diff --git a/bin/lint.rb b/bin/lint.rb index de185cfb448672..8daf567831918e 100755 --- a/bin/lint.rb +++ b/bin/lint.rb @@ -151,16 +151,17 @@ def self.youtube_bad(contents) def self.link_gtn_tutorial_external(contents) find_matching_texts( contents, - %r{\((https?://(training.galaxyproject.org|galaxyproject.github.io)/training-material/[^)]*)\)} + %r{\(https?://(training.galaxyproject.org|galaxyproject.github.io)/training-material/([^)]*)\)} ) .map do |idx, _text, selected| + # puts "#{idx} 0 #{selected[0]} 1 #{selected[1]} 2 #{selected[2]} 3 #{selected[3]}" ReviewDogEmitter.error( path: @path, idx: idx, # We wrap the entire URL (inside the explicit () in a matching group to make it easy to select/replace) - match_start: selected.begin(1), - match_end: selected.end(1) + 1, - replacement: "{% link #{selected[3]}.md %}", + match_start: selected.begin(0) + 1, + match_end: selected.end(0), + replacement: "{% link #{selected[2].gsub('.html', '.md')} %}", message: 'Please use the link function to link to other pages within the GTN. ' \ 'It helps us ensure that all links are correct', code: 'GTN:003' @@ -908,7 +909,7 @@ def self.format_reviewdog_output(message) # puts "#{original[0..start_coln - 2]} + #{repl} + #{original[end_coln-1..-1]}" fixed = original[0..start_coln - 2] + repl + original[end_coln - 1..] - warn "Fixing #{original} to #{fixed}" + warn "DIFF\n-#{original}\n+#{fixed}" lines[start_line - 1] = fixed # Save our changes diff --git a/faqs/galaxy/account_reduce_quota_usage.md b/faqs/galaxy/account_reduce_quota_usage.md index 3ff1ac4c30ce9e..febc6dcd0fb865 100644 --- a/faqs/galaxy/account_reduce_quota_usage.md +++ b/faqs/galaxy/account_reduce_quota_usage.md @@ -6,10 +6,10 @@ layout: faq box_type: tip contributors: [jennaj, bernandez] --- -* [**Download**](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_download_datasets.html) Datasets as individual files or entire Histories as an archive. Then *purge* them from the public server. -* **Transfer/Move** [Datasets](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_moving_datasets_between_galaxy_servers.html) or [Histories](https://training.galaxyproject.org/training-material/faqs/galaxy/histories_transfer_entire_histories_from_one_galaxy_server_to_another.html) to another Galaxy server, including your own Galaxy. Then purge. -* [**Copy**](https://training.galaxyproject.org/training-material/faqs/galaxy/histories_copy_dataset.html) your most important Datasets into a new/other History (inputs, results), then purge the original full History. -* [**Extract**](https://training.galaxyproject.org/training-material/faqs/galaxy/workflows_extract_from_history.html) a **Workflow** from the History, then purge it. +* [**Download**]({% link faqs/galaxy/datasets_download_datasets.md %}) Datasets as individual files or entire Histories as an archive. Then *purge* them from the public server. +* **Transfer/Move** [Datasets]({% link faqs/galaxy/datasets_moving_datasets_between_galaxy_servers.md %}) or [Histories]({% link faqs/galaxy/histories_transfer_entire_histories_from_one_galaxy_server_to_another.md %}) to another Galaxy server, including your own Galaxy. Then purge. +* [**Copy**]({% link faqs/galaxy/histories_copy_dataset.md %}) your most important Datasets into a new/other History (inputs, results), then purge the original full History. +* [**Extract**]({% link faqs/galaxy/workflows_extract_from_history.md %}) a **Workflow** from the History, then purge it. * **Back-up your work**. It is a best practice to download an archive of your FULL original Histories periodically, even those still in use, as a backup. **Resources** Much discussion about all of the above options can be found at the [Galaxy Help forum](https://help.galaxyproject.org/). diff --git a/faqs/galaxy/analysis_reporting_issues.md b/faqs/galaxy/analysis_reporting_issues.md index 80d404743f691d..59f0b78840e9d4 100644 --- a/faqs/galaxy/analysis_reporting_issues.md +++ b/faqs/galaxy/analysis_reporting_issues.md @@ -8,17 +8,17 @@ contributors: [jennaj, garimavs] - For reporting **Usage Problems**, related to tools and functions, head to the [Galaxy Help](https://help.galaxyproject.org/) site. - **Red Error Datasets**: - - Refer to the [Troubleshooting errors](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_troubleshooting.html) FAQ for red error in datasets. + - Refer to the [Troubleshooting errors]({% link faqs/galaxy/analysis_troubleshooting.md %}) FAQ for red error in datasets. - **Unexpected results in Green Success Dataset**: - - To resolve it you may be asked to send in a shared history link and possibly a shared workflow link. For sharing your history, refer to this [link](https://training.galaxyproject.org/training-material/faqs/galaxy/histories_sharing.html). - - To reach our support team, visit [Support FAQs](https://training.galaxyproject.org/training-material/faqs/galaxy/#support). + - To resolve it you may be asked to send in a shared history link and possibly a shared workflow link. For sharing your history, refer to this [these instructions]({% link faqs/galaxy/histories_sharing.md %}). + - To reach our support team, visit [Support FAQs]({% link faqs/galaxy/ %}#support). - **Functionality problems**: - Using [Galaxy Help](https://help.galaxyproject.org/) is the best way to get help in most cases. - - If the problem is more complex, email a description of the problem and how to reproduce it. + - If the problem is more complex, email a description of the problem and how to reproduce it. - **Administrative problems**: - - If the problem is present in your own Galaxy, the administrative configuration may be a factor. - - For the fastest help directly from the development community, admin issues can be alternatively reported to the mailing list or the GalaxyProject [Gitter channel](https://gitter.im/galaxyproject/Lobby). + - If the problem is present in your own Galaxy, the administrative configuration may be a factor. + - For the fastest help directly from the development community, admin issues can be alternatively reported to the mailing list or the GalaxyProject [Gitter channel](https://gitter.im/galaxyproject/Lobby). - For **Security Issues**, do not report them via GitHub. Kindly disclose these as explained in this [document](https://github.com/galaxyproject/galaxy/blob/dev/SECURITY_POLICY.md). -- For **Bug Reporting**, create a Github issue. Include the steps mentioned [here](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_troubleshooting_reporting.html). +- For **Bug Reporting**, create a Github issue. Include the steps mentioned [in these instructions]({% link faqs/galaxy/analysis_troubleshooting_reporting.md %}). - Search the [GTN Search](https://training.galaxyproject.org/search) to find prior Q & A, FAQs, tutorials, and other documentation across all Galaxy resources, to verify in case your issue was already faced by someone. diff --git a/faqs/galaxy/datasets_chromosome_identifiers.md b/faqs/galaxy/datasets_chromosome_identifiers.md index 066771000e7501..6658e872f332a8 100644 --- a/faqs/galaxy/datasets_chromosome_identifiers.md +++ b/faqs/galaxy/datasets_chromosome_identifiers.md @@ -11,9 +11,9 @@ contributors: [jennaj, Melkeb] - **If using a Custom Reference genome**, the methods below also apply, but the first step is to make certain that the Custom Genome is formatted correctly. Improper formatting is the most common root cause of CG related errors. -Method 1: [Finding BAM dataset identifiers](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_BAM_dataset_identifiers.html) +Method 1: [Finding BAM dataset identifiers]({% link faqs/galaxy/datasets_BAM_dataset_identifiers.md %}) -Method 2: [Directly obtaining UCSC sourced *genome* identifiers](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_UCSC_sourced_genome.html) +Method 2: [Directly obtaining UCSC sourced *genome* identifiers]({% link faqs/galaxy/datasets_UCSC_sourced_genome.md %}) Method 3: [Adjusting identifiers for UCSC sourced data used with other sourced data](https://galaxyproject.org/support/chrom-identifiers/#adjusting-identifiers-or-input-source) diff --git a/faqs/galaxy/datasets_download_using_command_line.md b/faqs/galaxy/datasets_download_using_command_line.md index e22e40d05619e5..9032edbdc56f10 100644 --- a/faqs/galaxy/datasets_download_using_command_line.md +++ b/faqs/galaxy/datasets_download_using_command_line.md @@ -20,7 +20,7 @@ From the terminal window on your computer, you can use **wget** or **curl**. > **``curl -o outfile '' `` > ``curl -o outfile --insecure '' # ignore SSL certificate warnings`` > ``curl -C - -o outfile '' # continue an interrupted download``** -4. For dataset collections and datasets within collections you have to supply your [API key](https://training.galaxyproject.org/training-material/faqs/galaxy/preferences_admin_api_key.html) with the request +4. For dataset collections and datasets within collections you have to supply your [API key]({% link faqs/galaxy/preferences_admin_api_key.md %}) with the request - Sample commands for **wget** and **curl** respectively are: > > **``wget https://usegalaxy.org/api/dataset_collections/d20ad3e1ccd4595de/download?key=MYSECRETAPIKEY``** diff --git a/faqs/galaxy/datasets_hidden.md b/faqs/galaxy/datasets_hidden.md index 223d9f49581a40..f47cf47a07cb99 100644 --- a/faqs/galaxy/datasets_hidden.md +++ b/faqs/galaxy/datasets_hidden.md @@ -10,6 +10,6 @@ If you have run a workflow with hidden datasets, in your History: - Click the **gear icon** {% icon galaxy-gear %} → Click **Unhide Hidden Datasets** - Or use the toggle ``hidden`` to view them -When using the [Copy Datasets](https://training.galaxyproject.org/training-material/faqs/galaxy/histories_copy_dataset.html) feature, hidden datasets will not be available to transfer from the **Source History** list of datasets. To include them: +When using the [Copy Datasets]({% link faqs/galaxy/histories_copy_dataset.md %}) feature, hidden datasets will not be available to transfer from the **Source History** list of datasets. To include them: 1. Click the **gear icon** {% icon galaxy-gear %} → Click **Unhide Hidden Datasets** 2. Click the **gear icon** {% icon galaxy-gear %} → Click **Copy Datasets** diff --git a/faqs/galaxy/datasets_quotas_for_datasets_and_histories.md b/faqs/galaxy/datasets_quotas_for_datasets_and_histories.md index b76994de69cac5..328629478290f0 100644 --- a/faqs/galaxy/datasets_quotas_for_datasets_and_histories.md +++ b/faqs/galaxy/datasets_quotas_for_datasets_and_histories.md @@ -10,4 +10,4 @@ contributors: [jennaj, garimavs] - _Permanently deleted datasets_ and _permanently deleted histories_ containing datasets **are not considered**. - _Histories/datasets that are shared with you_ are only **partially considered** unless you import them. -**Note:** To reduce quota usage, refer to [How can I reduce quota usage while still retaining prior work (data, tools, methods)?](https://training.galaxyproject.org/training-material/faqs/galaxy/reduce_quota_usage.html) FAQ. \ No newline at end of file +**Note:** To reduce quota usage, refer to [How can I reduce quota usage while still retaining prior work (data, tools, methods)?]({% link faqs/galaxy/account_reduce_quota_usage.md %}) FAQ. diff --git a/faqs/galaxy/datasets_working_with_fasta.md b/faqs/galaxy/datasets_working_with_fasta.md index a73040cf2ec52e..cc169791e40b07 100644 --- a/faqs/galaxy/datasets_working_with_fasta.md +++ b/faqs/galaxy/datasets_working_with_fasta.md @@ -14,11 +14,11 @@ contributors: [jennaj, garimavs] - Formatting criteria: - All sequence identifiers must be unique. - Some tools will require that there is no description line content, only identifiers, in the fasta title line (“>” line). Use **NormalizeFasta** to remove the description (all content after the first whitespace) and wrap the sequences to 80 bases. -- [Custom genome](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_add_custom_build.html), transcriptome exome? +- [Custom genome]({% link faqs/galaxy/analysis_add_custom_build.md %}), transcriptome exome? - Only appropriate for smaller genomes (bacterial, viral, most insects). - Not appropriate for any mammalian genomes, or some plants/fungi. - Sequence identifiers must be an exact match with all other inputs or expect problems. See **GFF GFT GFF3**. - Formatting criteria: - All sequence identifiers must be unique. - ALL tools will require that there is no description content, only identifiers, in the fasta title line (“>” line). Use **NormalizeFasta** to remove the description (all content after the first whitespace) and wrap the sequences to 80 bases. - - The only exception is when executing the **MakeBLASTdb** tool and when the input fasta is in NCBI BLAST format (see the tool form). \ No newline at end of file + - The only exception is when executing the **MakeBLASTdb** tool and when the input fasta is in NCBI BLAST format (see the tool form). diff --git a/faqs/galaxy/datasets_working_with_reference_annotation.md b/faqs/galaxy/datasets_working_with_reference_annotation.md index 3fc74a2fda492d..d52e96512d403b 100644 --- a/faqs/galaxy/datasets_working_with_reference_annotation.md +++ b/faqs/galaxy/datasets_working_with_reference_annotation.md @@ -17,7 +17,7 @@ contributors: [jennaj, garimavs] - If only GFF3 is available, you can attempt to transform it with the tool **gffread**. - Was GTF data detected as GFF during Upload? It probably has headers. -Remove the headers (lines that start with a "#") with the **Select** tool using the option "NOT Matching" with the regular expression: ^# - - [Redetect the datatype](https://training.galaxyproject.org/training-material/faqs/galaxy/#detecting-the-datatype-file-format). It should be GTF once corrected. + - [Redetect the datatype]({% link faqs/galaxy/datasets_detect_datatype.md %}). It should be GTF once corrected. - UCSC annotation - Find annotation under their Downloads area. The path will be similar to: `https://hgdownload.soe.ucsc.edu/goldenPath//bigZips/genes/` - Copy the URL from UCSC and paste it into the Upload tool, allowing Galaxy to detect the datatype. diff --git a/faqs/galaxy/datatypes_fastq_and_fastqsanger.md b/faqs/galaxy/datatypes_fastq_and_fastqsanger.md index f32752851bcd63..239e2273304c22 100644 --- a/faqs/galaxy/datatypes_fastq_and_fastqsanger.md +++ b/faqs/galaxy/datatypes_fastq_and_fastqsanger.md @@ -11,4 +11,4 @@ Before assigning `fastqsanger` or `fastqsanger.gz`, be sure to confirm the forma **TIP:** - Using *non-fastqsanger* scaled quality values will cause scientific problems with tools that expected `fastqsanger` formatted input. - Even if the tool does not fail, get the format right from the start to avoid problems. Incorrect format is still one of the most common reasons for tool errors or unexpected results (within Galaxy or not). -- For more information on [How to format fastq data for tools that require .fastqsanger format?](https://training.galaxyproject.org/training-material/faqs/galaxy/datatypes_fastq_format.html) +- For more information on [How to format fastq data for tools that require .fastqsanger format?]({% link faqs/galaxy/datatypes_fastq_format.md %}) diff --git a/faqs/galaxy/datatypes_understanding_datatypes.md b/faqs/galaxy/datatypes_understanding_datatypes.md index 626134c2aff237..44729e64b6a3b4 100644 --- a/faqs/galaxy/datatypes_understanding_datatypes.md +++ b/faqs/galaxy/datatypes_understanding_datatypes.md @@ -8,8 +8,8 @@ contributors: [jennaj, garimavs] - Allow Galaxy to detect the datatype during Upload, and adjust from there if needed. - Tool forms will filter for the appropriate datatypes it can use for each input. -- [Directly changing](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_change_datatype.html) a datatype can lead to errors. Be intentional and consider [converting](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_convert_datatype.html) instead when possible. -- Dataset content can also be adjusted (tools: Data manipulation) and the expected [datatype detected](https://training.galaxyproject.org/training-material/faqs/galaxy/#detecting-the-datatype-file-format). Detected datatypes are the most reliable in most cases. +- [Directly changing]({% link faqs/galaxy/datasets_change_datatype.md %}) a datatype can lead to errors. Be intentional and consider [converting]({% link faqs/galaxy/datasets_convert_datatype.md %}) instead when possible. +- Dataset content can also be adjusted (tools: Data manipulation) and the expected [datatype detected]({% link faqs/galaxy/datasets_detect_datatype.md %}). Detected datatypes are the most reliable in most cases. - If a tool does not accept a dataset as valid input, it is not in the correct format with the correct datatype. - Once a dataset’s content matches the datatype, and that dataset is repeatedly used (example: Reference annotation) use that same dataset for all steps in an analysis or expect problems. This may mean rerunning prior tools if you need to make a correction. - Tip: Not sure what datatypes a tool is expecting for an input? @@ -21,5 +21,5 @@ contributors: [jennaj, garimavs] - Why? This can also unexpectedly create hidden datasets that are near duplicates of your original data, only in a different format. - For large data, that can quickly consume working space (quota). - Deleting/purging any hidden datasets can lead to errors if you are still using the original datasets as an input. - - Consider [converting](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_convert_datatype.html) to the expected datatype yourself when data is large. - - Then test the tool directly on converted data. If it works, purge the original to recover space. \ No newline at end of file + - Consider [converting]({% link faqs/galaxy/datasets_convert_datatype.md %}) to the expected datatype yourself when data is large. + - Then test the tool directly on converted data. If it works, purge the original to recover space. diff --git a/faqs/galaxy/histories_shared.md b/faqs/galaxy/histories_shared.md index c284e9c557c94f..1407f5eb5961cc 100644 --- a/faqs/galaxy/histories_shared.md +++ b/faqs/galaxy/histories_shared.md @@ -15,4 +15,4 @@ contributors: [jennaj, garimavs] - Import the History into your account via copying it to work with it. - Unshare Histories that you no longer want shared with you or that you have already made a copy of. -**Note:** Shared Histories (when copied into your account or not) do count in portion toward your total account data quota usage. More details on histories shared concerning account quota usage can be found in [this link](https://training.galaxyproject.org/training-material/faqs/galaxy/#unsharing-unwanted-histories). +**Note:** Shared Histories (when copied into your account or not) do count in portion toward your total account data quota usage. More details on histories shared concerning account quota usage can be found in [this link]({% link faqs/galaxy/histories_unsharing_histories.md %}). diff --git a/faqs/galaxy/reference_genomes_troubleshooting_custom_genome.md b/faqs/galaxy/reference_genomes_troubleshooting_custom_genome.md index f7130a60476e52..41045347fe5ae4 100644 --- a/faqs/galaxy/reference_genomes_troubleshooting_custom_genome.md +++ b/faqs/galaxy/reference_genomes_troubleshooting_custom_genome.md @@ -7,7 +7,7 @@ contributors: [jennaj, Nurzhamalyrys] --- -If a custom genome/transcriptome/exome dataset is producing errors, double check the format and that the chromosome identifiers between **ALL** inputs. Clicking on the bug icon {% icon galaxy-bug %} will often provide a description of the problem. This does not automatically submit a bug report, and it is not always necessary to do so, but it is a good way to get some information about [why a job is failing](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_troubleshooting.html). +If a custom genome/transcriptome/exome dataset is producing errors, double check the format and that the chromosome identifiers between **ALL** inputs. Clicking on the bug icon {% icon galaxy-bug %} will often provide a description of the problem. This does not automatically submit a bug report, and it is not always necessary to do so, but it is a good way to get some information about [why a job is failing]({% link faqs/galaxy/analysis_troubleshooting.md %}). - Custom genome not assigned as FASTA format diff --git a/faqs/galaxy/troubleshooting_cluster_failure.md b/faqs/galaxy/troubleshooting_cluster_failure.md index 967a0238c06c92..c8b8fe5153a77c 100644 --- a/faqs/galaxy/troubleshooting_cluster_failure.md +++ b/faqs/galaxy/troubleshooting_cluster_failure.md @@ -25,5 +25,5 @@ Remote job server indicated a problem running or monitoring this job. - Solutions: - Try at least one rerun. Server/cluster errors like this are usually transient. - - Review the Solutions section of the [Understanding input error messages](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_job_failure_input_problem.html) FAQ. - - If after any corrections, the job still fails, please report the technical issue [following the extended issue guidelines](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_reporting_issues.html). + - Review the Solutions section of the [Understanding input error messages]({% link faqs/galaxy/troubleshooting_input_problem.md %}) FAQ. + - If after any corrections, the job still fails, please report the technical issue [following the extended issue guidelines]({% link faqs/galaxy/analysis_reporting_issues.md %}). diff --git a/faqs/galaxy/troubleshooting_excess_memory.md b/faqs/galaxy/troubleshooting_excess_memory.md index 24799d24b4f876..064b2888f10de4 100644 --- a/faqs/galaxy/troubleshooting_excess_memory.md +++ b/faqs/galaxy/troubleshooting_excess_memory.md @@ -43,5 +43,5 @@ Memory Error - The most common reasons for this error are input and tool parameters problems that must be adjusted/corrected. - Solutions: - Try at least one rerun to execute the job on a different cluster node. - - Review the Solutions section of the [Understanding input error messages](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_job_failure_input_problem.html) FAQ. - - Your data may actually be too large to process at a public Galaxy server. Alternatives include setting up a [private Galaxy server](https://training.galaxyproject.org/training-material/faqs/gtn/galaxy_usage.html). + - Review the Solutions section of the [Understanding input error messages]({% link faqs/galaxy/troubleshooting_input_problem.md %}) FAQ. + - Your data may actually be too large to process at a public Galaxy server. Alternatives include setting up a [private Galaxy server]({% link faqs/gtn/galaxy_usage.md %}). diff --git a/faqs/galaxy/troubleshooting_input_problem.md b/faqs/galaxy/troubleshooting_input_problem.md index 450d60841a6905..002bf510c368ea 100644 --- a/faqs/galaxy/troubleshooting_input_problem.md +++ b/faqs/galaxy/troubleshooting_input_problem.md @@ -12,7 +12,7 @@ Input problems are very common across any analysis that makes use of programmed - Causes: - No quality assurance or content/formatting checks were run on the first datasets of an analysis workflow. - Incomplete dataset Upload. - - Incorrect or unassigned [datatype](https://training.galaxyproject.org/training-material/faqs/galaxy/#datasets) or [database](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_change_dbkey.html). + - Incorrect or unassigned [datatype]({% link faqs/galaxy/index.md %}#datasets) or [database]({% link faqs/galaxy/datasets_change_dbkey.md %}). - Tool-specific formatting requirements for inputs were not met. - Parameters set on a tool form are a mismatch for the input data content or format. - Inputs were in an error state (red) or were putatively successful (green) but are empty. @@ -22,27 +22,27 @@ Input problems are very common across any analysis that makes use of programmed - **Special case:** Some of the data were generated outside of Galaxy, but later a built-in indexed genome build was assigned in Galaxy for use with downstream tools. This scenario can work, but only if those two reference genomes are an exact match. - Solutions: - - Review our [Troubleshooting Tips](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_troubleshooting.html) for what and where to check. + - Review our [Troubleshooting Tips]({% link faqs/galaxy/analysis_troubleshooting.md %}) for what and where to check. - Review the [GTN](https://training.galaxyproject.org/) for related tutorials on tools/analysis plus FAQs. - Review [Galaxy Help](https://help.galaxyproject.org/) for prior discussion with extended solutions. - - Review [datatype FAQs](https://training.galaxyproject.org/training-material/faqs/galaxy/datatypes_understanding_datatypes.html). + - Review [datatype FAQs]({% link faqs/galaxy/datatypes_understanding_datatypes.md %}). - Review the tool form. - Input selection areas include usage help. - The help section at the bottom of a tool form often has examples. Does your own data match the format/content? - See the links to publications and related resources. - Review the inputs. - All inputs must be in a success state (green) and actually contain content. - - Did you [directly assign the datatype](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_change_datatype.html) or [convert the datatype](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_convert_datatype.html)? What results when the [datatype is detected](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_detect_datatype.html) by Galaxy? If these differ, there is likely a content problem. + - Did you [directly assign the datatype]({% link faqs/galaxy/datasets_change_datatype.md %}) or [convert the datatype]({% link faqs/galaxy/datasets_convert_datatype.md %})? What results when the [datatype is detected]({% link faqs/galaxy/datasets_detect_datatype.md %}) by Galaxy? If these differ, there is likely a content problem. - For most analysis, allowing Galaxy to detect the datatype during Upload is best and adjusting a datatype later should rarely be needed. If a datatype is modified, the change has a specific purpose/reason. - - Does your data have headers? Is that in specification for the datatype? Does the tool form have an option to specify if the input has headers or not? Do you need to remove headers first for the correct datatype to be detected? Example [GTF](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_working_with_reference_annotation.html). - - Large inputs? Consider modifying your inputs to be smaller. Examples: [FASTQ](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_working_with_fastq.html) and [FASTA](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_working_with_fasta.html). + - Does your data have headers? Is that in specification for the datatype? Does the tool form have an option to specify if the input has headers or not? Do you need to remove headers first for the correct datatype to be detected? Example [GTF]({% link faqs/galaxy/datasets_working_with_reference_annotation.md %}). + - Large inputs? Consider modifying your inputs to be smaller. Examples: [FASTQ]({% link faqs/galaxy/datasets_working_with_fastq.md %}) and [FASTA]({% link faqs/galaxy/datasets_working_with_fasta.md %}). - Run quality checks on your data. - Search [GTN](https://training.galaxyproject.org/) tutorials with the keyword “qa-qc” for examples. - Search [Galaxy Help](https://help.galaxyproject.org/) with the keywords “qa-qc” and your datatype(s) for more help. - Reference annotation tips. - - In most cases, [GTF is preferred over GFF3](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_working_with_reference_annotation.html). + - In most cases, [GTF is preferred over GFF3]({% link faqs/galaxy/datasets_working_with_reference_annotation.md %}). - Search [Galaxy Help](https://help.galaxyproject.org/) with the keywords “gtf” and “gff3” for more help. - Input mismatch tips. - Do the chromosome/sequence identifiers exactly match between all inputs? Search [Galaxy Help](https://help.galaxyproject.org/) for more help about how to correct build/version identifier mismatches between inputs. - "Chr1" and "chr1" and "1" do not mean the same thing to a tool. - - Custom genome transcriptome exome tips. See [FASTA](https://training.galaxyproject.org/training-material/faqs/galaxy/datasets_working_with_fasta.html). + - Custom genome transcriptome exome tips. See [FASTA]({% link faqs/galaxy/datasets_working_with_fasta.md %}). diff --git a/faqs/galaxy/troubleshooting_walltime.md b/faqs/galaxy/troubleshooting_walltime.md index 26f5c792304832..3ed1add6a8eb90 100644 --- a/faqs/galaxy/troubleshooting_walltime.md +++ b/faqs/galaxy/troubleshooting_walltime.md @@ -28,6 +28,6 @@ Remote job server indicated a problem running or monitoring this job. - Very often input problems also cause this same error. - Solutions: - Try at least one rerun. - - Check the server homepage for banners or notices. Selected servers also post status [here](https://status.galaxyproject.org/). - - Review the Solutions section of the [Understanding input error messages](https://training.galaxyproject.org/training-material/faqs/galaxy/analysis_job_failure_input_problem.html) FAQ. - - Your data may actually be too large to process at a public Galaxy server. Alternatives include setting up a [private Galaxy server](https://training.galaxyproject.org/training-material/faqs/gtn/galaxy_usage.html). + - Check the server homepage for banners or notices. Selected servers also post to the [Galaxy status page](https://status.galaxyproject.org/). + - Review the Solutions section of the [Understanding input error messages]({% link faqs/galaxy/troubleshooting_input_problem.md %}) FAQ. + - Your data may actually be too large to process at a public Galaxy server. Alternatives include setting up a [private Galaxy server]({% link faqs/gtn/galaxy_usage.md %}). diff --git a/faqs/gtn/fair_training.md b/faqs/gtn/fair_training.md index e72f233c1d6102..6bd0ab3386de99 100644 --- a/faqs/gtn/fair_training.md +++ b/faqs/gtn/fair_training.md @@ -15,7 +15,7 @@ Improve findability of your training materials by properly describing them | Ric Give your training materials a unique identity | URL persistency with redirection in case of renaming of tutorials. Data used for tutorials stored on Zenodo and associated with a Digital Object Identifiers (DOI) Register your training materials online | Tutorials automatically registered on TeSS, the [ELIXIR's Training e-Support System](https://tess.elixir-europe.org/) If appropriate, define access rules for your training materials | Online and free to use without registration -Use an interoperable format for your training materials | Content of the tutorials and slides written in Markdown. Metadata associated with tutorials stored in YAML, and workflows in JSON. All of this metadata is available [from the GTN's API](https://training.galaxyproject.org/training-material/api/) +Use an interoperable format for your training materials | Content of the tutorials and slides written in Markdown. Metadata associated with tutorials stored in YAML, and workflows in JSON. All of this metadata is available [from the GTN's API]({% link api/index.html %}) Make your training materials (re-)usable for trainers | Online. Rich metadata associated with each tutorial: title, contributor details, license, description, learning outcomes, audience, requirements, tags/keywords, duration, date of last revision. Strong technical support for each tutorial: workflow, data on Zenodo and also available as data libraries on UseGalaxy.\*, tools installable via the Galaxy Tool Shed, list of possible Galaxy instances with the needed tools. Make your training materials (re-)usable for trainees | Online and easy to follow hands-on tutorials. Rich metadata with "Specific, Measurable, Attainable, Realistic and Time bound" (SMART) learning outcomes following Bloom's taxonomy. Requirements and follow-up tutorials to build learning path. List of Galaxy instances offering needed tools, data on Zenodo and also available as data libraries on UseGalaxy.\*. Support chat embedded in tutorial pages. Make your training materials contribution friendly and citable | Open and collaborative infrastructure with contribution guidelines, a CONTRIBUTING file and a chat. Details to cite tutorials and give credit to contributors available at the end of each tutorial. diff --git a/topics/assembly/tutorials/vgp_workflow_training/tutorial.md b/topics/assembly/tutorials/vgp_workflow_training/tutorial.md index aae925abbc0c6c..ce0f8f7fb07c79 100644 --- a/topics/assembly/tutorials/vgp_workflow_training/tutorial.md +++ b/topics/assembly/tutorials/vgp_workflow_training/tutorial.md @@ -61,7 +61,7 @@ This tutorial assumes you are comfortable getting data into Galaxy, running jobs - [Galaxy 101]({% link topics/introduction/tutorials/galaxy-intro-101/tutorial.md %}) - [Getting Data into Galaxy]({% link topics/galaxy-interface/tutorials/get-data/slides.html %}) - [Using Dataset Collections]({% link topics/galaxy-interface/tutorials/collections/tutorial.md %}) -- [Introduction to Galaxy Analyses](https://training.galaxyproject.org/training-material/topics/introduction) +- [Introduction to Galaxy Analyses]({% link topics/introduction/index.md %}) - [Understanding the Galaxy History System]({% link topics/galaxy-interface/tutorials/history/tutorial.md %}) - [Downloading and Deleting Data in Galaxy]({% link topics/galaxy-interface/tutorials/download-delete-data/tutorial.md %}) @@ -371,6 +371,3 @@ With respect to the total sequence length, we can conclude that the size of our ![Figure 10: Comparison reference genome](../../images/vgp_assembly/hi-c_pretext_conclusion.png "Comparison between contact maps generated using the final assembly (a) and the reference genome (b).") If we compare the contact map of our assembled genome (fig. 10a) with the reference assembly (fig. 10b), we can see that the two are indistinguishable, suggesting that we have generated a chromosome level genome assembly. - - - diff --git a/topics/contributing/tutorials/slides-with-video/tutorial.md b/topics/contributing/tutorials/slides-with-video/tutorial.md index fba9f926f4c845..24e59075d617a5 100644 --- a/topics/contributing/tutorials/slides-with-video/tutorial.md +++ b/topics/contributing/tutorials/slides-with-video/tutorial.md @@ -162,7 +162,7 @@ The above voice example is specific to Spanish language content, hence not being 1. We take our markdown slides, e.g. [`topics/introduction/tutorials/galaxy-intro-short/slides.html`](https://github.com/galaxyproject/training-material/blob/main/topics/introduction/tutorials/galaxy-intro-short/slides.html) 2. In order for them to be processed, slides must have an annotation saying `video: true` in the header metadata, and then 'speaker notes' (everything after the ??? before the ---) -3. This is turned into our 'plain text slides' which just renders the markdown a bit more nicely ([example](https://training.galaxyproject.org/training-material/topics/introduction/tutorials/galaxy-intro-short/slides-plain.html)) +3. This is turned into our 'plain text slides' which just renders the markdown a bit more nicely ([example]({{site.baseurl}}/topics/introduction/tutorials/galaxy-intro-short/slides-plain.html)) 4. Then we run ari.sh which does the following: - `make video` is run which runs [`bin/ari-make.sh`](https://github.com/galaxyproject/training-material/blob/main/bin/ari-make.sh) @@ -182,4 +182,3 @@ All of this is run on cron by [`.github/workflows/video.yml`](https://github.com Many of the scripts internally are prefixed with `ari`, we named our internal version after [github.com/jhudsl/ari/](https://github.com/jhudsl/ari/) which inspired it, but we wanted a version that would be more closely tied to the GTN and integrate with our infrastructure nicely, so we ended up writing our own. # Conclusion - diff --git a/topics/data-science/tutorials/online-resources-protein/tutorial.md b/topics/data-science/tutorials/online-resources-protein/tutorial.md index 55d37a43b8ce83..289397f2745b64 100644 --- a/topics/data-science/tutorials/online-resources-protein/tutorial.md +++ b/topics/data-science/tutorials/online-resources-protein/tutorial.md @@ -192,7 +192,7 @@ Let's now scroll the entry page, section by section. This section summarises the functions of this protein as follows: -> Visual pigments are the light-absorbing molecules that mediate vision. They consist of an apoprotein, opsin, covalently linked to cis-retinal. +Visual pigments are the light-absorbing molecules that mediate vision. They consist of an apoprotein, opsin, covalently linked to cis-retinal. Regardless of the level of details that you understand (depending on your background), this is impressively short and specific considering the enourmous amount of literature and studies that exist beyond the determination of a protein function. Anyway, someone did the work for us, and this protein is already fully classified in the Gene Ontology (GO), which describes any classified protein's molecular function, biological process and cellular component. diff --git a/topics/dev/tutorials/tool-from-scratch/faqs/get_planemo.md b/topics/dev/tutorials/tool-from-scratch/faqs/get_planemo.md index 7546685b300645..efc68f2da431c4 100644 --- a/topics/dev/tutorials/tool-from-scratch/faqs/get_planemo.md +++ b/topics/dev/tutorials/tool-from-scratch/faqs/get_planemo.md @@ -5,11 +5,6 @@ layout: faq contributors: [astrovsky01,davebx,bernt-matthias] --- -Plese see [the installation section](https://training.galaxyproject.org/training-material/topics/dev/tutorials/tool-from-scratch/tutorial.html#installing-planemo). Essentially you can `pip install planemo`. If you don’t have pip, you need to install this first. +Plese see [the installation section]({% link topics/dev/tutorials/tool-from-scratch/tutorial.md %}#installing-planemo). Essentially you can `pip install planemo`. If you don’t have pip, you need to install this first. On windows you’ll need `WSL2` and then you can `apt-get install python3-pip`, same for ubuntu. For OSX users it is probably present. - - - - - diff --git a/topics/evolution/tutorials/mtb_phylogeny/tutorial.md b/topics/evolution/tutorials/mtb_phylogeny/tutorial.md index 4a991f79fc8ee5..920a505ee8655f 100644 --- a/topics/evolution/tutorials/mtb_phylogeny/tutorial.md +++ b/topics/evolution/tutorials/mtb_phylogeny/tutorial.md @@ -122,7 +122,7 @@ Near the end of this tutorial, we will show and discuss ways to correct for this > The alternative approach: genome assembly > -> A frequently used alternative approach to obtain a phylogeny from short read data is to a) assemble the genomes (see the numerous [Galaxy tutorials](https://training.galaxyproject.org/training-material/topics/assembly) on this topic), b) annotate genes, c) extract genes present in all strains (the "core" genes), d) align the core genes. This approach underlies core genome multilocus sequence typing (cgMLST), which is often used to genotype bacterial pathogens (e.g. {% cite Zhou2021 %}). +> A frequently used alternative approach to obtain a phylogeny from short read data is to a) assemble the genomes (see the numerous [Galaxy tutorials]({% link topics/assembly/index.md %}) on this topic), b) annotate genes, c) extract genes present in all strains (the "core" genes), d) align the core genes. This approach underlies core genome multilocus sequence typing (cgMLST), which is often used to genotype bacterial pathogens (e.g. {% cite Zhou2021 %}). > {: .comment} diff --git a/topics/fair/tutorials/ro-crate-in-galaxy/tutorial.md b/topics/fair/tutorials/ro-crate-in-galaxy/tutorial.md index fd36a20dc33114..27ae272585753f 100644 --- a/topics/fair/tutorials/ro-crate-in-galaxy/tutorial.md +++ b/topics/fair/tutorials/ro-crate-in-galaxy/tutorial.md @@ -48,7 +48,7 @@ Additionally, the exported Workflow Run Crate allows for sharing workflow run pr This tutorial will guide you through the steps of defining such a report for your workflow, . -This tutorial will show you how to generate Galaxy-based [Workflow Run RO-Crate](https://w3id.org/ro/crate/) after running the workflow. +This tutorial will show you how to generate Galaxy-based [Workflow Run RO-Crate](https://w3id.org/ro/crate/) after running the workflow. ## Import an example workflow @@ -125,7 +125,7 @@ After the workflow has completed, we can export the RO-Crate. The crate does not > - Location where the export should be sent to (Download locally or a remote location) > > 5. Click on the Download option -> +> > ![screenshot of the beginning of the workflow run export options](./images/workflow-invocation-export.png) > {: .hands_on} diff --git a/topics/galaxy-interface/tutorials/collections/tutorial.md b/topics/galaxy-interface/tutorials/collections/tutorial.md index 9677ff680ae861..02b81f4671884b 100644 --- a/topics/galaxy-interface/tutorials/collections/tutorial.md +++ b/topics/galaxy-interface/tutorials/collections/tutorial.md @@ -511,7 +511,7 @@ More about tags > More about tags > Galaxy allows tagging datasets to facilitate analyses. There are several types of tags including simple tags, name tags, and group tags. **Simple** tags allow you to attach an alternative label to a dataset, which will make it easier to find it later. **Name** tags allow you to track propagation of a dataset through the analyses: all datasets derived from the initial dataset labeled with a name tag will inherit it. Finally, **group** tags allow you to label group of datasets. This is useful. for example, for differential expression analysis where you can have two groups of datasets labeled as "treatment" and "control". > ->To learn mote about tags go to [training site](https://training.galaxyproject.org/training-material/search?query=tags). +>To learn mote about tags go to [training site]({% link search.md %}?query=tags). {: .tip} ## Tools that change collection structure diff --git a/topics/sequence-analysis/tutorials/ncbi-blast-against-the-madland/tutorial.md b/topics/sequence-analysis/tutorials/ncbi-blast-against-the-madland/tutorial.md index 864e98c5a6240b..7269d44b6735ca 100644 --- a/topics/sequence-analysis/tutorials/ncbi-blast-against-the-madland/tutorial.md +++ b/topics/sequence-analysis/tutorials/ncbi-blast-against-the-madland/tutorial.md @@ -111,5 +111,3 @@ The fields are separated by tabs, and each row represents a single hit. For more > > See {% cite Buchfink2014 %} for more discussion. {: .details} - - diff --git a/topics/single-cell/tutorials/scrna-case_alevin/tutorial.md b/topics/single-cell/tutorials/scrna-case_alevin/tutorial.md index 69b7a3a646e449..c22cb2c779883b 100644 --- a/topics/single-cell/tutorials/scrna-case_alevin/tutorial.md +++ b/topics/single-cell/tutorials/scrna-case_alevin/tutorial.md @@ -520,7 +520,7 @@ This sample was originally one of seven. So to run the other [12 downsampled FAS # Mitochondrial flagging -We have assumed you will be combining multiple files - but if that's not the case, you'll need to perform this step to turn your column of `true` and `false` labelling the mitochondrial genes into some metrics telling you the % of mitochondrial genes in each cell. You can follow that step here in the **Mithochondrial reads** section in the following tutorial: [Combining datasets with Alevin tutorial]({% link topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md %}). +We have assumed you will be combining multiple files - but if that's not the case, you'll need to perform this step to turn your column of `true` and `false` labelling the mitochondrial genes into some metrics telling you the % of mitochondrial genes in each cell. You can follow that step here: [Mitochondrial calculations]({% link topics/single-cell/tutorials/scrna-case_alevin-combine-datasets/tutorial.md %}#mitochondrial-reads). # Conclusion