how the populations of microorganisms are modified according to the conditions of the environment. + +Kraken outputs can not be given directly to **Krona**, they first need to be converted. + +__Krakentools__ ({% cite Lu.2017 %}) is a suite of tools to work on Kraken outputs. It include a tool designed to translate results of the Kraken metagenomic classifier to the full representation of NCBI taxonomy. The output of this tool can be directly visualized by the Krona tool. + +> Convert Kraken report file +> +> 1. {% tool [Krakentools: Convert kraken report file](toolshed.g2.bx.psu.edu/repos/iuc/krakentools_kreport2krona/krakentools_kreport2krona/1.2+galaxy0) %} with the following parameters: +> - {% icon param-collection %} *"Kraken report file"*: **Report** collection of **Kraken** +> +> 2. Inspect the generated output for JC1A +{: .hands_on} + + +> +> +> ``` +> 3868 k__Bacteria +> 2867 k__Bacteria p__Proteobacteria +> 3494 k__Bacteria p__Proteobacteria c__Alphaproteobacteria +> 1085 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales +> 461 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae +> 53 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus +> 10 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_pantotrophus +> 6 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sanguinis +> 4 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sp._AK26 +> 1 k__Bacteria p__Proteobacteria c__Alphaproteobacteria o__Rhodobacterales f__Rhodobacteraceae g__Paracoccus s__Paracoccus_sp._MA +> ``` +> +> 1. What are the different columns? +> 2. What are the lines? +> +> > +> > +> > 1. Column 1 seems to correspond to the number of fragments covered by a taxon, the columns after represent the different taxonomic level (from kingdom to species) +> > 2. A line is a taxon with its hierarchy and the number of reads assigned to is +> {: .solution} +> +{: .question} + +Let's now run **Krona** + +> Generate Krona visualisation +> 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1+galaxy0) %} with the following parameters: +> - *"Type of input data"*: `Tabular` +> - {% icon param-collection %} *"Input file"*: output of **Krakentools** +> +> 2. Inspect the generated file +{: .hands_on} + + + +> +> +> 1. What are the percentage on unclassified for JC1A and JP4D? +> 2. What are the kindgoms found for JC1A and JP4D? +> 3. Where might the eukaryotic DNA come from? +> 4. How is the diversity of Proteobacteria in JC1A and JP4D? +> +> > +> > +> > 1. 78% for JC1A and 90% for JP4D +> > 2. Kindgoms: +> > - JC1A: 13% Bacteria, 10% Eukaryota, 0.03% Virus +> > - JP4D: 10% Bacteria, 0.7% Eukaryota +> > 3. It seems to be human contamination +> > 4. JC1A seems to have a big diversity of classes and species of Proteobacteria. JP4D seems more dominated by Aphaproteobacteria. +> {: .solution} +> +{: .question} + + +## Visualization using Pavian + +__Pavian__ (pathogen visualization and more) ({% cite Breitwieser.2020 %}) is an interactive visualization tool for metagenomic data. It was developed for the clinical metagenomic problem to find a disease-causing pathogen in a patient sample, but it is useful to analyze and visualize any kind of metagenomics data. + +> Launch Pavian +> 1. {% tool [Pavian](interactive_tool_pavian) %} with the following paramters: +> - {% icon param-collection %} *"Kraken and MetaPhlAn-style reports"*: **Report** collection of **Kraken** +> +{: .hands_on} + +**Pavian** runs a Galaxy Interactive tool. You can access it when it become orange. + +> Interact with Pavian +> +> 1. Open **Pavian** +> +> {% snippet faqs/galaxy/interactive_tools_open.md %} +> +> 2. Import data +> 1. Click on `Use data on server` +> 2. Select both samples +> 3. Click on `Read selected directories` +> 4. Check you have a table in `Available sample sets` looks like +> +> X | FormatOK | Include | Name | ReportFile | ReportFilePath +> --- | --- | --- | --- | --- | --- +> 1 | X | X | JP4AD | JP4AD | /home/shiny//JP4AD +> 2 | X | X | JC1A | JC1A | /home/shiny//JC1A +> +> 5. Click on `Save table` +> +> 3. Click on `Results Overview` in the left panel +> +{: .hands_on} + +This page shows the summary of the classifications in the selected sample set: + +![Screenshot of a table with 2 rows (JP4A and JC1A) and columns: Name, Number of raw reads, Classified reads, Chordate reads, Artificial reads, Unclassified reads, Microbial reads, Bacterial reads, Viral reads, Fungal reads, Protozoan reads. The cells have a barchart that shows the relation of the cell value to other cell values in the same category](./images/pavian-kraken-results-overview.png) + +> +> +> 1. Does both sample have same size? +> 1. What are the percentage of classified reads for JC1A and JP4D? +> 2. Are the percentage of bacterial reads similar? +> +> > +> > +> > 1. JP4D has much more reads than JC1A +> > 2. 10.2% for JP4D and 23.1% for JC1A +> > 3. 12.6% for JC1A and 9.44% for JP4D. So similar magnitude orders +> {: .solution} +> +{: .question} + +Let's now inspect assignements to reads per sample. + +> Inspect samples with Pavian +> +> 1. Click on `Sample` in the left panel +> 2. Select `JC1A` in the `Select sample` drop-down on the top +> +> The first view gives a Sankey diagram for one sample: +> +> ![Sankey plot with taxonomy hierarchy from domain on the left to species on the right](./images/pavian-kraken-sankey-JC1A.png) +> +> > +> > +> > 1. What is a Sankey diagram? +> > 2. What are the different set of values represented as the horizontal axis? +> > +> > > +> > > +> > > 1. A sankey diagram is a visualization used to depict a flow from one set of values to another +> > > 2. The taxonomy hierarchy from domain on the left to species on the right +> > {: .solution} +> > +> {: .question} +> +> 3. Click on `Proteobacteria` in the Sankey plot +> 4. Inspect the created graph on the right +> +> ![Bar plot with the number of reads across all samples for Proteobacteria. JP4A bar is much higher than JC1A one. The turqoise bar shows the number of reads that are identified at the specific taxon; the orange bar shows the number of reads identified at children of the specified taxon.](./images/pavian-kraken-proteobacteria.png) +> +> > +> > +> > 1. Are the number of reads assigned to Proteobacteria similar for both samples? +> > 2. Why? +> > +> > > +> > > +> > > 1. JP4A has many more reads assigned to Proteobacteria than JC1A +> > > 2. JP4A has many more reads initialls +> > {: .solution} +> > +> {: .question} +{: .hands_on} + +We would like now to compare both samples. + +> Inspect samples with Pavian +> +> 1. Click on `Comparison` in the left panel +> 2. Select `%` and unclick `Reads` in the blue area drop-down on the top +> 3. Click on `Domain` green button +> +> ![Screenshot of a table with 4 rows (Bacteria, Eukaryota, Archaea, Viruses) and / columns (Name, Rank, TID, Max, JP4A, JC1A, Lineage)](./images/pavian-kraken-sankey-JC1A.png) +> +> > +> > +> > Is there similar proportion of Bacteria in both samples? +> > +> > > +> > > +> > > JP4D has much higher proportion of Bacteria (> 93%>) than JC1A (57%), which contains quite a lot of Eukaryote +> > {: .solution} +> > +> {: .question} +> +> 4. Select `Homo sapiens` in the `Filter taxa` box below the green buttons +> +> > +> > +> > Is there similar proportion of Bacteria in both samples? +> > +> > > +> > > +> > > After human filtering, both samples have similar proportion of Bacteria +> > {: .solution} +> > +> {: .question} +> +> 3. Click on `Class` green button +> +> > +> > +> > 1. How are the diversities of classes in both samples? +> > 2. What could it biologically mean given that JC1A is a control and JP4D a sample from fertilized pond? +> > +> > > +> > > +> > > 1. JP4D seems highly dominated by a Alphaproteobacteria class. JC1A has also a majority of Alphaproteobacteria, but also significant proportions of Betaproteobacteria, Gammaproteobacteria, Flavobacteria, Actinomycetia +> > > 2. Alphaproteobacteria seems to have a survival advantage in the new environment. According to the authors this correlates with specific genomic traits that enable them to cope better with high nutrient availability. +> > {: .solution} +> > +> {: .question} +> +{: .hands_on} + +Once you are done with Pavian, you should delete it in your history so the corresponding job is killed. + +> Visualize the taxonomical classification with Phinch +> +> __Phinch__ ({% cite Bik.2014 %}) is another tools to visualize large biological datasets like our taxonomic classification. Taxonomy Bar Charts, Bubble Charts, Sankey Diagrams, Donut Partitions and Attributes Column Chart can be generated using this tool. +> +> As a first step, we need to convert the Kraken output file into a kraken-biom file to make it accessible for Phinch. For this, we need to add a metadata file, provided on [Zenodo]({{ page.zenodo_link }}/files/metadata.tabular). +> When generating a metadata file for your own data, you can take this as an example and apply the [general guidelines](http://qiime.org/documentation/file_formats.html). +> +> > Phinch +> > 1. Import the metadata tabular from [Zenodo]({{ page.zenodo_link }}) or Galaxy shared data libraries: +> > +> > ```text +> > {{ page.zenodo_link }}/files/metadata.tabular +> > ``` +> > +> > 1. Use {% tool [Kraken-biom](toolshed.g2.bx.psu.edu/repos/iuc/kraken_biom/kraken_biom/1.2.0+galaxy1) %} to convert Kraken2 report into the correct format for phinch with the following parameters. +> > - {% icon param-collection %} *"Input"*: **Report** output of **Kraken2** +> > - {% icon param-file %} *"Sample Metadata file"*: Metadata tabular +> > - *"Output format"*: `JSON` +> > +> > 2. {% tool [Phinch Visualisation](interactive_tool_phinch) %} with the following paramters: +> > - *"Input"*: `Kraken-biom output file` +> {: .hands_on} +> +> **Phinch** runs a Galaxy Interactive tool. You can access it when it become orange. +> +> > Interact with Phinch +> > +> > 1. Open **Phinch** +> > +> > {% snippet faqs/galaxy/interactive_tools_open.md %} +> > +> > The first pages shows an overview of your samples. Here, you have the possibility to further filter your data, for example by date or location, depending on which information you provided in your metadata file. +> > +> > > +> > > +> > > 1. How many sequence reads do the samples contain? +> > > +> > > > +> > > > +> > > > 1. JC1A (Phinch name: 0) contains 56008 reads, while JP4D (Phinch name: 1) contains 242438 reads +> > > {: .solution} +> > {: .question} +> > +> > 2. Click on `Proceed to gallery` to see an overview of all visualization options. +> {: .hands_on} +> +> Let's have a look at the **taxonomy bar chart**. Here, you can see the abundance of different taxa depicted in different colors in your samples. On top of the chart you can select which rank is supposed to be shown in the chart. You can also change the display options to for example switch between value und percentage. +> +> > +> > +> > 1. What information can you get from hovering over a sample? +> > 2. How many percent of the sample reads are bacteria and how many are eukaryota? +> > +> > > +> > > +> > > 1. the taxon’s name and the taxonomy occurrence in the sample +> > > 2. choose kingdom and hover over the bars to find "taxonomy occurence in this sample": +> > > - Sample 0: 75,65 % bacteria; 24,51 % eukaryota +> > > - Sample 1: 92,70 % bacteria; 6,87 % eukaryota +> > {: .solution} +> {: .question} +> +> Let's go back to the gallery and choose the **bubble chart**. Here, you can find the distribution of taxa across the whole dataset at the rank that you can choose above the chart. When hovering over the bubbles, you get additional information concerning the taxon. +> +> > +> > +> > 1. Which is the most abundant Class? +> > 2. How many reads are found in both samples? +> > +> > > +> > > +> > > To order the bubbles according to their size you can choose the `list` option shown right next to the taxonomy level. Clicking on one bubble gives you the direct comparison of the distribution of this taxon in the different samples. +> > > 1. The most abundant Class is Alphaproteobacteria +> > > 2. With 18.114 reads in Sample 0 and 153.230 reads in sample 1 +> > {: .solution} +> {: .question} +> +> +> Another displaying option is the **Sankey diagram**, that is depicting the abundance of taxonomies as a flow chart. Again, you can choose the taxonomy level that you want to show in your diagram. When clicking on one bar of the diagram, this part is enlarged for better view. +> +> The **donut partition** summarizes the microbial community according to non-numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the 'environmental medium' to see the difference between sediment and water (It doesn't really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). When clicking on one part of the donut you will also find the distribution of the taxon across the samples. On the right hand side you can additionally choose if you’d like to have dynamic y axis or prefer standard y axis to compare different donuts with each other. +> +> The **attributes column chart** summarizes the microbial community according to numerical attributes. In the drop-down menu at the top right corner, you can switch between the different attributes provided in the metadata file. In our case, you can for example choose the 'geographic location' to (again, it doesn't really make a lot of sense in our very simple dataset, as this will show the same result as sorting them by sample 0 and 1, but if attributes change across different samples this might be an interesting visualization option). +> +> Once you are done with Phinch, you should delete it in your history so the corresponding job is killed. +{: .details} + +# Choosing the right tool + +When it comes to taxonomic assignment while analyzing metagenomic data, **Kraken2** is not the only tool available. Several papers do benchmarking of different tools ({% cite Meyer.2022 %},{% cite Sczyrba.2017 %},{% cite Ye.2019 %}). + +> Benchmarking taxonomic classification tools +> +> The benchmarking papers present different methods for comparing the available tools: +> - The **CAMI challenge** is based on results of different labs that each used the CAMI dataset to perform their analysis on and send it back to the authors. +> - {% cite Ye.2019 %} performed all the analysis themselves +> +> Additionally, the datasets used for both benchmarking approaches differ: +> - **CAMI**: only ~30%-40% of reads are simulated from known taxa while the rest of the reads are from novel taxa, plasmids or simulated evolved strains. +> - {% cite Ye.2019 %} used International Metagenomics and Microbiome Standards Alliance (IMMSA) datasets, wherein the taxa are described better. +> +> When benchmarking different classification tools, several metrics are used to compare their performance: +> 1. **Precision**: proportion of true positive species identified in the sample divided by number of total species identified by the method +> 2. **Recall**: proportion of true positive species divided by the number of distinct species actually in the sample +> 3. Precision-recall curve: each point represents the precision and recall scores at a specific abundance threshold, the **area under the precision-recall curve (AUPR)** +> 4. **L2 distance**: representation of abundance profiles → how accurately the abundance of each species or genera in the resulting classification reflects the abundance of each species in the original biological sample (“ground truth”) +> +> When it comes to taxonomic profiling, thus investigating the abundance of specific taxa, the biggest problem is the abundance bias. It is introduced during isolation of DNA (which might work for some organisms better then for others) and by PCR duplicates during PCR amplification. +{: .details} + +> Profiling tools +> +> Profilers, which are tools that investigate relative abundances of taxa within a dataset, fall into three groups depending on their performance: +> 1. Profilers, that correctly predict relative abundances +> 2. Precise profilers (suitable, when many false positives would increase cost and effort in downstream analysis) +> 3. Profilers with high recall (suitable for pathogen detection, when the failure of detecting an organism can have severe negative consequences) +> +> However, some characteristics are common to all profilers: +> - Most profilers only perform well until the family level +> - Drastic decrease in performance between family and genus level, while little change between order and family level +> - poorer performance of all profilers on CAMI datasets compared to International Metagenomics and Microbiome Standards Alliance (IMMSA) +> - Fidelity of abundance estimates decreases notably when viruses and plasmids were present +> - high numbers of false positive calls at low abundance +> - Taxonomic profilers vs profiles from taxonomic binning: +> Precision and recall of the taxonomic binners were comparable to that of the profilers; +> abundance estimation at higher ranks was more problematic for the binners +{: .details} + +Tool | Approach | Available in Galaxy | CAMI challenge | {% cite Ye.2019 %} +--- | --- | --- | --- | --- +mOTUs | | No | Most memory efficient | +MetaPhlAn | Marker genes | Yes | | Recommended for low computational requirements (< 2 Gb of memory) +DUDes | | No | | +FOCUS | | No | Fast, most memory efficient | +Bracken | k-mer | Yes | Fast | +Kraken | k-mer | Yes | Fastest; most memory efficient | Good performance metrics; very fast on large numbers of samples; allow custom databases when high amounts of memory (>100 Gb) are available + +> Using a marker gene approach with MetaPhlAn +> +> In this tutorial, we follow second approach using **MetaPhlAn** ({% cite truong2015metaphlan2 %}). MetaPhlAn is a computational tool for profiling the composition of microbial communities (Bacteria, Archaea and Eukaryotes) from metagenomic shotgun sequencing data (i.e. not 16S) at species-level. MetaPhlAn 4 relies on ~5.1M unique clade-specific marker genes identified from ~1M microbial genomes (~236,600 references (bacterial, archeal, viral and eukaryotic) and 771,500 metagenomic assembled genomes) spanning 26,970 species-level genome bins. +> +> It allows: +> - unambiguous taxonomic assignments; +> - accurate estimation of organismal relative abundance; +> - species-level resolution for bacteria, archaea, eukaryotes and viruses; +> - strain identification and tracking +> - orders of magnitude speedups compared to existing methods. +> - microbiota strain-level population genomics +> - MetaPhlAn clade-abundance estimation +> +> The basic usage of MetaPhlAn consists in the identification of the clades (from phyla to species and strains in particular cases) present in the microbiota obtained from a microbiome sample and their relative abundance. +> +> **MetaPhlAn** in Galaxy can not directly take as input a paired collection but expect 2 collections: 1 with forward data and 1 with reverse. Before launching **MetaPhlAn**, we need to split our input paired collection. +> +> > Assign taxonomic labels with MetaPhlAn +> > +> > 1. {% tool [Unzip collection](__UNZIP_COLLECTION__) %} with the following parameters: +> > - {% icon param-collection %} *"Paired input to unzip"*: Input paired collection +> > +> > 2. {% tool [MetaPhlAn](toolshed.g2.bx.psu.edu/repos/iuc/metaphlan/metaphlan/4.0.6+galaxy1) %} with the following parameters: +> > - In *"Inputs"* +> > - *"Input(s)"*: `Fasta/FastQ file(s) with microbiota reads` +> > - *"Fasta/FastQ file(s) with microbiota reads"*: `Paired-end files` +> > - {% icon param-collection %} *"Forward paired-end Fasta/FastQ file with microbiota reads"*: output of **Unzip collection** with **forward** in the name +> > - {% icon param-collection %} *"Reverse paired-end Fasta/FastQ file with microbiota reads"*: output of **Unzip collection** with **reverse** in the name +> > - In *"Outputs"*: +> > - *"Output for Krona?"*: `Yes` +> {: .hands_on} +> +> +> 5 files and a collection are generated by **MetaPhlAn** {% icon tool %}: +> +> - `Predicted taxon relative abundances`: tabular files with the **community profile** +> +> ``` +> #SampleID Metaphlan_Analysis +> #clade_name NCBI_tax_id relative_abundance additional_species +> k__Bacteria 2 100.0 +> k__Bacteria|p__Bacteroidetes 2|976 94.38814 +> k__Bacteria|p__Proteobacteria 2|1224 5.61186 +> k__Bacteria|p__Bacteroidetes|c__CFGB45935 2|976| 94.38814 +> k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria 2|1224|28211 5.61186 +> k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935 2|976|| 94.38814 +> k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales 2|1224|28211|204455 5.61186 +> k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935 2|976||| 94.38814 +> k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae 2|1224|28211|> 204455| 31989 5.61186 +> k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609 2|976|||| 94.38814 +> k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes 2| 1224| 28211|204455|31989|2873978 5.61186 +> k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609|s__GGB56609_SGB78025 2| 976||||| 94. 38814 +> k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes| s__Phycocomes_zhengii 2|1224|28211|204455|31989|2873978|2056810 5.61186 +> k__Bacteria|p__Bacteroidetes|c__CFGB45935|o__OFGB45935|f__FGB45935|g__GGB56609|s__GGB56609_SGB78025| t__SGB78025 2| 976|||||| 94.38814 +> k__Bacteria|p__Proteobacteria|c__Alphaproteobacteria|o__Rhodobacterales|f__Rhodobacteraceae|g__Phycocomes| s__Phycocomes_zhengii|t__SGB31485 2|1224|28211|204455|31989|2873978|2056810| 5.61186 +> ``` +> +> Each line contains 4 columns: +> 1. the lineage with different taxonomic levels, from high level taxa (kingdom: `k__`) to more precise taxa +> 2. the NCBI taxon ids of the lineage taxonomic level +> 3. the relative abundance found for our sample for the lineage +> 4. any additional species +> +> > +> > +> > 1. Which kindgoms have been identified for JC1A? +> > 2. For JP4D? +> > 3. How is the diversity for JP4D compared to **Kraken** results? +> > +> > > +> > > 1. All reads have been unclassified so no kindgom identified +> > > 2. Bacteria, no eukaryotes +> > > 3. Diversity is really reduced for JP4D using **MetaPhlAn**, compared to the one identified with **Kraken** +> > {: .solution} +> {: .question} +> +> - `Predicted taxon relative abundances for Krona`: same information as the previous files but formatted for > visualization using **Krona** +> +> ``` +> Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7 Column 8 Column 9 Column 10 +> 94.38814 Bacteria Bacteroidetes CFGB45935 OFGB45935 FGB45935 GGB56609 GGB56609 SGB78025 +> 5.61186 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Phycocomes Phycocomes zhengii +> 94.38814 Bacteria Bacteroidetes CFGB45935 OFGB45935 FGB45935 GGB56609 GGB56609 SGB78025 SGB78025 +> 5.61186 Bacteria Proteobacteria Alphaproteobacteria Rhodobacterales Rhodobacteraceae Phycocomes Phycocomes zhengii SGB31485 +> ``` +> +> Each line represent an identified taxons with 9 columns: +> - Column 1: The percentage of reads assigned the taxon +> - Column 2-9: The taxon description at the different taxonomic levels from Kindgom to more precise taxa +> +> - A **collection** with the same information as in the tabular file but splitted into different files, one per taxonomic level +> - `BIOM file` with **community profile** in BIOM format, a common and standard format in microbiomics and used as the input for tools like mothur or Qiime. +> +> - `Bowtie2 output` and `SAM file` with the results of the sequence mapping on the reference database +> +> Let's now run **Krona** to visualize the communities +> +> +> > Generate Krona visualisation +> > 1. {% tool [Krona pie chart](toolshed.g2.bx.psu.edu/repos/crs4/taxonomy_krona_chart/taxonomy_krona_chart/2.7.1) %} with the following parameters: +> > - *"Type of input data"*: `tabular` +> > - {% icon param-collection %} *"Input file"*: Krona output of **Metaphlan** +> > +> > 2. Inspect the generated file +> {: .hands_on} +> +> As pointed before, the community looks a lot less diverse than with **Kraken**. It is probably due to the reference database complete enough yet to identify all taxons, or not enough reads in the input data to have marker genes in them. Indeed, no taxon has been identified for JC1A, which contains much less reads than JP4D. **Kraken** is also known to have high number of false positive. +> +{: .details} + +# Conclusion + +In this tutorial, we look how to get the community profile from microbiome data. We apply **Kraken2** to assign taxonomic labels to two microbiome sample datasets. We then visualize the results using **Krona**, **Pavian** and **Phinch** to analyze and compare the datasets. Finally, we discuss important facts when it comes to choosing the right tool for taxonomic assignment. Additionally, we use **MetaPhlAn** on the same datsets and compare the results to **Kraken2**. diff --git a/topics/metagenomics/tutorials/taxonomic-assignment/workflows/index.md b/topics/metagenomics/tutorials/taxonomic-profiling/workflows/index.md similarity index 100% rename from topics/metagenomics/tutorials/taxonomic-assignment/workflows/index.md rename to topics/metagenomics/tutorials/taxonomic-profiling/workflows/index.md diff --git a/topics/metagenomics/tutorials/taxonomic-profiling/workflows/main-workflow-test.yml b/topics/metagenomics/tutorials/taxonomic-profiling/workflows/main-workflow-test.yml new file mode 100644 index 00000000000000..6acbb58a157ef6 --- /dev/null +++ b/topics/metagenomics/tutorials/taxonomic-profiling/workflows/main-workflow-test.yml @@ -0,0 +1,66 @@ +--- +- doc: Test for the workflow + job: + raw-reads: + class: Collection + collection_type: 'list:paired' + elements: + - class: Collection + type: paired + identifier: JP4D + elements: + - identifier: forward + class: File + location: https://zenodo.org/record/7871630/files/JP4D_R1.fastqsanger.gz + filetype: fastqsanger + - identifier: reverse + class: File + location: https://zenodo.org/record/7871630/files/JP4D_R2.fastqsanger.gz + filetype: fastqsanger + - class: Collection + type: paired + identifier: JC1A + elements: + - identifier: forward + class: File + location: https://zenodo.org/record/7871630/files/JC1A_R1.fastqsanger.gz + filetype: fastqsanger + - identifier: reverse + class: File + location: https://zenodo.org/record/7871630/files/JC1A_2.fastqsanger.gz + filetype: fastqsanger + metadata: + class: File + location: metadata.tabular + filetype: tabular + outputs: + kraken_report: + element_tests: + JC1A: + asserts: + has_text: + text: 'Rhodobacterales' + JP4D: + asserts: + has_text: + text: 'Aphaproteobacteria' + krakentool_report: + element_tests: + JC1A: + asserts: + has_text: + text: 'o__Rhodobacterales' + JP4D: + asserts: + has_text: + text: 'o__Rhodobacterales' + 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a/topics/genome-annotation/tutorials/apollo/slides.html +++ b/topics/genome-annotation/tutorials/apollo/slides.html @@ -83,9 +83,9 @@ - A Human finds problems algorithms can't -.pull-left[.image-90[![Schema showing how automated annotation, experimental evidences (cDNAs, HMM domain searches, RNASeq, similarity with other species), and human analysis are used by Apollo to manually curate an annotation](../../images/apollo/apollo_workflow.png)]] +.pull-left[.image-75[![Schema showing how automated annotation, experimental evidences (cDNAs, HMM domain searches, RNASeq, similarity with other species), and human analysis are used by Apollo to manually curate an annotation](../../images/apollo/apollo_workflow.png)]] -.pull-right[.image-40[![Apollo screenshot showing how RNASeq reads align mostly within some exons limits, but not perfectly](../../images/apollo/rnaseq_cov.png)]] +.pull-right[.image-25[![Apollo screenshot showing how RNASeq reads align mostly within some exons limits, but not perfectly](../../images/apollo/rnaseq_cov.png)]] ??? From 1ccc77edefd2eab2f31780b1e525a8658601d89a Mon Sep 17 00:00:00 2001 From: Saskia Hiltemann Date: Wed, 3 May 2023 16:33:23 +0200 Subject: [PATCH 59/66] Update topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md --- topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md b/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md index 0a32f00e3c9e67..ed470427d4255e 100644 --- a/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md @@ -75,7 +75,7 @@ After this theoretical introduction, let's now get hands on analyzing an actual ## Background on data -The dataset we will use for this tutorial comes from an oasis in the mexican desert called Cuatro Ciénegas ({% cite Okie.2020 %}). The researchers were interested in genomic traits that affect the rates and costs of biochemical information processing within cells. They performed a whole-ecosystem experiment, thus fertilizing the pond to achieve nutrient enriched conditions. +The dataset we will use for this tutorial comes from an oasis in the Mexican desert called Cuatro Ciénegas ({% cite Okie.2020 %}). The researchers were interested in genomic traits that affect the rates and costs of biochemical information processing within cells. They performed a whole-ecosystem experiment, thus fertilizing the pond to achieve nutrient enriched conditions. Here we will use 2 datasets: - `JP4D`: a microbiome sample collected from the Lagunita Fertilized Pond From 78c8e98b00620e30e40b4503d9dd69329206d436 Mon Sep 17 00:00:00 2001 From: Saskia Hiltemann Date: Wed, 3 May 2023 16:33:33 +0200 Subject: [PATCH 60/66] Update topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md --- topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md b/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md index ed470427d4255e..dd1c538753a44a 100644 --- a/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-profiling/tutorial.md @@ -268,7 +268,7 @@ For this tutorial, we will use the Standard (archaea, bacteria, viral, plasmid, > {: .question} -sGetting an overview of the assignation is not straightforward with the **Kraken2** outputs directly. We can use visualisation tools for that. +Getting an overview of the assignation is not straightforward with the **Kraken2** outputs directly. We can use visualisation tools for that. A "simple and worthwile addition to Kraken for better abundance estimates" ({% cite Ye.2019 %}) is called __Bracken__ (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species" ({% cite Lu.2017 %}). From 0dcb82455cf086a4422ccbc30311f56532197071 Mon Sep 17 00:00:00 2001 From: Yvan Le Bras Date: Wed, 3 May 2023 18:25:37 +0200 Subject: [PATCH 61/66] Remove duplicate & clean description --- learning-pathways/intro-to-galaxy-and-ecology.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/learning-pathways/intro-to-galaxy-and-ecology.md b/learning-pathways/intro-to-galaxy-and-ecology.md index b1e6565c3da51f..51da0c8ca5b247 100644 --- a/learning-pathways/intro-to-galaxy-and-ecology.md +++ b/learning-pathways/intro-to-galaxy-and-ecology.md @@ -6,8 +6,7 @@ title: Introduction to Galaxy and Ecological data analysis description: | This learning path aims to teach you the basics of Galaxy and analysis of ecological data. You will learn how to use Galaxy for analysis, and will be guided through the common - steps of biodiversity data analysis; download, check and filter GBIF data and analyze abundance data through modeling - sequences. + steps of biodiversity data analysis: download, check, filter and explore biodiversity data and analyze abundance data through modeling. priority: 1 @@ -31,8 +30,7 @@ pathway: topic: ecology - name: gbif_cleaning topic: ecology - - name: PAMPA-toolsuite-tutorial - topic: ecology + - section: "Module 3: Basics of Biodiversity abundance data analysis" description: Working on abundance data, you often want to analyze it through modeling to compute and analyze biodiversity metrics. From 9810a71c795c65d286eb747c1d4d5f885460e1a5 Mon Sep 17 00:00:00 2001 From: Tunc Kayikcioglu <19364610+tuncK@users.noreply.github.com> Date: Thu, 4 May 2023 07:04:07 +0000 Subject: [PATCH 62/66] job.conf warning --- topics/admin/tutorials/ansible-galaxy/tutorial.md | 4 ++-- topics/admin/tutorials/interactive-tools/tutorial.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/topics/admin/tutorials/ansible-galaxy/tutorial.md b/topics/admin/tutorials/ansible-galaxy/tutorial.md index 81d4a42e59a009..95c4b0b7e8ff3d 100644 --- a/topics/admin/tutorials/ansible-galaxy/tutorial.md +++ b/topics/admin/tutorials/ansible-galaxy/tutorial.md @@ -1779,7 +1779,7 @@ Galaxy is now configured with an admin user, a database, and a place to store da > > Mar 16 01:15:15 gat systemd[1]: galaxy-gunicorn.service: Consumed 3.381s CPU time. > > ``` > > -> > Check your /srv/galaxy/config/galaxy.yml and ensure that it lines up exactly with what you expect. +> > Check your /srv/galaxy/config/galaxy.yml and ensure that it lines up exactly with what you expect. You might observe a warning that `Dynamic handlers are configured in Gravity but Galaxy is not configured to assign jobs to handlers dynamically`. We will address this [below](#job-configuration), and you can disregard it for now. > {: .tip} > > 6. Some things to note: @@ -1814,7 +1814,7 @@ With this we have: - PostgreSQL running - Galaxy running (managed by Gravity + systemd) -Although Gunicorn can server HTTP for us directly, a reverse proxy in front of Gunicorn can automatically compress selected content, and we can easily apply caching headers to specific types of content like CSS or images. It is also necessary if we want to serve multiple sites at once, e.g. with a group website at `/` and Galaxy at `/galaxy`. Lastly, it can provide authentication as well, as noted in the [External Authentication]({{ site.baseurl }}/topics/admin/tutorials/external-auth/tutorial.html) tutorial. +Although Gunicorn can serve HTTP for us directly, a reverse proxy in front of Gunicorn can automatically compress selected content, and we can easily apply caching headers to specific types of content like CSS or images. It is also necessary if we want to serve multiple sites at once, e.g. with a group website at `/` and Galaxy at `/galaxy`. Lastly, it can provide authentication as well, as noted in the [External Authentication]({{ site.baseurl }}/topics/admin/tutorials/external-auth/tutorial.html) tutorial. For this, we will use NGINX (pronounced "engine X" /ˌɛndʒɪnˈɛks/ EN-jin-EKS). It is possible to configure Galaxy with Apache and potentially other webservers but this is not the configuration that receives the most testing. We recommend NGINX unless you have a specific need for Apache. diff --git a/topics/admin/tutorials/interactive-tools/tutorial.md b/topics/admin/tutorials/interactive-tools/tutorial.md index 6d7bd23bbad8ba..ed6eb2518d62f7 100644 --- a/topics/admin/tutorials/interactive-tools/tutorial.md +++ b/topics/admin/tutorials/interactive-tools/tutorial.md @@ -409,7 +409,7 @@ As we use Let's Encrypt in staging mode, the wildcard certificates generated wit > #certbot_auth_method: --webroot > ``` > -> Although this is not explicitly required (setting `cerbot_dns_provider` as we do overrides this setting), doing so is less confusing in the future, since it makes it clear that the "webroot" method for Let's Encrypt WEB-01 challenges is no longer in use for this server. +> Although this is not explicitly required (setting `certbot_dns_provider` as we do overrides this setting), doing so is less confusing in the future, since it makes it clear that the "webroot" method for Let's Encrypt WEB-01 challenges is no longer in use for this server. > > - Add the following lines to your `group_vars/galaxyservers.yml` file: > @@ -586,7 +586,7 @@ A few Interactive Tool wrappers are provided with Galaxy, but they are [commente > > ``` > -> 2. We need to modify `job_conf.xml` to instruct Galaxy on how run Interactive Tools (and specifically, how to run them in Docker). We will begin with a basic job conf: +> 2. We need to modify `job_conf.xml` to instruct Galaxy on how to run Interactive Tools (and specifically, how to run them in Docker). We will begin with a basic job conf: > > Create `templates/galaxy/config/job_conf.xml.j2` with the following contents: > From 87f15edef2e90cb35c86a72afa7d3a258d2905e7 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Thu, 4 May 2023 11:18:04 +0100 Subject: [PATCH 63/66] Update faqs/galaxy/interactive_tools_jupyter_launch.md Co-authored-by: Saskia Hiltemann --- faqs/galaxy/interactive_tools_jupyter_launch.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/faqs/galaxy/interactive_tools_jupyter_launch.md b/faqs/galaxy/interactive_tools_jupyter_launch.md index ec3d1bab59b99d..46c7b5994d895a 100644 --- a/faqs/galaxy/interactive_tools_jupyter_launch.md +++ b/faqs/galaxy/interactive_tools_jupyter_launch.md @@ -11,7 +11,7 @@ contributors: [annefou,shiltemann,nomadscientist] > > > Run JupyterLab > > -> > 1. {% tool [Interactive Jupyter Notebook](interactive_tool_jupyter_notebook) %}. Note that on some instances this is called {% tool [Interactive JupyTool and notebook](interactive_tool_jupyter_notebook) %}: +> > 1. {% tool [Interactive Jupyter Notebook](interactive_tool_jupyter_notebook) %}. Note that on some Galaxies this is called {% tool [Interactive JupyTool and notebook](interactive_tool_jupyter_notebook) %}: > > 2. Click Run Tool > > 3. The tool will start running and will stay running permanently > > From fd2a5f8bf5b369d3c2972048ff64e9ba1358c4a7 Mon Sep 17 00:00:00 2001 From: Wendi Bacon <44605769+nomadscientist@users.noreply.github.com> Date: Thu, 4 May 2023 11:18:21 +0100 Subject: [PATCH 64/66] Update topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md Co-authored-by: Saskia Hiltemann --- .../tutorials/scrna-case_monocle3-rstudio/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md index 35a93ec59f6aa3..63f632f2ad0832 100644 --- a/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_monocle3-rstudio/tutorial.md @@ -97,7 +97,7 @@ gene_metadata <- ann$var > Uploading files from your computer is also an option! > -> If you already have files containing the expression matrix, genes and cells metadata, you can upload them to JupyLab and generate a cds file from them instaed. For example, if you first downloaded the files from Galaxy, your files will have the `.tabular` extension. In this case, we will use the `read.delim()` function to read them in. In this function, the first argument is the file path - in our case, the files are in the same folder as the notebook, so the file path is the same as the file name. You can always check that by right-clicking on the file and choosing `Copy path`. The second argument, `row.names=1` takes the column number of the data file from which to take the row names. +> If you already have files containing the expression matrix, genes and cells metadata, you can upload them to JupyLab and generate a cds file from them instead. For example, if you first downloaded the files from Galaxy, your files will have the `.tabular` extension. In this case, we will use the `read.delim()` function to read them in. In this function, the first argument is the file path - in our case, the files are in the same folder as the notebook, so the file path is the same as the file name. You can always check that by right-clicking on the file and choosing `Copy path`. The second argument, `row.names=1` takes the column number of the data file from which to take the row names. > ```r > # read in the files > cell_metadata <- read.delim('cells.tabular', row.names=1) From 0c9ec2a1f5b3f94c2dd255b87997ffda41ef142c Mon Sep 17 00:00:00 2001 From: Tunc Kayikcioglu <19364610+tuncK@users.noreply.github.com> Date: Thu, 4 May 2023 12:52:29 +0000 Subject: [PATCH 65/66] GxIT hiccups --- topics/admin/tutorials/interactive-tools/tutorial.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/topics/admin/tutorials/interactive-tools/tutorial.md b/topics/admin/tutorials/interactive-tools/tutorial.md index ed6eb2518d62f7..80cd0a31ad95a5 100644 --- a/topics/admin/tutorials/interactive-tools/tutorial.md +++ b/topics/admin/tutorials/interactive-tools/tutorial.md @@ -112,7 +112,7 @@ We will use several Ansible roles for this tutorial. In order to avoid repetetiv > > ```yaml > - src: geerlingguy.docker -> version: 2.6.0 +> version: 6.1.0 > - src: usegalaxy_eu.gie_proxy > version: 0.0.2 > ``` @@ -644,7 +644,7 @@ A few Interactive Tool wrappers are provided with Galaxy, but they are [commente > ``` > {% endraw %} > -> Next, inform `galaxyproject.galaxy` of where you would like the `job_conf.xml` to reside, that GxITs should be enabled, and where the GxIT map database can be found: +> Next, inform `galaxyproject.galaxy` of where you would like the `job_conf.xml` to reside, that GxITs should be enabled, and where the GxIT map database can be found. Watch for other conflicting configurations from previous tutorials (e.g. `job_config: ...`): > > {% raw %} > ```yaml From 1b4d05a867f494fedb15a0ab4595429f06b83531 Mon Sep 17 00:00:00 2001 From: Tunc Kayikcioglu <19364610+tuncK@users.noreply.github.com> Date: Thu, 4 May 2023 13:20:25 +0000 Subject: [PATCH 66/66] typo in ansible-galaxy --- topics/admin/tutorials/interactive-tools/tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/topics/admin/tutorials/interactive-tools/tutorial.md b/topics/admin/tutorials/interactive-tools/tutorial.md index 80cd0a31ad95a5..001ad515ecdfe4 100644 --- a/topics/admin/tutorials/interactive-tools/tutorial.md +++ b/topics/admin/tutorials/interactive-tools/tutorial.md @@ -515,7 +515,7 @@ As we use Let's Encrypt in staging mode, the wildcard certificates generated wit > #certbot_auth_method: --webroot > ``` > -> Although this is not explicitly required (setting `cerbot_dns_provider` as we do overrides this setting), doing so is less confusing in the future, since it makes it clear that the "webroot" method for Let's Encrypt WEB-01 challenges is no longer in use for this server. +> Although this is not explicitly required (setting `certbot_dns_provider` as we do overrides this setting), doing so is less confusing in the future, since it makes it clear that the "webroot" method for Let's Encrypt WEB-01 challenges is no longer in use for this server. > > - Add the following lines to your `group_vars/galaxyservers.yml` file: >

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c20e5868e6b33d..2cdad9e66be257 100644 --- a/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md +++ b/topics/metagenomics/tutorials/taxonomic-assignment/tutorial.md @@ -181,9 +181,9 @@ As both files contain a lot of information, we will use __Krona__ {% cite Ondov. # Hands on: estimating species abundance -A "simple and worthwile addition to Kraken for better abundance estimates" {% cite Ye.2019 %} is called Bracken (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species." {% cite Lu.2017 %} +A "simple and worthwile addition to Kraken for better abundance estimates" {% cite Ye.2019 %} is called __Bracken__ (Bayesian Reestimation of Abundance after Classification with Kraken). Instead of only using proportions of classified reads, it takes a probabilistic approach to generate final abundance profiles. It works by re-distributing reads in the taxonomic tree: "Reads assigned to nodes above the species level are distributed down to the species nodes, while reads assigned at the strain level are re-distributed upward to their parent species." {% cite Lu.2017 %} -> ### {% icon hands_on %} Hands-on: estimate species abundance with Bracken xxx +> ### {% icon hands_on %} Hands-on: estimate species abundance with Bracken (Name in Galaxy: Estimate Abundance at Taxonomic Level) > > 1. {% tool [Bracken](https://toolshed.g2.bx.psu.edu/view/iuc/bracken/79450f7fd718) %} with the following parameters: - *"Kraken report file"*: Report: Kraken2 on dataset collection (! do not use the classification file) From eeb310ca4a55d437de3b7cfafa2ca7f92636c6a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?B=C3=A9r=C3=A9nice=20Batut?= Date: Thu, 16 Mar 2023 13:51:25 +0100 Subject: [PATCH 27/66] Work on tutorial --- .../images}/kmers-kraken.jpg | Bin topics/metagenomics/faqs/kraken.md | 13 + topics/metagenomics/faqs/taxon.md | 2 + .../tutorials/beer-data-analysis/tutorial.md | 10 +- .../tutorial.md | 20 +- .../Kraken2_classification_screenshot.png | Bin 15767 -> 0 bytes .../images/Kraken2_report_screenshot.png | Bin 12973 -> 0 bytes .../images/Kraken_algorithm.png | Bin 93780 -> 0 bytes .../images/krona-kraken.png | Bin 0 -> 257356 bytes .../pavian-kraken-comparison-domain.png | Bin 0 -> 26199 bytes .../images/pavian-kraken-proteobacteria.png | Bin 0 -> 12197 bytes .../images/pavian-kraken-results-overview.png | Bin 0 -> 38330 bytes .../images/pavian-kraken-sankey-JC1A.png | Bin 0 -> 101861 bytes .../images/pavian-kraken-sankey-JP4D.png | Bin 0 -> 80726 bytes .../tutorials/taxonomic-assignment/images/tmp | 1 - .../taxonomic-assignment/krona-kraken.html | 11137 ++++++++++++++++ .../taxonomic-assignment/tutorial.bib | 93 +- .../taxonomic-assignment/tutorial.md | 873 +- 18 files changed, 11801 insertions(+), 348 deletions(-) rename topics/metagenomics/{images/metagenomics-nanopore => faqs/images}/kmers-kraken.jpg (100%) create mode 100644 topics/metagenomics/faqs/kraken.md delete mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/Kraken2_classification_screenshot.png delete mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/Kraken2_report_screenshot.png delete mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/Kraken_algorithm.png create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/krona-kraken.png create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/pavian-kraken-comparison-domain.png create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/pavian-kraken-proteobacteria.png create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/pavian-kraken-results-overview.png create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/pavian-kraken-sankey-JC1A.png create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/pavian-kraken-sankey-JP4D.png delete mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/images/tmp create mode 100644 topics/metagenomics/tutorials/taxonomic-assignment/krona-kraken.html diff --git a/topics/metagenomics/images/metagenomics-nanopore/kmers-kraken.jpg b/topics/metagenomics/faqs/images/kmers-kraken.jpg similarity index 100% rename from topics/metagenomics/images/metagenomics-nanopore/kmers-kraken.jpg rename to topics/metagenomics/faqs/images/kmers-kraken.jpg diff --git a/topics/metagenomics/faqs/kraken.md b/topics/metagenomics/faqs/kraken.md new file mode 100644 index 00000000000000..1c4426a00626e6 --- /dev/null +++ b/topics/metagenomics/faqs/kraken.md @@ -0,0 +1,13 @@ +--- +title: Kraken2 and the k-mer approach for taxonomy classification +area: format +box_type: details +layout: faq +contributors: [bebatut] +--- + +In the $$k$$-mer approach for taxonomy classification, we use a database containing DNA sequences of genomes whose taxonomy we already know. On a computer, the genome sequences are broken into short pieces of length $$k$$ (called $$k$$-mers), usually 30bp. + +**Kraken** examines the $$k$$-mers within the query sequence, searches for them in the database, looks for where these are placed within the taxonomy tree inside the database, makes the classification with the most probable position, then maps $$k$$-mers to the lowest common ancestor (LCA) of all genomes known to contain the given $$k$$-mer. + +![Kraken2](./images/kmers-kraken.jpg "Kraken sequence classification algorithm. To classify a sequence, each k-mer in the sequence is mapped to the lowest common ancestor (LCA, i.e. the lowest node) of the genomes that contain that k-mer in the database. The taxa associated with the sequence's k-mers, as well as the taxa's ancestors, form a pruned subtree of the general taxonomy tree, which is used for classification. In the classification tree, each node has a weight equal to the number of k-mers in the sequence associated with the node's taxon. Each root-to-leaf (RTL) path in the classification tree is scored by adding all weights in the path, and the maximal RTL path in the classification tree is the classification path (nodes highlighted in yellow). The leaf of this classification path (the orange, leftmost leaf in the classification tree) is the classification used for the query sequence. Source: {% cite Wood2014 %}") diff --git a/topics/metagenomics/faqs/taxon.md b/topics/metagenomics/faqs/taxon.md index 4261bbf11da095..4fa07cfe6ee931 100644 --- a/topics/metagenomics/faqs/taxon.md +++ b/topics/metagenomics/faqs/taxon.md @@ -29,6 +29,8 @@ Family | Felidae Genus | *Felis* Species | *F. catus* +From this classification, one can generate a tree of life, also known as a phylogenetic tree. It is a rooted tree that describes the relationship of all life on earth. At the root sits the "last universal common ancestor" and the three main branches (in taxonomy also called domains) are bacteria, archaea and eukaryotes. Most important for this is the idea that all life on earth is derived from a common ancestor and therefore when comparing two species, you will -sooner or later- find a common ancestor for all of them. + Let's explore taxonomy in the Tree of Life, using [Lifemap](https://lifemap.univ-lyon1.fr/) diff --git a/topics/metagenomics/tutorials/beer-data-analysis/tutorial.md b/topics/metagenomics/tutorials/beer-data-analysis/tutorial.md index 11274ae937cc34..5958410db38410 100644 --- a/topics/metagenomics/tutorials/beer-data-analysis/tutorial.md +++ b/topics/metagenomics/tutorials/beer-data-analysis/tutorial.md @@ -317,15 +317,7 @@ One of the main aims in microbiome data analysis is to identify the organisms se Taxonomic assignment or classification is the process of assigning an **Operational Taxonomic Unit** (OTUs, that is, groups of related individuals / taxon) to sequences. To assign an OTU to a sequence it is compared against a database, but this comparison can be done in different ways, with different bioinformatics tools. Here we will use **Kraken2** ({% cite wood2019improved %}). -> Kraken2 and the k-mer approach for taxonomy classification -> -> In the $$k$$-mer approach for taxonomy classification, we use a database containing DNA sequences of genomes whose taxonomy we already know. On a computer, the genome sequences are broken into short pieces of length $$k$$ (called $$k$$-mers), usually 30bp. -> -> **Kraken** examines the $$k$$-mers within the query sequence, searches for them in the database, looks for where these are placed within the taxonomy tree inside the database, makes the classification with the most probable position, then maps $$k$$-mers to the lowest common ancestor (LCA) of all genomes known to contain the given $$k$$-mer. -> -> ![Kraken2](../../images/metagenomics-nanopore/kmers-kraken.jpg "Kraken sequence classification algorithm. To classify a sequence, each k-mer in the sequence is mapped to the lowest common ancestor (LCA, i.e. the lowest node) of the genomes that contain that k-mer in the database. The taxa associated with the sequence's k-mers, as well as the taxa's ancestors, form a pruned subtree of the general taxonomy tree, which is used for classification. In the classification tree, each node has a weight equal to the number of k-mers in the sequence associated with the node's taxon. Each root-to-leaf (RTL) path in the classification tree is scored by adding all weights in the path, and the maximal RTL path in the classification tree is the classification path (nodes highlighted in yellow). The leaf of this classification path (the orange, leftmost leaf in the classification tree) is the classification used for the query sequence. Source: {% cite Wood2014 %}") -> -{: .details} +{% snippet topics/metagenomics/faqs/kraken.md %} > Kraken2 > diff --git a/topics/metagenomics/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.md b/topics/metagenomics/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.md index 15d13acfa53b6f..ac016adeb48426 100644 --- a/topics/metagenomics/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.md +++ b/topics/metagenomics/tutorials/pathogen-detection-from-nanopore-foodborne-data/tutorial.md @@ -376,15 +376,7 @@ In this section we would like to identify the different organisms found in our s In the previous section we ran **Kraken2** along with the **Kalamari** database, which is also a kind of taxonomy profiling but the database used is designed to include all possible host sequences. In the following part, we run **Kraken2** again; but this time with one of its built-in databases, **Standard PlusPF**, which can give us more insight into pathogen candidate species than **Kalamari**. You can test this yourself by comparing reports of both **Kraken2** runs. -> Kraken2 and the k-mer approach for taxonomy classification -> -> In the $$k$$-mer approach for taxonomy classification, we use a database containing DNA sequences of genomes whose taxonomy we already know. On a computer, the genome sequences are broken into short pieces of length $$k$$ (called $$k$$-mers), usually 30bp. -> -> **Kraken** examines the $$k$$-mers within the query sequence, searches for them in the database, looks for where these are placed within the taxonomy tree inside the database, makes the classification with the most probable position, then maps $$k$$-mers to the lowest common ancestor (LCA) of all genomes known to contain the given $$k$$-mer. -> -> ![Kraken2](../../images/metagenomics-nanopore/kmers-kraken.jpg "Kraken sequence classification algorithm. To classify a sequence, each k-mer in the sequence is mapped to the lowest common ancestor (LCA, i.e. the lowest node) of the genomes that contain that k-mer in the database. The taxa associated with the sequence's k-mers, as well as the taxa's ancestors, form a pruned subtree of the general taxonomy tree, which is used for classification. In the classification tree, each node has a weight equal to the number of k-mers in the sequence associated with the node's taxon. Each root-to-leaf (RTL) path in the classification tree is scored by adding all weights in the path, and the maximal RTL path in the classification tree is the classification path (nodes highlighted in yellow). The leaf of this classification path (the orange, leftmost leaf in the classification tree) is the classification used for the query sequence. Source: {% cite Wood2014 %}") -> -{: .details} +{% snippet topics/metagenomics/faqs/kraken.md %}