README.md

AwesomeGenomics

Cancer Data Science's go to place for excellent genomics tools and packages

If something needs to be changed or added, feel free to create a pull request.

Awesome awesomeness

Here is a list of lists of awesome bio-informatics tools!

• https://github.com/danielecook/Awesome-Bioinformatics :A curated list of awesome Bioinformatics libraries and software.

• https://github.com/WooGenome/awesome-bioinformatics: A curated list of awesome Bioinformatics databases, softwares, libraries, toolboxes, pipelines, books, courses, tutorials and more.

• https://github.com/mikelove/awesome-multi-omics: List of software packages for multi-omics analysis

• https://github.com/seandavi/awesome-single-cell: Community-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.

• https://github.com/crazyhottommy/ChIP-seq-analysis: A curated list of awesome ChIP-seq things

• https://github.com/j-andrews7/awesome-bioinformatics-benchmarks: A curated list of bioinformatics bench-marking papers and resources.

Awesome Bio reads

• https://github.com/zhongmicai/awesomeBiology: awesome biology
• https://github.com/raivivek/awesome-biology: Curated list of resources for Biology.
• https://github.com/XindiWu/Awesome-Machine-Learning-in-Biomedical-Healthcare-Imaging: A list of awesome selected resources towards the application of machine learning in Biomedical/Healthcare Imaging, inspired by
• https://github.com/yangkky/Machine-learning-for-proteins: Listing of papers about machine learning for proteins.
• https://github.com/gokceneraslan/awesome-deepbio: A curated list of awesome deep learning applications in the field of computational biology

Others

• https://github.com/GuanLab/Awesome-Bioinformatics: 2018 Recommended Papers to Read in Bioinformatics as Voted by Bioinformaticians
• https://github.com/keller-mark/awesome-biological-visualizations: A list of web-based interactive biological visualizations.
• https://github.com/hussius/deeplearning-biology: A list of deep learning implementations in biology
• https://github.com/caufieldjh/awesome-bioie: 🧫 A curated list of resources relevant to doing Biomedical Information Extraction (including BioNLP)
• https://github.com/mahmoud/awesome-python-applications: awesome apps
• https://github.com/serhii-londar/open-source-mac-os-apps: awesome macapps
• https://github.com/shenwei356/awesome: awesome datascience
• https://github.com/krzjoa/awesome-python-data-science: awesome datascience2!
• https://github.com/lukasz-madon/awesome-remote-job: awesome remote job
• https://github.com/xiamx/awesome-sentiment-analysis: sentiment analysis
• https://github.com/analyticalmonk/awesome-neuroscience: awesome neuroscience
• https://github.com/xhacker/awesome-github-extensions: improve your github experience

Awesome python

• awesome notebooks
• awesome notebooks2
• awesome python
• awesome python2!
• awesome python3!!
• awesome python security
• everythings pytorch
• goto snippets
• wanna do more async?
• scrap the web
• python chemistry
• decorate your code
• put your notebook to the next phase
• ...and your pandas

Computational

Worklow Management

• Terra: a very low barrier to entry, workflow and data management platform for medical and research genomics
  • Dalmatian: to interact with Terra in python
• Nextflow
• Google Genomics Pipelines
• reflow
• snakemake

Dataset Management

• Terra
• GS
• Taiga:
  • taigaR
  • taigaPy

bash Basics

python Basics

• bokeh: Best Interactive Plot with JS

R Basics

Neat python

• POT: library for solving optimal transport optimization problems.
• Itrask: set of differential privacy tools for analyzing data!
• JKBio: Jeremie Kalfon's python scripts for genomics.
• CDSpy: some plotting scripts for genomic analysis.
• Selene: a framework for training sequence-level deep learning networks
• nbstripout: removes notebook outputs before git pushing
• voila: turns your jupyter notebook into awesome web apps
• ngrock: secure introspectable tunnels: transforms http://8.8.8.8:8888 into https://www.ngrock.id.com
• SublimeJEDI: python+sublime
• functional-python: learn!
• python parser
• plot in your terminal

Neat R

Neat Bash

Other

• Nice tools and Discussion on DL: Tutorials, assignments, and competitions for MIT Deep Learning related courses. https://deeplearning.mit.edu
• Kipoi: model Zoo for DL in genomics!
• interpretability: start building interpretable models

Genomics

read mapper

• NextGenMap: NextGenMap is a flexible highly sensitive short read mapping tool that handles much higher mismatch rates than comparable algorithms
• [bwa]
• [bowtie]
• []
• []

Mutations

• Mutect1: _an awesome cancer mutation caller, especially for calling point mutations)
• Lancet: Based on microAssembly with a decision model
• [R] ACE: Absolute CN estimation from low coverage WGS
• Absolute: Absolute CN estimation from WES/WGS giving off many predictions to choose from (need prior knowledge)
  • DoAbsolute: an R package to Automate the Absolute algorithm
• Strelka: small variant caller (germline/somatic)
• Manta: the SV caller version of Strelka
• DeepVariant: Fast Variant Calling with DL

Effect Prediction

• [py] DeepSea: Prediction of Effect of non coding variant on ChIP seq binding/Expression/.. with DL
• [py] SpliceAI: Predicting Effect of Variant on the splicing (modeling the spliceosome) with DL
• [py] ExPecto: Predicting effect of NC variant on Expression with DL

Annotators

• OncoKB: annotates MAF from oncoKB DB
• Oncotator: annotates MAF from many DBs (not very well documented)

Expression

• STAR-Fusion: call fusions from RNAseq data

Next gen Expression

• slamdunk: to analyse slamseq data
• JK/slamdunk: Paired End version

Differential Expression

Others

Single Cell

some competitors: [py] awesome single cell

Expression

• novosparc: reconstruct 3D disposition from scRNAseq and some known location+expression atlases

Differential Expression

Epigenomics

A lot is available for ChIPseq from crazyhottommy's repo

• pyGenomeTracks: viewer/plotter for Multi-Epigenomics data

ChIPseq and related

• MACS2: go to peak caller
• EPIC2: calling peaks from ChIP seq
• RSEG: another peak caller
• coda: denoising ChIPseq data with CNNs
• CREAM: identifying clusters of functional regions within the genome from ChIPseq data
• ngsplot: multi omics viz tool at specific locus
• epi-corr: correlation tool for pairs of ChIP seq data
• SUPERmerge: a ChIP-seq read pileup analysis and annotation algorithm for investigating alignment (BAM) files of diffuse histone modification ChIP-seq datasets with broad chromatin domains at a single base pair resolution level
• nf-core/chipseq: Complete ChIPseq pipeline on nextflow
• pyBigWig: interacts with bigwig from python
• deepTools: a set of cmd line tools for epigenomics data
• EnrichedHeatmap: make the famous enrichment at locus heatmap plots.

diff binding

• MACS2 diff binding: how to do differential binding analysis with MACS2.

Predictors

• DeepBind: predicting binding location from previous binding data with CNN -DeeperBind: a deeper version
• Basenji: Predicts Binding from Mutations with CNN
• ABC model: Predicts Enhancer-gene links

ATACseq

HiCseq and related

• HiCExplorer: process, normalize and visualize HiC data