biotools: A list of useful bioinformatics resources

来自 : 发布时间：2025-01-16

同步操作将从 JmWang/biotools 强制同步，此操作会覆盖自 Fork 仓库以来所做的任何修改，且无法恢复！！！确定后同步将在后台操作，完成时将刷新页面，请耐心等待。This is a collection of bioinformatics tools I have sourced from recent literature, organized by topic. I have not used most of these tools. Table of Contents Clinical data EHR Data Management Data Sets Discovery Genomics General Information Algorithms Assay Design Candidate Prioritization Databases Data Formats Functional Enrichment/Ontology GWAS/QTL Microarray Motif/TFBS Network Analysis Population genetics Prediction Security Sequencing Protocols Simulation Variant annotation Sequence Analysis General-purpose Demultiplexing QC ChIP-seq Chromatin accessibility Chromatin Interactions DNA Footprinting Metagenomics Methylation MNase-seq Nanopore RNA Single-cell Somatic Integrated Methods General Programming Resources C/C++ R Find packages Database Data Cleaning Reporting Misc Python HPC Command Line (OSX/Linux) Databases Reproducibility/Containerization Building Pipelines Software Distribution Other Statistics/Machine Learning Methods/algorithms Python Deep Learning Web APIs Data Sets Text classification Visualization Networks Phylogenetic trees R ggplot2 Plot Types Data Types Interactive Python Javascript Examples Publication/Archiving Code/Data sharing Journals Writing Posters Slides CV Promising methods without software implementation Clinical data EHR BlueButton related tools https://github.com/amida-tech Data Management Send stdout to a Google Sheet: https://github.com/kren1/tosheets Data Sets 1000 Genomes (RNA-Seq, ChIP-Seq): http://archive.gersteinlab.org/docs/2015/06.04/1kg_fun_studies.htm In S3: http://s3.amazonaws.com/1000genomes 2500 Phase 3 samples at 30X: https://www.ebi.ac.uk/ena/data/view/PRJEB31736 Reference panel of ~250 Dutch families http://biorxiv.org/content/early/2016/01/18/036897 FANTOM consortium has CAGE (5\' single molecule RNA counting) data from ~1000 human cell/tissue/cell-line samples from ~300 different cell/tissue types Full text of all PMC papers from 2008-present: ftp://ftp.ncbi.nlm.nih.gov/pub/pmc/manuscript/ Phased genome sequences 100 fully-phased: http://gigascience.biomedcentral.com/articles/10.1186/s13742-016-0148-z Statistically phased: http://www.haplotype-reference-consortium.org/ Phased variants: http://genome.cshlp.org/content/early/2016/11/25/gr.210500.116.full.pdf+html Clinical trials: http://vivli.org/ Targets for drug discovery: https://www.targetvalidation.org/ Large medical datasets for ML: https://github.com/beamandrew/medical-data Species images: http://phylopic.org/image/browse/ Public RNA-Seq data: https://jhubiostatistics.shinyapps.io/recount/ with phenotype predictions https://bioconductor.org/packages/release/bioc/html/recount.html human and mouse: http://amp.pharm.mssm.edu/archs4 Whole genomes of 150 Danish individuals: http://www.nature.com/nature/journal/v548/n7665/full/nature23264.html Simons Genome Diversity Project: https://www.simonsfoundation.org/simons-genome-diversity-project/ BloodPAC: https://doi.bloodpac.org/BLOODPAC.0001/ Reference haplotypes for STR phasing: https://www.biorxiv.org/content/early/2018/03/06/277673 Database of Curated Mutations: http://www.docm.info/ Normalized, batch-corrected RNA-seq for both normal (GTEx) and cancer (TCGA) data: https://www.nature.com/articles/sdata201861 Hartwig WGS (requires data request): https://www.hartwigmedicalfoundation.nl/en/wgs-database/ Orthogonally validated TP and FP CNVs: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473400/ Microarray transcriptome profiles prepared for ML applications: https://www.biorxiv.org/content/biorxiv/early/2018/06/22/353698.full.pdf Cloud resource for filtering 1kg data http://bamsi.research.it.uu.se/ Python API: https://github.com/NGDSG/BAMSI-API Paired mRNA and protein abundance in 26 human tissues: https://www.biorxiv.org/content/biorxiv/early/2018/06/27/357137.full.pdf Cancer cell lines: correlation with primary tumor data (analysis for use as models): http://comphealth.ucsf.edu/TCGA110/ gnomAD: SNV, small InDels, MNV, SV, and LoF metrics from large numbers of exomes and whole-genomes: https://gnomad.broadinstitute.org/downloads 1000G Structural variation: http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/hgsv_sv_discovery/working/20181025_EEE_SV-Pop_1/AltReference_EEE_SV-Pop_1/ PGP UK: https://www.personalgenomes.org.uk/data/ Platinum genomes phased variant calls: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001224.v1.p1 ICGC: https://dcc.icgc.org/ Cancer Cell Lines Encylopedia: https://portal.gdc.cancer.gov/legacy-archive/search/f Deep sequencing of 3 cancer cell lines (2 colorectal, one breast cancer) on 2 sequencing platforms: https://www.biorxiv.org/content/10.1101/623702v1 Deep sequencing of another breast cancer cell line (SEQC2 consoritum): https://www.biorxiv.org/content/biorxiv/early/2019/05/13/625624.full.pdf Variation benchmark datasets: http://structure.bmc.lu.se/VariBench/ Discovery When looking for a bioinformatics tool for a specific application: OmicTools: http://omictools.com/ GitXiv: http://www.gitxiv.com/?cat%5B0%5D=bioinformatics Bio.Tools: https://bio.tools/ Biosharing: https://biosharing.org Genomics General Information Migrating to GRCh38: https://software.broadinstitute.org/gatk/blog?id=8180 Mappings between contig names in different assemblies: https://github.com/dpryan79/ChromosomeMappings Command-line interface to download and manage reference genomes: https://github.com/simonvh/genomepy Algorithms Suffix arrays: http://almob.biomedcentral.com/articles/10.1186/s13015-016-0068-6 gapped k-mer SVM: classifiers for DNA and protein sequences http://www.beerlab.org/gkmsvm/ Version for large-scale data: https://github.com/Dongwon-Lee/lsgkm/ Fast BWT creation: https://github.com/hitbc/deBWT Assay Design Choosing assays based on complementarity to existing data: https://github.com/melodi-lab/Submodular-Selection-of-Assays MBRAnator: design of MPRA libraries https://www.genomegeek.com/ Candidate Prioritization GenRank: http://bioconductor.org/packages/GenRank/ Bayesian prioritizaiton of rare functional variants using RNA-seq data: https://github.com/ipw012/RIVER SnpNexus: http://snp-nexus.org/IW-Scoring/ Databases RNA Meta Analysis (web app) Perform meta-analyses of GEO microarray and RNA-Seq studies Correlate resulting signatures to CMAP02/L1000 perts and 26,000+ GEO studies https://www.rnama.com NAR catalog of databases, by subject: http://www.oxfordjournals.org/our_journals/nar/database/c/ Super-Enhancer Archive: http://www.bio-bigdata.com/SEA/ GWAS database: http://jjwanglab.org/gwasdb rVarBase: regulatory features of human genetic variants http://rv.psych.ac.cn/ TransVar http://bioinformatics.mdanderson.org/transvarweb/ dbMAE: mono-allelic expression https://mae.hms.harvard.edu/ Disease-gene associations http://www.disgenet.org/web/DisGeNET/menu/rdf BISQUE: convert between database identifiers http://bisque.yulab.org/ Human tissue-specific enhancers: http://www.enhanceratlas.org/ Searh HLI\'s genome data: hli-opensearch.com Chromatin-state annotations + per-base functionality scores for 164 cell types: http://noble.gs.washington.edu/proj/encyclopedia/ Feature-based classification of human transcription factors: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1349-2 Database of epifactors (epigenetic factors): http://epifactors.autosome.ru/ Database of disease-associated methylation: http://202.97.205.78/diseasemeth/ SRA metadata: http://deweylab.biostat.wisc.edu/metasra/ Human histone modifications: http://www.tongjidmb.com/human/index.html iRegNet3D: SNP-focused catalog of TF-TF, TF-DNA, and DNA-DNA interactions http://iregnet3d.yulab.org/index/ CTD: drug interactions and toxicity http://ctdbase.org/ FANTOM lncRNA catalog: http://fantom.gsc.riken.jp/cat/ Integrated database of public ChIP-Seq datasets: http://chip-atlas.org/ Alternative splicing: http://vastdb.crg.eu/wiki/Main_Page Interactive multi-omics tissue assay database: https://ccb-web.cs.uni-saarland.de/imota/ Database of cis-regulatory elements (enhancers): http://www.kostkalab.net/software.html Database of genetic variant effects on gene expression: https://xhaubem01.u.hpc.mssm.edu/gwas2genes/ Structural variation: https://www.ncbi.nlm.nih.gov/dbvar PharmacoDB Search multiple cancer pharmacogenomic databases with a single query Software is GPL licensed; target databases have various licenses http://pharmacodb.pmgenomics.ca HIVE BioMuta: https://hive.biochemistry.gwu.edu/biomuta/readme Database of druggable variant information (web only): http://depo-dinglab.ddns.net/ CHESS: new gene annotation database Contains most genes from RefSeq and Gencode, plus additional genes discovered from GTEx transcripts http://ccb.jhu.edu/chess/ Variant benchmark datasets: http://structure.bmc.lu.se/VariBench/ Metabase: aggregates various gene annotation sources: http://metascape.org Data Formats Fast ordered sampling of records: https://github.com/tonymugen/sampleSNPs/ Searching overlaps of large collections of interval databases: https://github.com/ryanlayer/giggle/ FASTA/FASTQ Read filtering and extraction: https://github.com/ad3002/Cookiecutter fqtools: for working with FASTQ files https://github.com/alastair-droop/fqtools Integrating results from multiple genome binning programs: https://github.com/songweizhi/Binning_refiner Compression fqzcomp: https://sourceforge.net/projects/fqzcomp/ Clumpify: https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/clumpify-guide/ NAF: https://github.com/KirillKryukov/naf SPRING: https://github.com/shubhamchandak94/SPRING Using de Bruijn graphs: https://github.com/rongjiewang/BdBG SAM/BAM/CRAM Read filtering and profiling: https://github.com/jwalabroad/VariantBam VCF Annotation: https://github.com/brentp/vcfanno BGT: https://github.com/lh3/bgt GQT BCFtools: includes new tool to identify RoH http://samtools.github.io/bcftools/ Work with VCF in R: https://github.com/knausb/vcfR More VCF tools: http://vcf-kit.readthedocs.io/ Validation: https://github.com/EBIvariation/vcf-validator BED/GFF GFFTools: https://github.com/ihh/gfftools Gff3Sort: https://github.com/billzt/gff3sort Combine p-values https://github.com/brentp/combined-pvalues Assessing interval overlap of multiple genomic features: https://github.com/andrew-leith/GINOM Python toolkit: https://github.com/dputhier/pygtftk Improved command line viewing of formatted data PrettySam: http://lindenb.github.io/jvarkit/PrettySam.html BioSyntax: https://biosyntax.org/install DNAcol: https://github.com/koelling/dnacol Generate NCBI submission data: https://github.com/genomeannotation/GAG Convert between many different formats, using a SQL database as intermediate representation: https://watson.hgen.pitt.edu/mega2/mega2r/ Functional Enrichment/Ontology General: R interface to DAVID: http://www.bioconductor.org/packages/release/bioc/html/RDAVIDWebService.html GSEA Cautions about the GSEA null model: http://bioinformatics.oxfordjournals.org/content/early/2017/01/02/bioinformatics.btw803.short GiANT: uncertainty in GSEA https://cran.r-project.org/web/packages/GiANT/index.html Ensemble gene set enrichment analysis: http://bioconductor.org/packages/release/bioc/html/EGSEA.html Fast GSA: https://github.com/billyhw/GSALightning Fast GSEA: https://github.com/ctlab/fgsea Multi-dimensional GSEA: http://bioconductor.org/packages/release/bioc/html/mdgsa.html GSEA with external information https://cran.r-project.org/web/packages/netgsa/index.html QTest: http://statgen.snu.ac.kr/software/QTest/ Gene set analysis with specific alternative hypothesis: https://bioconductor.org/packages/release/bioc/html/GSAR.html clusterProfiler: https://guangchuangyu.github.io/clusterProfiler/ DOSE: disease ontology https://guangchuangyu.github.io/dose/ ReactomePA: https://guangchuangyu.github.io/reactomepa/ SetRank: https://cran.r-project.org/web/packages/SetRank/index.html Identify and rank significance of overlaps: https://github.com/ryanlayer/giggle Generate feature vectors from ontology annotations: https://github.com/bio-ontology-research-group/onto2vec Gene sets EGSEA: http://bioconductor.org/packages/release/data/experiment/html/EGSEAdata.html ENCODE gene set hub: http://genomespot.blogspot.com.au/2017/02/introducing-encode-gene-set-hub.html GO term: GOrilla: http://cbl-gorilla.cs.technion.ac.il/ LRPath: http://lrpath.ncibi.org/ Reduce GO term lists: Revigo: http://revigo.irb.hr/ clusterProfiler: http://guangchuangyu.github.io/2015/10/use-simplify-to-remove-redundancy-of-enriched-go-terms/ GO Express: https://www.bioconductor.org/packages/release/bioc/html/GOexpress.html GO Extender: https://www.msu.edu/~jinchen/GOExtender/ DAL: http://iwera.ir/~ahmad/dal/ Negative GO enrichment: https://sites.google.com/site/guoxian85/neggoa Variant Set VSE: https://cran.r-project.org/web/packages/VSE/vignettes/my-vignette.html Functional enrichment with LD correction MESH MeSH over-representation: http://www.bioconductor.org/packages/release/bioc/vignettes/meshr/inst/doc/MeSH.pdf meshes: https://guangchuangyu.github.io/meshes/ Regional LOLA: http://lola.computational-epigenetics.org AnnotatR: https://github.com/rcavalcante/annotatr/ Compare epigenetic features in multiple samples: http://epigenome.wustl.edu/EpiCompare1/ Trait traseR: Trait-associated SNP enrichment https://www.bioconductor.org/packages/release/bioc/html/traseR.html Identifying genetic heterogeneity within phenotypically defined subgroups: https://github.com/jamesliley/subtest Multi-omics Single-sample GSA across data sets: https://www.bioconductor.org/packages/3.3/bioc/html/mogsa.html Network-based NEAT: https://cran.r-project.org/web/packages/neat/index.html EGAD: https://github.com/sarbal/EGAD Mutation intolerance: LIMBR: https://redmine.igm.cumc.columbia.edu/projects/atav/wiki/LIMBR Sub-regional: https://www.biorxiv.org/content/early/2018/09/30/431536 GWAS/QTL Association testing QTLtools: Pipeline for molecular QTL analysis https://qtltools.github.io/qtltools/ FastQTL: http://fastqtl.sourceforge.net/ RASQUAL: allele-specific QTL using phased SNPs - https://github.com/dg13/rasqual Relatedness, PCA: http://zhengxwen.github.io/SNPRelate/ Multi-SNP, multi-trait regression https://github.com/ashlee1031/BERRRI regioneR: permutation testing for association between genomic region and phenotype http://bioconductor.org/packages/release/bioc/html/regioneR.html MTG2: https://sites.google.com/site/honglee0707/mtg2 Use local gene networks to improve trans-eQTL detection: https://github.com/PMBio/GNetLMM Fast correlation testing: https://github.com/gabraham/flashpca/tree/master/flashpcaR Gene and pathway association testing from summary statistics: https://cran.r-project.org/web/packages/aSPU/ Account for LD and functional information: https://github.com/yjingj/SFBA Correcting for prediction uncertainty in TWAS: http://biorxiv.org/content/early/2017/02/14/108316 Using random forests https://github.com/0asa/TTree-source Using k-mers: https://github.com/atifrahman/HAWK BOLT-LMM: https://data.broadinstitute.org/alkesgroup/BOLT-LMM/ tensorQTL: https://github.com/broadinstitute/tensorqtl Variance eQTL veQTL mapper: https://funpopgen.github.io/veqtl-mapper/ Multiple test correction eigenMT: Efficient multiple-test correction http://montgomerylab.stanford.edu/resources/eigenMT/eigenMT.html Fast multiple-test correction for LMMs: http://genetics.cs.ucla.edu/multiTrans/ Hierarchical eQTL MTC: http://bioinformatics.org/treeqtl/ Controlling bias in EWAS/TWAS using null distribution: http://bioconductor.org/packages/bacon/ Prioritization GenoWAP: Prioritization of GWAS signals using functional information http://genocanyon.med.yale.edu/GenoWAP HitWalker2: https://github.com/biodev/HitWalker2 CRstar: https://nijingchao.github.io/CRstar/ Pines: http://genetics.bwh.harvard.edu/pines/ Fine-mapping Using summary statistics http://www.christianbenner.com uHDset: https://sites.google.com/site/shaolongscode/home/uhdset PAINTOR: fine mapping, prioritization - https://github.com/gkichaev/PAINTOR_FineMapping/ Genotype synthesis: https://sourceforge.net/projects/getsynth/ Prediction of causal variants from epigenomic annotations: https://github.mit.edu/liyue/rivieraBeta DAP: Bayesian framework for QTL analysis and fine-mapping https://github.com/xqwen/dap BayesFM: https://sourceforge.net/projects/bayesfm-mcmc-v1-0/ Determining causal genes using TADs http://biorxiv.org/content/early/2016/11/15/087718 Network analysis: https://github.com/YuanlongLiu/SigMod Haplotype-based: http://apps.biocompute.org.uk/haprap/ Ancestry SNPweights: https://www.hsph.harvard.edu/alkes-price/software/ LD LD score calculation and regression https://github.com/bulik/ldsc Haplotype block detection: https://github.com/sunnyeesl/BigLD Find directional effect of a signed functional annotation using summary statistics: https://github.com/yakirr/sldp Imputation of missing phenotype information http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3513.html Epistasis General linear-mixed model library; also includes mixed-RF method for detecting epistasis with population structure correction: https://github.com/PMBio/limix GPU-accelerated detection of epistasis using Bayesian neural networks: https://github.com/beamandrew/BNN MEPID: marginal epistasis test http://www.xzlab.org/software.html Other Browser for geographical distribution of genetic variants: http://popgen.uchicago.edu/ggv/ Integration of GWAS with molecular QTL: https://github.com/xqwen/integrative GxE interactions: https://github.com/davidaknowles/eagle Microarray Correct for batch effects between training data and external datasets: https://cran.r-project.org/web/packages/bapred/index.html Impute from Affy expression arrays: http://simtk.org/home/affyimpute SNP Call haplotypes https://cran.r-project.org/web/packages/GHap/index.html Neural network for breakpoint detection for CNV calling: https://www.biorxiv.org/content/biorxiv/early/2018/06/24/354423.full.pdf Methylation Minfi: R package for working with 450k methylation arrays D3M: two-sample test of differential methylation from distribution-valued data https://cran.r-project.org/web/packages/D3M/D3M.pdf Network-based approach to discovering epigenetic \"modules\" that can be associated with gene expression: http://bioinformatics.oxfordjournals.org/content/30/16/2360.long Filtering probes using technical replicates https://cran.r-project.org/web/packages/CpGFilter/index.html Imputation of genome-wide methylation: http://wanglab.ucsd.edu/star/LR450K/ Tutorial for analysis using bioconductor packages: http://biorxiv.org/content/biorxiv/early/2016/05/25/055087.full.pdf Normalization Probe design bias correction: https://www.bioconductor.org/packages/release/bioc/html/ENmix.html DMR calling SeqLM: https://github.com/raivokolde/seqlm PyMAP: http://aminmahpour.github.io/PyMAP/ Interactive exploration: http://bioconductor.org/packages/release/bioc/html/shinyMethyl.html eFORGE: identify cell type-specific signals in differentially methylated positions (mostly important for blood-based EWAS) http://eforge.cs.ucl.ac.uk/ Model for EWAS using probe signal intensities: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1347-4 Reference-based tissue deconvolution (three different algorithms): https://github.com/sjczheng/EpiDISH Glint pipeline (qc, EWAS, population structure): http://glint-epigenetics.readthedocs.io/en/latest/ Bayesian extension of Refactor cell type heterogeneity correction that incorporates experimentally determined cell counts: https://github.com/cozygene/BayesCCE Meffil: https://github.com/perishky/meffil/ Motif/TFBS Fast, SNP-aware PWM matching https://www.cs.helsinki.fi/group/pssmfind/ Cell type-specific TFBS analysis (focuses analysis on TFs expressed in cell type of interest): https://github.com/Danko-Lab/rtfbs_db Bayesian motif discovery: https://github.com/soedinglab/BaMMmotif R package for TFBS analysis: http://bioconductor.org/packages/release/bioc/html/TFBSTools.html FIMO and MCAST perform best of TFBS predictors: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1298-9 Intra-motif dependencies Identifying http://www.jstacs.de/index.php/InMoDe Visualizing http://bioinformaticstools.mayo.edu/circularlogo/index.html Dinucleotide weight tensors encode dependencies between positions in motifs: http://dwt.unibas.ch/ Circular logos: http://bioinformaticstools.mayo.edu/circularlogo/index.html Convert kernels learned by CNN to PWMs: ftp://ftp.cbi.pku.edu.cn/pub/software/CBI/k2p CRBM: https://github.com/schulter/crbm YAMDA: https://github.com/daquang/YAMDA Network Analysis Identification of module with biclustering: http://web.ist.utl.pt/rmch/bicnet/temporary/index.jsp Functional analysis https://bioconductor.org/packages/release/bioc/html/EGAD.html GAGE: http://bioconductor.org/packages/release/bioc/html/gage.html PAXToolsR: http://bioconductor.org/packages/release/bioc/html/paxtoolsr.html ancGWAS: post GWAS association with protein-protein interaction networks http://www.cbio.uct.ac.za/~emile/software.html Gene network reconstruction For a set of TFs: https://sourceforge.net/projects/aracne-ap/ From PPI or motif sharing: https://github.com/davidvi/pypanda (is also an integrative method that can incorporate multiple sources of information) Perturb-Net: Learn gene networks underlying clinical phenotypes https://github.com/sssykim/Perturb-Net https://www.biorxiv.org/content/early/2018/09/10/412817 Applied to TF binding: https://www.biorxiv.org/content/early/2018/09/10/412841 BANFF: https://cran.r-project.org/web/packages/BANFF/index.html SAFE: https://bitbucket.org/abarysh/safe Merlin-P: https://bitbucket.org/roygroup/merlin-p OSS alternative to Inginuity pathway analysis: https://www.bioconductor.org/packages/release/bioc/html/QuaternaryProd.html Similarity search: https://github.com/zhangjiaobxy/nssrfPackage Tissue-specific: https://cran.r-project.org/web/packages/GRAPE/index.html CLR with B-Spline for mutual information-based inference of transcriptional regulatory networks: https://bitbucket.org/Jonathan-Ish-Horowicz/fastgenemi/ Functional module identification and enrichement through Gene Ontology terms https://www.nature.com/articles/s41598-018-23672-0 Population genetics Population history from unphased whole-genomes: https://github.com/popgenmethods/smcpp Management of allele-frequency data: https://grenaud.github.io/glactools/ Calculation of LD from genome-wide genotype likelihoods: https://github.com/fgvieira/ngsLD Prediction Gene expression Predicting gene expression from methylation: http://arxiv.org/abs/1603.08386 QTL Imputation of summary statistics in multi-ethnic cohorts: http://dleelab.github.io/jepegmix/ Causal variant identification: http://genetics.cs.ucla.edu/caviar/ eQTL Imputation of gene expression from genotype data : https://github.com/hriordan/PrediXcan Genetic risk AnnoPred: https://github.com/yiminghu/AnnoPred Pathogenicity Disease-specific https://github.com/samesense/pathopredictor Causal variant Ensemble method: https://github.com/gifford-lab/EnsembleExpr/ eCAVIAR: probability that a variant is causal for both QTL and eQTL http://genetics.cs.ucla.edu/caviar/ qCat: https://github.com/dleelab/qcat Disease-associated risk variants: https://sites.google.com/site/emorydivan/ Predicting gene targets from GWAS summary statistics https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4979185/ Orion: https://github.com/igm-team/orion-public Chromatin States GenoSTAN: http://bioconductor.org/packages/release/bioc/html/STAN.html R package for predicting chromatin states from histone marks across conditions https://github.com/ataudt/chromstaR Rule-based: http://www.statehub.org/ Hierarchical HMM: https://github.com/gcyuan/diHMM Basenji: https://github.com/calico/basenji CMint: https://bitbucket.org/roygroup/cmint Enhancers Prediction of enhancer strength from sequenced http://bioinformatics.hitsz.edu.cn/iEnhancer-2L Prediction of core cell type-specific TFs from super enhancers https://bitbucket.org/young_computation/crcmapper Prediction of superenhancers https://github.com/asntech/improse Deep learning-based: BiRen: https://github.com/wenjiegroup/BiRen DeepCAPE: https://www.biorxiv.org/content/early/2018/08/24/398115 https://www.biorxiv.org/content/early/2018/02/14/264200 Coding mutations Predict mutation effects from sequence covariation: https://marks.hms.harvard.edu/evmutation/ Predicting loss-of-function from expression: https://www.nature.com/articles/s41467-017-00443-5 Prediction of missense variants: https://www.biorxiv.org/content/early/2018/02/02/259390 Disease-specific functional prediction https://sites.google.com/site/emorydivan/ Impact of coding SNPs: http://pantherdb.org/tools/csnpScoreForm.jsp Predict disease risk from GWAS summary statistics: https://github.com/yiminghu/AnnoPred Regulatory variants/TF binding LedPred: prediction of regulatory sequences from ChIP-seq https://github.com/aitgon/LedPred GERV: prediction of regulatory variants that affect TF inding http://gerv.csail.mit.edu/ Score variant deleteriousness: http://cadd.gs.washington.edu/ BASSET: prediction of sequence activity https://github.com/davek44/Basset DanQ: hybrid convolutional and recurrent neural network model for predicting the function of DNA de novo from sequence http://github.com/uci-cbcl/DanQ DeepSequence: Generative model https://github.com/debbiemarkslab/DeepSequence Can also be used for sequence clustering LINSIGHT Protein binding affinity: https://bitbucket.org/wenxiu/sequence-shape.git Change in local frustration index: https://github.com/gersteinlab/frustration TFImpute: multi-task learning from ChIP-seq data across factors and tissues to impute TF binding for an unassayed tissue/factor combination: https://bitbucket.org/feeldead/tfimpute PPI https://www.ncbi.nlm.nih.gov/research/mutabind/index.fcgi/ Multiple instance learning of TF binding: http://www.cs.utsa.edu/~jruan/MIL/ Cell type-specific: https://github.com/uci-cbcl/FactorNet Predict TF binding from ATAC-Seq using deep neural network: https://github.com/hiranumn/deepatac CentiSNPs: http://genome.grid.wayne.edu/centisnps/ Benchmark: https://github.com/Oncostat/BenchmarkNCVTools PathoPredictor: https://github.com/samesense/pathopredictor Cell-type agnostic regulatory activity prediction from ENCODE: http://screen.encodeproject.org/ Segway functional scores: https://noble.gs.washington.edu/proj/encyclopedia/ Predict effect of variants on epigenetic factors (chromatin accessibility, histone marks, etc): http://deepfigv.mssm.edu/downloads.html HaploReg: http://www.broadinstitute.org/mammals/haploreg/haploreg.php Consensus approaches: PredictSNP2: https://loschmidt.chemi.muni.cz/predictsnp2/ PRVCS: http://jjwanglab.org/PRVCS/ Queryor: http://queryor.cribi.unipd.it/cgi-bin/queryor/mainpage.pl Using epigenomic data https://github.com/mulin0424/cepip Tissue-specific effects on expression: https://github.com/FunctionLab/ExPecto Methylation CpGenie: predicts methylation from sequence, predicts impact of variants on nearby methylation https://github.com/gifford-lab/CpGenie Chromatin accessibility SCM: http://scm.csail.mit.edu/ TFBS Predict TF binding affinities using open chromatin + PWMs: https://github.com/schulzlab/TEPIC LR-DNAse: TFBS prediction using features derived from DNase-seq: http://biorxiv.org/content/early/2016/10/24/082594 Classification of cis-regulatory modules: https://github.com/weiyangedward/IMMBoost Imputation: https://github.com/tdurham86/PREDICTD Variable selection for random forest: https://github.com/jomayer/SMuRF Security Secret sharing schemes for keeping patient ID secret while being able to reconstruct it from other identifiers: https://pdfs.semanticscholar.org/0307/48be9820512a1d5582351552bd0452711296.pdf Sequencing Protocols Single cell Simultaneous RNA and methylation (and inference of CNV): http://www.nature.com/cr/journal/vaop/ncurrent/full/cr201623a.html Simultaneous RNA and methylation (scM T-seq): http://www.nature.com/nmeth/journal/v13/n3/full/nmeth.3728.html Simultaneous RNA and protein measurements: http://www.sciencedirect.com/science/article/pii/S2211124715014345 CRISPR-DS: Uses CRISPR/Cas9 excision of target sequences to improve library quality relative to hybrid capture: https://www.biorxiv.org/content/early/2017/10/23/207027 Simulation InterSIM: simulate correlated multi-omics data https://cran.r-project.org/web/packages/InterSIM/index.html Simulate capture sequencing: https://github.com/mdcao/capsim Database of simulated tumor genomes (created with BamSurgeon on NA12878 background): https://www.biorxiv.org/content/early/2018/02/07/261503 Simulation of cancer genomes: https://github.com/rsemeraro/XomeBlender Simulate CNVs: https://github.com/pughlab/bamgineer Simulate mutations in a BAM file: https://github.com/adamewing/bamsurgeon GWAS: https://github.com/chr1swallace/simGWAS Variant annotation WGSA pipeline https://sites.google.com/site/jpopgen/wgsa/ SnpEff: http://snpeff.sourceforge.net/ Normalization of SNP ID\'s from literature: https://github.com/rockt/SETH HAIL: https://hail.is/ Tissue-specific https://github.com/kevinVervier/TiSAn VCF visualization with Circos plot: http://legolas.ariel.ac.il/~tools/CircosVCF/ HGVS This is a standard notation for variants http://varnomen.hgvs.org/ Toolkit for working with HGVS-formatted variants: https://github.com/biocommons/hgvs Integration of multiple predictive measures of mutation deleterious effect: https://www.ncbi.nlm.nih.gov/research/snpdelscore/ Varsome: database that aggregates variant information from many sources https://varsome.com/ Provides a REST API that can be queried with dbSNP or HGVS IDs Constrained coding regions: https://github.com/quinlan-lab/ccrhtml Database of human chromosomal fragile sites: http://webs.iiitd.edu.in/raghava/humcfs FunSeq2: http://funseq2.gersteinlab.org Deep learning-based: https://www.biorxiv.org/content/early/2017/12/18/235655.1 Search engine for indexing and intersecting with large numbers of annotation BED/VCF files: https://github.com/ryanlayer/giggle/ Clinical annotation: https://github.com/ding-lab/CharGer Gene panel/disease-specific annotation and filtering: https://www.ebi.ac.uk/gene2phenotype/g2p_vep_plugin Cravat: https://github.com/KarchinLab/open-cravat/tree/master/cravat Individual, per-gene pathogenicity scores: https://github.com/UoS-HGIG/GenePy SV AnnotSV: http://lbgi.fr/AnnotSV/ Curation/annotation by depth: https://github.com/brentp/duphold Sequence Analysis General-purpose Google Genomics R API: https://followthedata.wordpress.com/2015/02/05/notes-on-genomics-apis-2-google-genomics-api/ k-mer counting khmer: https://github.com/ged-lab/khmer https://docs.google.com/presentation/d/1biQmLkwPlCOA56mNZdUAiXa1OyGE0qyvI2nvU0qlOIE/mobilepresent?pli=1 slide=id.p58 KCMBT: https://github.com/abdullah009/kcmbt_mt KAT https://github.com/TGAC/KAT ntCard https://github.com/bcgsc/ntCard KMC3 https://github.com/refresh-bio/KMC Encoding counts with kmer data: https://github.com/lzhLab/kmcEx Gerbil: GPU accelerated, outputs count table https://github.com/uni-halle/gerbil Toolkit for working with unique kmers: https://github.com/shenwei356/unikmer k-mer hashing: https://github.com/czbiohub/kmer-hashing Density-based clustering: https://bitbucket.org/jerry00/densitycut_dev chopBAI: segment BAM indexes by region for faster access https://github.com/DecodeGenetics/chopBAI GFFutils: http://daler.github.io/gffutils/ R package for aligned chromatin-oriented sequencing data: https://cran.r-project.org/web/packages/Pasha/ MMR: resolve multi-mapping reads https://github.com/ratschlab/mmr BAMQL: query language for extracting reads from BAM files https://github.com/BoutrosLaboratory/bamql SAMBAMBA: samtools alternative BAMtools: another samtools alternative, plus some additional tools https://github.com/pezmaster31/bamtools/wiki DeepTools: more useful SAM/BAM operations http://deeptools.readthedocs.io/en/latest/content/list_of_tools.html bedtools http://bedtools.readthedocs.io/en/latest/ bedops alternative/additional BED operations http://bedops.readthedocs.io/en/latest/ Normalization: GLScale: https://github.com/allenxhcao/glscale QSmooth: https://github.com/stephaniehicks/qsmooth ORNA: https://github.com/SchulzLab/ORNA Suquan: https://github.com/jpvert/suquan Demultiplexing/deduping barcoded reads w/ UMIs: http://gbcs.embl.de/portal/tiki-index.php?page=Je Hardware acceleration of alignment (requires $5k FPGA module): https://github.com/BilkentCompGen/GateKeeper Detection and removement of barcode swapping (issue on Illumina sequencers that used patterned flow cells: https://github.com/MarioniLab/BarcodeSwapping2017 Data processing pipelines for many types of omics data, built using NextFlow and Singularity: https://github.com/c-guzman/cipher-workflow-platform Index and fetch data from BGZF-compressed files. Segment lfitover: https://github.com/baudisgroup/segment-liftover Recover unaligned reads: https://github.com/VCCRI/Scavenger Intersection and visualization of multiple gene/region sets: https://bitbucket.org/CBGR/intervene Mantis: index of raw-read datasets for efficient and exacty queries https://github.com/splatlab/mantis Machine learning method for determining sequence identity: https://github.com/TulsaBioinformaticsToolsmith/FASTCAR Progressive MSA (DNA or protein) with indel evolution: https://github.com/acg-team/ProPIP Tools to create ENCODE blacklists, and pre-computed blacklists for model organisms: https://github.com/Boyle-Lab/Blacklist Server for reference sequences and indices: https://github.com/databio/refgenie Demultiplexing Axe: https://axe-demultiplexer.readthedocs.io/en/latest/usage.html FuzzySplit: https://github.com/Daniel-Liu-c0deb0t/Java-Fuzzy-Search QC Qualimap2: http://qualimap.bioinfo.cipf.es/ Determine whether two BAM files are from the same source: https://bitbucket.org/sacgf/bam-matcher DOGMA: Measure completeness of a transcriptome or proteome assembly https://ebbgit.uni-muenster.de/domainWorld/DOGMA/ Identify and remove UMI sequences from reads: https://github.com/CGATOxford/UMI-tools Integrated report from multiple tools: http://multiqc.info/ Batch effects: BatchQC: https://github.com/mani2012/BatchQC Correct batch effects using residual neural net: https://github.com/ushaham/BatchEffectRemoval AlmostSignificant: https://github.com/bartongroup/AlmostSignificant Genetic relatedness from raw reads: https://github.com/kdmurray91/kwip https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/Software.cgi Fast coverage estimate from BAM index: https://github.com/brentp/goleft/tree/master/indexcov Detecting sample swaps: https://github.com/PapenfussLab/HaveYouSwappedYourSamples QC Fail articles: Patterned flow cells (HiSeq 3000+) have high rates of optical duplicates: https://sequencing.qcfail.com/articles/illumina-patterned-flow-cells-generate-duplicated-sequences/ SamStat: http://samstat.sourceforge.net/ Fingerprints: http://db.systemsbiology.net/gestalt/genome_fingerprints/ Fastq.bio: http://fastq.bio/ ChronQC: https://github.com/nilesh-tawari/ChronQC Read Origin Protocol: https://github.com/smangul1/rop QC on Illumina run folder: https://github.com/Molmed/checkQC New diploid reference for benchmarking: https://www.biorxiv.org/content/early/2017/11/22/223297 Historical tracking: https://github.com/nilesh-tawari/ChronQC Estimate library complexity: http://smithlabresearch.org/software/preseq/ Protocol-specific pre-processing: https://github.com/lh3/pre-pe fluff: https://fluff.readthedocs.io/en/latest/introduction.html GenMap: compute mappability; pre-built indices https://github.com/cpockrandt/genmap Quantification and normalization of coverage peaks: https://github.com/ncbi/BAMscale ChIP-seq Pre-processing Quality assessment: https://github.com/rnakato/SSP Allocation of multi-mapping reads: https://github.com/keleslab/permseq Peak calling GC-aware peak caller: https://bioconductor.org/packages/devel/bioc/html/gcapc.html GenoGAM peak caller: https://master.bioconductor.org/packages/3.3/bioc/html/GenoGAM.html De-noising: https://github.com/kundajelab/TF_chipseq_pipeline Peak discretizer (merging of replicates): https://github.com/nanakiksc/zerone Compute error: https://github.com/tdhock/PeakError Specifically for histone modifications: https://github.com/Bohdan-Khomtchouk/SUPERmerge ChIPWig: https://github.com/vidarmehr/ChIPWig hiddenDomains: https://sourceforge.net/projects/hiddendomains/ Network-based identification of relationships among ChIP-seq data sets: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0925-0 Motif assessment: http://www.bioinf.ict.ru.ac.za/ Chilin: https://github.com/cfce/chilin Web-based tool to compute enrichment at a variety of genomic features: http://liulab.dfci.harvard.edu/CEAS/ Database of labeled ChIP-seq peaks: http://cbio.ensmp.fr/thocking/chip-seq-chunk-db/ (error of peak calls computed using https://github.com/tdhock/PeakError) EM algorithm for cooperatively bound TFs: https://github.com/vishakad/cpi-em Find different modes of binding: https://narlikarlab.github.io/DIVERSITY/ Functional analysis FunChIP: http://bioconductor.org/packages/FunChIP annoPeak: https://github.com/XingTang2014/annoPeak Pipelines ChIPSeqFPro: https://github.com/milospjanic/ChIPSeqFPro Shape motifs: https://github.com/h-samee/shape-motif K-mer-based alternative to PWMs for predicting TF binding sites: http://groups.csail.mit.edu/cgs/gem/kmac/ Recalibrate p-values from peak callers: https://github.com/theodorejperkins/RECAP Branch of MACS for improved weighting of controls: https://github.com/aawdeh/MACS/tree/WACS Chromatin accessibility DNase footprinting: https://github.com/ajank/Romulus HINT: http://costalab.org/publications-2/dh-hmm/ (was best out of 10 compared tools in recent NatMeth paper) HINT-ATAC: method specifically for ATAC-seq footprinting https://github.com/CostaLab/reg-gen ALTRE: https://mathelab.github.io/ALTRE/vignette.html Predict TF binding affinities using open chromatin + PWMs: https://github.com/schulzlab/TEPIC LR-DNAse: TFBS prediction using features derived from DNase-seq: http://biorxiv.org/content/early/2016/10/24/082594 Identify accessible chromatin from NOMe-seq https://sourceforge.net/projects/came/ Nucleotide-specific bias adjustment: https://github.com/txje/sequence-bias-adjustment ATAC-seq peak caller: https://github.com/LiuLabUB/HMMRATAC esATAC: ATAC-Seq pipeline https://bioconductor.org/packages/release/bioc/html/esATAC.html Predict targets of accessible regions: https://github.com/cole-trapnell-lab/cicero-release ATAC-Seq QC: https://bioconductor.org/packages/release/bioc/html/ATACseqQC.html Correcting transposition bias in ATAC-seq: https://www.biorxiv.org/content/biorxiv/early/2019/01/22/525808.full.pdf AIAP: https://www.biorxiv.org/content/10.1101/686808v1 Has associated viewer https://github.com/lidaof/qATACviewer/tree/localjson https://qa.targetepigenomics.org/ Chromatin Interactions Model 3D chromosome structure from Hi-C contact maps + optional FISH constraints: https://github.com/yjzhang/FISH_MDS.jl, https://github.com/yjzhang/3DC-Browser Predict enhancer targets: https://github.com/shwhalen/targetfinder Predicting TADs from histone modifications: https://cb.utdallas.edu/CITD/index.htm#ajax=home Resolution enhancement: http://dna.cs.miami.edu/HiCNN/ Binary storage format for interaction matrix: https://github.com/mirnylab/cooler DNA Filtering Removing redundant reads from deep sequencing data: https://git.informatik.uni-kiel.de/axw/Bignorm Clustering PE UMI-tagged reads: https://github.com/vpc-ccg/calib Error correction BFC: https://github.com/lh3/bfc RECKONER http://sun.aei.polsl.pl/REFRESH/index.php?page=projects project=reckoner subpage=about MultiRes - says it\'s for viral populations; not sure if applicable to humans: https://github.com/raunaq-m/MultiRes http://github.com/Malfoy/BCOOL Correction only of reads that contain repetative sequences: https://github.com/biointec/browniecorrector Duplicate removal Pardre: https://sourceforge.net/projects/pardre/ Alignment Recommendations: https://github.com/CCDG/Pipeline-Standardization These are optimized for germline variant calling, however there are a few interesting points BWA MEM has a hidden option that enables \"deterministic alignment results\" (-K 100000000) They recommend binning of quality scores for greater compression Scores 2-6 correspond to Illumina error codes and are left as-is 10, 20, 30 Round scores to the nearest value in probability space The have some recommendations for standardizing headers AGA: aligner specifically for coding sequences that takes into account the translated protein sequence: https://github.com/emweb/aga Scaling alignment to massively parallel processors https://www.biorxiv.org/content/early/2017/10/24/205328 Parallelizing hundreds of threads on Xeon Phi (64-72 core processors) Alignment speed-up using PIM technologies: https://arxiv.org/abs/1711.01177 Whisper Accelerates alignment by pre-sorting reads by sequence, and only aligning non-duplicates http://sun.aei.polsl.pl/REFRESH/Whisper Paper: https://www.biorxiv.org/content/early/2017/12/28/240358?rss=1 utm_source=dlvr.it utm_medium=twitter Align simultaneously against multiple reference genomes http://1001genomes.org/software/genomemapper.html Compressed reference-based alignment: http://groups.csail.mit.edu/cb/cora/ Compression and querying of aligned haplotype data: https://github.com/richarddurbin/pbwt LibGaba: https://github.com/ocxtal/libgaba Minimap2: https://github.com/lh3/minimap2 Long read https://github.com/MohammadJRS/SureMap https://github.com/isovic/graphmap https://github.com/ocxtal/minialign Graph-based: https://github.com/maickrau/GraphAligner Glia (mainly for local realignment): https://github.com/ekg/glia Raptor: https://github.com/isovic/raptor https://github.com/lh3/minigraph MECAT: https://github.com/xiaochuanle/MECAT Correct sex chromosome-related errors/artifacts: https://github.com/WilsonSayresLab/XYalign Align samples with a Y chromosome to a YPAR-masked genome, and samples without a Y chromosome to a Y-masked genome https://www.biorxiv.org/content/10.1101/668376v1 Major-allele SNP references for Bowtie: http://bowtie-bio.sourceforge.net/bowtie2/index.shtml Other Optical computing: https://www.optalysys.com/ FPGA: http://edicogenome.com/dragen-bioit-platform/ Selecting variants for graph genome: https://www.biorxiv.org/content/early/2018/04/30/311720 Assembly Build de Bruijn Graph from multiple genomes: https://github.com/medvedevgroup/TwoPaCo Align to a de Brujn graph: https://github.com/Malfoy/BGREAT Cosmo/VARI: Assembler using succinct colored de Bruijn graphs to encode population information https://github.com/cosmo-team/cosmo/tree/VARI Streaming: https://github.com/Shamir-Lab/Faucet BFGraph: https://github.com/pmelsted/bfgraph Spark-based: https://github.com/BioHPC/SORA/wiki Polishing: https://github.com/bcgsc/ntedit Long-read https://github.com/lbcb-sci/ra Post-alignment Base quality recalibration: https://github.com/swainechen/lacer Indel realignment (also supports RNAseq): https://github.com/mozack/abra2 Error correction Racon: https://github.com/isovic/racon Mageri: https://github.com/mikessh/mageri ReadStomper: https://github.com/gringer/bioinfscripts/blob/master/readstomper.pl ProDuSe: https://github.com/morinlab/ProDuSe/ AGeNT: https://www.genomics.agilent.com/en/NGS-Data-Analysis-Software/AGeNT/?cid=AG-PT-154 tabId=prod2570007 Prepare for variant calling: https://github.com/ExaScience/elprep replacement for sort, markdup, BQSR Fast pileup of targeted regions in C/Python: https://github.com/brentp/hileup Lossy compression of quality scores: https://github.com/jkbonfield/crumble Denoising (error correction + quality score adjustment): https://github.com/ihwang/SAMDUDE Variant calling Matching variant sets: https://github.com/medvedevgroup/varmatch Post-processing variant calls to determine whether variants at regions with alternative loci have allele(s) from an alternate locus: https://github.com/charite/asdpex Filtering low-frequency variants that likely result from DNA damage: https://github.com/eilslabs/DKFZBiasFilter Reference-free: Kevlar: https://github.com/dib-lab/kevlar RUFUS: https://github.com/jandrewrfarrell/RUFUS DeepVariant is now open source DeepVariant: https://github.com/google/deepvariant https://blog.dnanexus.com/2017-12-05-evaluating-deepvariant-googles-machine-learning-variant-caller/?utm_source=email utm_medium=mailchimp utm_campaign=december_newsletter https://blog.dnanexus.com/2018-01-16-evaluating-the-performance-of-ngs-pipelines-on-noisy-wgs-data/ Currently it doesn\'t handle somatic calling Deep learning will increase accuracy in the nest GATK release: https://gatkforums.broadinstitute.org/gatk/discussion/10996/deep-learning-in-gatk4 Benchmarking GA4GH/PrecisionFDA guidelines and tools for germline variants https://www.biorxiv.org/content/early/2018/02/23/270157 https://github.com/ga4gh/benchmarking-tools https://platform.dnanexus.com/login?scope=%7B%22full%22%3A+true%7D redirect_uri=https%3A%2F%2Fprecision.fda.gov%2Freturn_from_login client_id=precision_fda_gov Using trios: https://github.com/sbg/geck Ococo Variant calling is performed in a streaming fashion, with evidence for variation being updated from each read as it is mapped. Theoretically, new evidence can be ignored once the confidence level reaches a certain threshold. In practice, they show this is about 10% of reads. However, they only deal with SNPs. It would be interesting to investigate whether: This can be extended to somatic variant calling This can work with rare variants (eg cfDNA) https://github.com/karel-brinda/ococo Paper: https://arxiv.org/pdf/1712.01146.pdf Distributed variant calling using Spark: http://bdgenomics.org/projects/ De-novo variant caller: https://github.com/bgm-cwg/novoCaller Create population-specific blacklists: http://lab.rockefeller.edu/casanova/BL/programs From long/linked reads: https://github.com/pjedge/longshot https://github.com/maiziex/Aquila Scotch: ML approach for calling intermediate-sized indels: https://github.com/AshleyLab/scotch Encoding variant information in BWT: https://www.uni-ulm.de/in/theo/research/seqana/ Detect variants by comparing reads between samples without genome alignment: https://github.com/akiomiyao/ped Genotyping ebGenotyping: https://cran.r-project.org/web/packages/ebGenotyping/ebGenotyping.pdf FastGT: http://bioinfo.ut.ee/FastGT/ Compression of genotype data: http://sun.aei.polsl.pl/REFRESH/gtc Base quality recalibration: https://github.com/swainechen/lacer Mosaic variants: https://github.com/abyzovlab/Leucippus HISAT-genotype: http://ccb.jhu.edu/hisat-genotype/index.php/Main_Page Trio-based concordance: https://github.com/sbg/VBT-TrioAnalysis Using a graph reference: https://github.com/bioinformatics-centre/BayesTyper https://github.com/AlgoLab/malva SVs/CNV calling Score SVs based on predicted functional impact https://github.com/lganel/SVScore Pipelines for CNV/SV calling: MARATHON: https://github.com/yuchaojiang/MARATHON Anaconda: https://mcg.ustc.edu.cn/bsc/ANACONDA/ CODEX2: https://github.com/yuchaojiang/CODEX2 Ximmer: https://github.com/ssadedin/ximmer STIX: A tool for determining the frequency of SVs in population data: https://github.com/ryanlayer/stix Tool for online review of SVs: https://github.com/jbelyeu/SV-plaudit Using off-target reads to call CNVs: https://ep70.eventpilotadmin.com/web/page.php?page=IntHtml project=ASHG17 id=170120230 GRIDSS: detects rearrangements using du Bruijn graph assembly: https://github.com/PapenfussLab/gridss ichorCNA: https://github.com/broadinstitute/ichorCNA indelope: https://github.com/brentp/indelope MultiGEMS: https://github.com/cui-lab/multigems WHAM: CNV caller https://github.com/zeeev/wham Identification of mosic events: https://github.com/asifrim/mrmosaic This may be useful for calling allele-specific (i.e. heterozygous) copy number variation: https://drive.google.com/open?id=0B7E7HSjQ-SumQmlPc1Z0aUR5Sk0 Benchmarking of pipelines for calling complex variants: https://www.biorxiv.org/content/early/2017/11/23/218529 Manta and novoBreak performed best in DREME challenge for SV calling: https://www.biorxiv.org/content/early/2017/11/25/224733 https://github.com/Mesh89/SurVIndel Allele-specific: Falcon: allele-specific copy number changes https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4344483/ AS-GENSENG: https://sourceforge.net/projects/asgenseng/ ASCAT: https://github.com/Crick-CancerGenomics/ascat/tree/master/ASCAT http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0075350 TAPS (for affy arrays): https://genomebiology.biomedcentral.com/articles/10.1186/gb-2011-12-10-r108 Bamgineer: simulate allele-specific CNVs https://github.com/pughlab/bamgineer Mappability tracks, for normalizing CNV calls http://genome-test.soe.ucsc.edu/cgi-bin/hgTrackUi?hgsid=392476445_0mwMQdJgiLvv8WtNo4YktFt41IkI c=chr1 g=umap WALDO: detection of LINE amplifications http://www.pnas.org/content/115/8/1871.short Smoove: https://github.com/brentp/smoove Svaba: https://github.com/walaj/svaba Consensus caller: https://github.com/TheJacksonLaboratory/SVE Randomized algorithm to speed up CNV calling: https://sourceforge.net/projects/genseng/ CNV calling from plasma: https://www.biorxiv.org/content/early/2018/03/28/290171 Ensemble caller: https://github.com/HaoKeLab/ensembleCNV Parliament2 Different variant callers excel at calling different types of SVs: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1720-5 Population-scale: https://github.com/hall-lab/svtools Graph-based genotyping: https://github.com/illumina/paragraph Deep learning-based callers: https://github.com/CSuperlei/DeepSV Specifically for duplications and deletions: https://github.com/tomh1lll/dudeml Fusion detection: https://github.com/sgilab/JuLI Alignment-free SV genotyping: https://github.com/Parsoa/NebulousSerendipity Genotyping tandem repeats: https://github.com/mcfrith/tandem-genotypes Linked read: https://github.com/sgreer77/gemtools Savvy: uses off-target reads https://github.com/rdemolgen/SavvySuite Exome/targeted Exon-level CNV calling in exome/targeted seqeuencing: https://github.com/SD-Genomics/DeviCNV Ensemble method: https://github.com/girirajanlab/CN_Learn Reference sample selection: https://www.biorxiv.org/content/early/2018/11/25/478313.1 Generate consensus reads (optionally using UMIs): https://github.com/OpenGene/gencore Breakpoint prediction: https://github.com/SinOncology/Break Atlas-CNV: https://github.com/theodorc/atlas-cnv STR Pipeline to estimate mutational parameters for every STR in the human genome: https://www.nature.com/ng/journal/v49/n10/full/ng.3952.html MIRMMR: MSI caller https://github.com/ding-lab/MIRMMR Uses methylation and mutation information Targeted STR profiling: http://darwin.informatics.indiana.edu/str/ Krait: integrated microsatellite detection and primer design https://github.com/lmdu/krait https://www.biorxiv.org/content/early/2017/11/18/221754 MANTIS: https://github.com/OSU-SRLab/MANTIS MSIsensor: https://github.com/ding-lab/msisensor https://www.biorxiv.org/content/early/2018/01/10/246108 They generate a reference STR database using PacBio They provide software to estimate STR number from short-read data using the reference database This could be used to quickly extract STR-containing sequences from a BAM: https://github.com/rkmlab/perf AdVNTR: https://github.com/mehrdadbakhtiari/adVNTR Tandem genotypes: https://github.com/mcfrith/tandem-genotypes lobSTR: http://melissagymrek.com/lobstr-code/ GangSTR: Genotyping STR longer than read length: https://github.com/gymreklab/GangSTR Alignment-free STR genotyping: https://github.com/jbudis/dante HLA arcasHLA: https://github.com/RabadanLab/arcasHLA mtDNA https://github.com/linzhi2013/MitoZ Variant filtering GARFIELD: Using deep learning model to filter false-positive variants https://www.biorxiv.org/content/biorxiv/early/2017/09/11/149146.full.pdf Pre-trained (using GIAB) Illumina and IonTorrent models are here: https://github.com/gedoardo83/GARFIELD-NGS ISOWN Distinguishes somatic from germline variants https://github.com/ikalatskaya/ISOWN Paper: https://genomemedicine.biomedcentral.com/articles/10.1186/s13073-017-0446-9 Bystro: annotation and filtering (web only) https://bystro.io/ https://genomebiology.biomedcentral.com/articles/10.1186/s13059-018-1387-3 SevenBridges\': https://github.com/sbg/smart-variant-filtering Skyhawk: https://github.com/aquaskyline/Skyhawk This paper describes criteria that identify 100% of FP (as determined by Sanger sequencing) in a large clinical dataset https://www.biorxiv.org/content/early/2018/05/31/335950 Regions subject to systematic bias: https://www.biorxiv.org/content/10.1101/679423v2 Another RF classifier: https://github.com/avallonking/ForestQC Repeat calling REPdenovo: https://github.com/Reedwarbler/REPdenovo Pipelines SpeedSeq: alignment/annotation pipeline - https://github.com/hall-lab/speedseq GenomeVIP: cloud-based pipeline https://github.com/ding-lab/GenomeVIP/ superFreq: https://github.com/ChristofferFlensburg/superFreq GROM: https://osf.io/6rtws/ Amplicon Canary: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1950-z pyAmpli: https://mbeyens.github.io/pyAmpli/ DNAScan https://github.com/KHP-Informatics/DNAscan Align with HiSAT2; realign soft-clipped and unaligned reads with BWA MEM due to poor performance of indel calling from HISAT2-aligned data Variant calling with Freebayes, extract indel positions and realign with GATK HC, perform hard filtering Manta and Expansion Hunter for SVs Ancestry and kinship analysis AKT: http://illumina.github.io/akt/ MixFit: http://www.geenivaramu.ee/en/tools/mixfit Genotype clustering and ethnicity prediction using Deep Belief Networks: https://arxiv.org/abs/1805.12218 Truffle: IBD and ancestry analysis https://adimitromanolakis.github.io/truffle-website/ ngsRelate: https://github.com/ANGSD/ngsRelate Fast D-statistics: https://github.com/millanek/Dsuite Compute genome fingerprint from VCF: https://github.com/gglusman/genome-fingerprints Phasing/Haplotyping Eagle2: https://data.broadinstitute.org/alkesgroup/Eagle/ Using HiC+partial haplotypes: https://github.com/YakhiniGroup/SpectraPh PhaseME http://beehive.cs.princeton.edu/wiki/phaseme/ HapCut2 (unclear if this works with standard Illumina WGS) https://github.com/vibansal/HapCUT2 hap.py: Illumina tool for identifying local haplotypes (also does genotype call comparison) https://github.com/Illumina/hap.py/blob/master/doc/happy.md This might be useful in multiBP merging and in detecting contamination For single individuals: https://github.com/jcna99/PEATH https://arxiv.org/abs/1801.09864 Alignment-free: Uses kmer frequency: https://github.com/paudano/kestrel IVC https://github.com/namsyvo/IVC Integrates alignment and variant calling Uses known variant information Germline, Illumina paired-end only Using decision tree: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2147-9 PEATH: https://github.com/jcna99/PEATH SNP partitioning by halpotype block: https://bioconductor.org/packages/release/bioc/html/gpart.html Amplicon Pisces: https://github.com/Illumina/Pisces/ Graph PanVC: https://gitlab.com/dvalenzu/PanVC Graph genome alignment and variant calling on SevenBridges: https://www.sevenbridges.com/graph/ Non-Illumina Adapter between aligners and downstream tools for handling PacBio quality values https://github.com/mchaisso/pbsamstream BGI compared to Illumina: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190264 Other VCF compression and data extraction: https://github.com/kedartatwawadi/GTRAC Run length encoded multi-sample BWT + server: https://github.com/wtsi-svi/ReadServer XYalign: sex chromosome copy number estimation from sequencing https://github.com/SexChrLab/XYalign Liftover between genome assemblies: https://github.com/verilylifesciences/genomewarp Footprinting Centipede: http://rajanil.github.io/msCentipede/ NucID: nucleosome positioning from DNase-seq https://jianlingzhong.github.io/NucID/ SeqGL: predict TF binding from DNase/ATAC-seq https://bitbucket.org/leslielab/seqgl/wiki/Home DeFCoM: https://bitbucket.org/bryancquach/defcom Metagenomics Alignment-free functional binning and abundance estimation: https://github.com/snz20/carnelian Map reads against redundant databases: https://bitbucket.org/genomicepidemiology/kma Taxonomic classificatino using pseudoalignment: https://github.com/mreppell/Karp Iterative bloom filter and Yara mapper for rapid updating and mapping to large metagenomic databases: https://gitlab.com/pirovc/dream_yara/ Spaced seed hashing: https://bitbucket.org/samu661/fish/overview SNV calling: https://github.com/tseemann/snippy Methylation QC ME-plot: Error detection and correction in bisulfite-converted sequencing reads https://github.com/joshuabhk/methylsuite Correction for cell-type composition: http://www.cs.tau.ac.il/~heran/cozygene/software/refactor.html Mapping https://github.com/chhylp123/BitMapperBS Arioc: GPU-accelerated https://github.com/RWilton/Arioc BatMeth2: https://github.com/GuoliangLi-HZAU/BatMeth2 SNP calling BS-SNPer: fast SNP calling from bisulfite-converted sequencing reads https://github.com/hellbelly/BS-Snper Differential methylation https://github.com/tare/LuxGLM/ Metilene: Calling differential methylation http://www.bioinf.uni-leipzig.de/Software/metilene/ Using VAE: https://www.biorxiv.org/content/early/2018/11/07/433763 Association MACAU: Mixed-model association http://www.xzlab.org/software.html GEM: R package for meQTL and EWAS https://bioconductor.org/packages/devel/bioc/html/GEM.html Microarray Call CNVs from methylation array data: https://github.com/mknoll/cnAnalysis450k Other Compression tool for bedMethyl files: https://github.com/jianhao2016/METHCOMP Reference-free bisulfite sequence comparison: https://github.com/thomasvangurp/epiGBS Summarization of multiple bedgraph files: https://github.com/CompEpigen/methrix MNase-seq Alternative/differential nuleosome positioning: https://github.com/airoldilab/cplate Nanopore Minion_qc: https://github.com/roblanf/minion_qc Clinical application: https://www.nature.com/articles/s41467-017-01343-4 Signal-level barcode demux: https://github.com/rrwick/Deepbinner Scrubbing low-quality reads: https://bitbucket.org/berkeleylab/jgi-miniscrub Real-time base calling with read-until: https://github.com/harrisonedwards/RUBRIC https://github.com/nanoporetech/wub Detect analytes from raw event data: https://github.com/nanoporetech/mako Online CNA detection: https://sourceforge.net/projects/nanogladiator/ RNA Annotations Reassembly and annotation of public data for multi-tissue transcriptome map: http://big.hanyang.ac.kr/CAFE Splice junctions Set of novel (i.e. missing from annotation databases) splice junctions identified from SRA datasets: https://github.com/nellore/intropolis/blob/master/README.md Complete sets of splice junctions in public RNA-seq datasets: http://snaptron.cs.jhu.edu/data/ QC NOISeq - exploratory analysis of read mappings https://www.bioconductor.org/packages/release/bioc/html/NOISeq.html AuPairWise: determine replicability without replicates https://github.com/sarbal/AuPairWise dupRadar: duplications https://www.bioconductor.org/packages/release/bioc/html/dupRadar.html Correcting for RNA quality: https://github.com/jengelmann/FastqPuri Error correction using DBG: https://github.com/Malfoy/BCT Align/quantify Need to re-evaluate HiSat2 + StringTie pipeline https://github.com/gpertea/stringtie RapMap: stand-alone lightweight alignment library: https://github.com/COMBINE-lab/RapMap/tree/SAQuasiAlignment Kallisto is a fast and accurate method for transcript quantification https://pachterlab.github.io/kallisto/ Sleuth is a companion R package for differential expression analysis http://pachterlab.github.io/sleuth/ Different models can be used in Sleuth to, for example, perform time-course experiments http://nxn.se/post/134227694720/timecourse-analysis-with-sleuth tximport: R package for aggregating transcript-level quantifications for gene-level analysis: http://f1000research.com/articles/4-1521/v1 Wasabi: prepare Salmon/Sailfish output for Sleuth https://github.com/COMBINE-lab/wasabi featureCounts: read summarization http://bioinf.wehi.edu.au/featureCounts/ D-GEX: Quantification of whole-transcriptome gene expression from landmark genes https://github.com/uci-cbcl/D-GEX Faster version of HTSeq/featureCount: https://github.com/qinzhu/VERSE Quantification using both structure and abundance information: https://pypi.python.org/pypi/rsq Improve transcript quantification by integrating PolII ChIP-seq data: https://github.com/pliu55/RSEM/tree/pRSEM Hera: simultaneous alignment, quantification, and fusion detection https://github.com/bioturing/hera Aligner calibraiton: https://bitbucket.org/irenerodriguez/fbb Quantification using k-mers unique to each gene: https://github.com/informationsea/Matataki Read assignment for multi-mapping reads: http://gitlabscottgroup.med.usherbrooke.ca/scott-group/coco TPMCalculator: https://github.com/ncbi/TPMCalculator Convert between BAM and TCC: https://github.com/pachterlab/bam2tcc Long read: https://github.com/hitbc/deSALT Correction/Normalization Choosing normalization methods: https://arxiv.org/abs/1609.00959 TDM: cross-platform normalization https://github.com/greenelab/TDMresults alpine: corrects for fragment sequence bias https://github.com/mikelove/alpine/blob/master/vignettes/alpine.Rmd Filter out lowly-expressed transcripts prior to quantification decreases FP rate: http://www.genomebiology.com/2016/17/1/12 Partition variance between biological and technical sources: https://www.bioconductor.org/packages/3.3/bioc/vignettes/variancePartition Quantify and correct for uncertainty in abundance estimates: https://github.com/PSI-Lab/BENTO-Seq Simultaneous isoform discovery and quantification across multiple samples: http://cbio.ensmp.fr/flipflop R package to compare normalization methods: https://github.com/Edert/NVT Filtering and tissue-aware normalization: http://bioconductor.org/packages/release/bioc/html/yarn.html Bias correction for transcript abundance estimation: https://www.lexogen.com/mix-square-scientific-license/ Replacement for htseq-counts/featureCounts that handles multi-mapping reads: https://bitbucket.org/mzytnicki/multi-mapping-counter https://github.com/wgmao/DataRemix Junction compatibility score for idenitifying genes whose isoform-level abundance estimates differ significantly from expectation: https://github.com/csoneson/annotation_problem_txabundance Count corrector for embedded and multi-mapped genes: http://gitlabscottgroup.med.usherbrooke.ca/scott-group/coco/tree/master Workflows Artemis (RNA-Seq workflow designed around Kallisto): https://github.com/RamsinghLab/artemis Rafalab workflow: https://github.com/ririzarr/rafalib Isolator: https://github.com/dcjones/isolator RNACocktail: https://bioinform.github.io/rnacocktail/ QuickRNASeq: http://baohongz.github.io/QuickRNASeq/ Encode long-read: https://github.com/ENCODE-DCC/long-rna-seq-pipeline eQTL Multi-tissue HT-eQTL https://github.com/reagan0323/MT-eQTL MT-HESS: eQTL analysis across tissues http://www.mrc-bsu.cam.ac.uk/software/ eQTLBMA: cross-tissue eQTL https://github.com/timflutre/eqtlbma Multi-tissue eQTL: https://cran.r-project.org/web/packages/JAGUAR/index.html Multi-tissue, polygenic TWAS: https://github.com/ypark/fqtl Quantile regression approach: https://xiaoyusong.shinyapps.io/QRBT/ Using prior knowledge: https://github.com/redsofa/LassoMP CONDOR: simultaneous cis- and trans-eQTL analysis https://github.com/jplatig/condor log allelic fold change (aFC) to quanitfy effect sizes of eQTL: http://biorxiv.org/content/biorxiv/early/2016/09/30/078717.full.pdf Identify cis mediators of trans-eQTL: http://biorxiv.org/content/early/2016/09/30/078683 https://cran.r-project.org/web/packages/QRank/ Differential expression cjBitSeq: https://github.com/mqbssppe/cjBitSeq/wiki Differential junction usage: https://github.com/hartleys/JunctionSeq TROM: comparison of transcriptomes between species (and maybe between cell/tissue types?) https://cran.r-project.org/web/packages/TROM/index.html Tissue specificity of genes, based on GTEx data: http://genetics.wustl.edu/jdlab/tsea/ Informative priors for Bayesian differential expression analysis using historical data: https://github.com/benliemory/IPBT RNA-enrich: http://lrpath.ncibi.org/ Diferentially expressed region finder (also works for ChIP-seq peaks): www.bioconductor.org/packages/derfinder Differentially expressed pathways using kernel MMD: https://eib.stat.ub.edu/tiki-index.php?page_ref_id=73 Method using dimension-reduced ANOVA: http://homepage.fudan.edu.cn/zhangh/softwares/multiDE/ Correct for hidden sources of variation: https://github.com/sutigit21/SVAPLSseq Alternative method for estimating variances: https://github.com/mengyin/vashr With few replicates: https://figshare.com/s/963e895f812d6f06468a Network sub-pathways: http://bioconductor.org/packages/release/bioc/html/DEsubs.html SDEAP: https://github.com/ewyang089/SDEAP/wiki Local subnetworks enriched for DE genes: https://cran.rstudio.com/web/packages/LEANR/index.html DiscriminantCut: https://github.com/beiyuanzhe/DiscriminantCut Stage-wise DE/DT https://github.com/statOmics/stageR Computing heritability of gene expression: https://cran.r-project.org/web/packages/HeritSeq/index.html Power analysis: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1648-2 Using a beta binomial model with dynamic correction for overdispersion: https://github.com/GuoshuaiCai/BBDG Kmer-based: https://github.com/Transipedia/dekupl Correction for log fold change bias at the extremes: https://bioconductor.org/packages/release/bioc/html/apeglm.html Differential transcript usage Rats: https://github.com/bartongroup/Rats Bayesian extenstion to BitSeq for differential transcript usage: https://github.com/mqbssppe/cjBitSeq From Kallisto/Salmon ECCs: https://github.com/Oshlack/ec-dtu-paper Co-expression/networks GTEx: http://biorxiv.org/content/early/2016/10/02/078741 BicMix: differential co-expression networks http://beehive.cs.princeton.edu/software/ ASE GeniASE: ASE without haplotypes http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4758070/pdf/srep21134.pdf Without phasing: http://lifecenter.sgst.cn/cisASE/ ASElux: https://drive.google.com/open?id=0B7E7HSjQ-SumQmlPc1Z0aUR5Sk0 Splicing JunctionSeq: https://github.com/hartleys/JunctionSeq SplicER: https://github.com/lkmklsmn/SplicER Annotation of splicing types https://r-forge.r-project.org/projects/splicingtypes/ MAJIQ: detection of local splice variation http://majiq.biociphers.org/ LeafCutter: https://github.com/davidaknowles/leafcutter JSplice: http://www.mhs.biol.ethz.ch/research/krek/jsplice.html Splice site prediction: http://cabgrid.res.in:8080/HSplice/ DRIM-seq: http://bioconductor.org/packages/DRIMSeq Fast quantification of differential splicing: https://github.com/comprna/SUPPA Identify variant associated with splicing: https://sourceforge.net/projects/isvase/ Prediction of intronic splice branchpoints: https://github.com/betsig/branchpointer/ Proportion spliced index: https://github.com/comprna/Junckey Whippet: https://github.com/timbitz/Whippet.jl SparseIso: http://github.com/henryxushi/SparseIso PennDiff: https://github.com/tigerhu15/PennDiff ASElux: https://github.com/abl0719/ASElux F1000/Bioconductor workflow on DRIMSeq and DEXSeq for DTU: https://f1000research.com/articles/7-952/v1 Assembly CIDANE: http://ccb.jhu.edu/software/cidane/ transrate: evaluation of de novo assemblies http://hibberdlab.com/transrate/ dammit: annotator for de novo assemblies http://dammit.readthedocs.org/en/latest/ kma: detection of differential intron retention https://github.com/pachterlab/kma RapClust: lightweight clustering of de novo transcriptomes https://pypi.python.org/pypi/rapclust/0.1 Shannon http://sreeramkannan.github.io/Shannon/ Strawberry https://github.com/ruolin/Strawberry CLASS2: http://ccb.jhu.edu/people/florea/research/CLASS2/ BinPacker: https://sourceforge.net/projects/transcriptomeassembly/files/ Multi-sample transcriptome assembly: http://tacorna.github.io/ Clustering to decontaminate de novo assemblies: https://github.com/Lafond-LapalmeJ/MCSC_Decontamination Assembly from unstranded data: http://big.hanyang.ac.kr/CAFE Scallop: https://github.com/Kingsford-Group/scallop Identification of transcript boundaries: https://github.com/realbigws/DeepBound Corset: gene counts from a transcriptome assembly https://github.com/Oshlack/Corset/wiki Consensus method: https://github.com/macmanes-lab/Oyster_River_Protocol Necklace: https://github.com/Oshlack/necklace Strawberry: https://github.com/ruolin/strawberry BIISQ: also does differential isoform expression https://github.com/bee-hive/BIISQ Cluster and annotate contigs from denovo assemblies: https://github.com/COMBINE-lab/grouper https://github.com/studla/RYUTO Time series DiceSeq: http://diceseq.sourceforge.net/ ctsGE: https://bioconductor.org/packages/release/bioc/html/ctsGE.html Dynamic-BM: http://bioinfo.ibp.ac.cn/Dynamic-BM/ Differential isoform usage over time http://bioconductor.org/packages/devel/bioc/html/maSigPro.html Deconvolution VoCAL: https://cran.r-project.org/web/packages/ComICS/index.html DeconRNASeq: deconvolute expression profiles in mixed tissues http://www.bioconductor.org/packages/2.12/bioc/vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf Search BloomTree: http://www.cs.cmu.edu/∼ckingsf/software/bloomtree/ SBTs https://github.com/medvedevgroup/bloomtree-allsome Variant calling Post-processing to improve germline calling: https://github.com/inab/SmartRNASeqCaller Structural variation Squid: https://github.com/Kingsford-Group/squid Identify gene expression driven by copy number alteration in samples with matched RNA-seq and CNA data: https://www.bioconductor.org/packages/release/bioc/html/iGC.html Fusions FusionCatcher: https://github.com/ndaniel/fusioncatcher STAR: http://star-fusion.github.io AF4: Alignment-free fusion detection https://github.com/grailbio/bio/tree/master/fusion Using kallisto: https://github.com/pmelsted/pizzly Using RapMap https://github.com/nghiavtr/FuSeq Other Biclustering for gene co-expression analysis: http://bioconductor.org/packages/devel/bioc/html/QUBIC.html Sample size calculation for experimental design: https://cran.r-project.org/web/packages/ssizeRNA/index.html Variance estimation: http://github.com/mengyin/vashr Sample expression \"admixture\" (can also be used for deconvolution): https://www.bioconductor.org/packages/release/bioc/html/CountClust.html Essentially, this assigns samples to clusters based on similarity in expression of sets of genes determined be be most discriminating. Each sample can belong to multiple clusters (similar to an admixture analysis). Identification of regulatory networks: https://sites.google.com/a/fleming.gr/rnea/ Predict ribosome footprint from transcripts https://sourceforge.net/projects/riboshape/ Phasing: https://github.com/secastel/phaser Identifying the source of (almost) all RNA-seq reads: https://github.com/smangul1/rop/wiki Subsampling to determine effect of read depth on downstream analyses: http://www.bio-complexity.com/samExploreR_1.0.0.tar.gz Phenotype prediction: https://github.com/clabuzze/Phenotype-Prediction-Pipeline Predict RNA-RNA interaction: https://github.com/satoken/ractip Mitigate cell-cycle effects: http://www.nature.com/articles/srep33892 Fast computation of probabilities of pairwise regulation https://github.com/lingfeiwang/findr Align against synthetic transcript-based reference: https://github.com/Oshlack/Lace Interactive visualization http://bioconductor.org/packages/devel/bioc/html/Glimma.html New factorization method for dimensionality reduction: https://github.com/brian-cleary/CS-SMAF Database of public RNA-seq data sets processed using the same pipeline: https://github.com/mskcc/RNAseqDB Evaluation of aligners on long reads (GMap performs best): http://biorxiv.org/content/early/2017/04/11/126656 Test different ML algorithms for classifying expression profiles: https://github.com/gboris/blkbox Find regions of correlated expression: https://cran.r-project.org/web/packages/SegCorr/index.html Tools for barcoded RNA-Seq: https://github.com/lhhunghimself/LINCS_RNAseq_cpp Includes umimerge_filter, which converts barcoded RNA-Seq alignments into a 8-byte hashes (barcode + position) Single-cell Comparative analysis of methods: http://www.sciencedirect.com/science/article/pii/S1097276517300497 https://www.nature.com/nmeth/journal/v14/n4/full/nmeth.4220.html Benchmarking: https://www.biorxiv.org/content/early/2018/10/08/433102 Review of experimental design and analysis: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0927-y Sean Davis\' list: https://github.com/seandavi/awesome-single-cell Binary format and tools for storing single-cell data: https://github.com/BUStools Datasets scRNAseqDB: https://bioinfo.uth.edu/scrnaseqdb/ Analysis-ready datasets: http://imlspenticton.uzh.ch:3838/conquer/ Platforms Microwells: http://www.nature.com/articles/srep33883 Simulation SymSim: https://github.com/YosefLab/SymSim Using GANs: https://github.com/imsb-uke/scGAN https://github.com/COMBINE-lab/minnow QC http://www.morgridge.net/SinQC.html https://github.com/YosefLab/scone Parallel transcriptome/epigenome data: https://github.com/ChengchenZhao/DrSeq2 https://bitbucket.org/princessmaximacenter/Sharq Normalization ks test: http://michelebusby.tumblr.com/post/130202229486/the-ks-test-looks-pretty-good-for-single-cell Accounting for technical variation: http://www.nature.com/ncomms/2015/151022/ncomms9687/full/ncomms9687.html#supplementary-information ZIFA: Zero-inflated factor analysis https://github.com/epierson9/ZIFA NODES http://biorxiv.org/content/early/2016/04/22/049734.full.pdf+html https://www.dropbox.com/s/pno78mmlj0exv7s/NODES_0.0.0.9010.tar.gz?dl=0 scran: http://bioconductor.org/packages/devel/bioc/html/scran.html Correct for expression heterogeneity: https://github.com/PMBio/scLVM Comparison of normalization methods: http://biorxiv.org/content/biorxiv/early/2016/07/17/064329.full.pdf SCnorm: https://github.com/rhondabacher/SCnorm/tree/master/R Factor analysis: https://github.com/UcarLab/IA-SVA/ qSVA corrects for RNA quality (in the SVA package) ERCC https://bitbucket.org/bsblabludwig/bearscc Weighting method that enables bulk RNA-seq tools to be used with single-cell: https://www.ncbi.nlm.nih.gov/pubmed/29478411 Gene/Transcript counting Modified version of Kallisto: https://github.com/govinda-kamath/clustering_on_transcript_compatibility_counts DISCO: https://pbtech-vc.med.cornell.edu/git/mason-lab/disco/tree/master ESAT: http://garberlab.umassmed.edu/software/esat/ Cell type-specific expression CellMapper: http://bioconductor.org/packages/release/bioc/html/CellMapper.html Clustering Comparative analysis: http://biorxiv.org/content/early/2016/04/07/047613 https://github.com/epurdom/clusterExperiment SC3: consensus clustering https://github.com/hemberg-lab/sc3 destiny: diffusion maps for single-cell data http://bioconductor.org/packages/release/bioc/html/destiny.html https://github.com/govinda-kamath/clustering_on_transcript_compatibility_counts GiniClust https://github.com/lanjiangboston/GiniClust pcaReduce: https://github.com/JustinaZ/pcaReduce SIMLR: https://github.com/BatzoglouLabSU/SIMLR CIDR: https://github.com/VCCRI/CIDR Vortex: http://web.stanford.edu/~samusik/vortex/ Identify rare cell types: RaceID http://www.nature.com/nature/journal/v525/n7568/full/nature14966.html CIDR: https://github.com/VCCRI/CIDR ZIMBwave: https://github.com/drisso/zinbwave Packages from the Chen lab: http://www.pitt.edu/~wec47/singlecell.html Neural networks for dimensionality reduction and clustering http://sb.cs.cmu.edu/scnn/ DCSS: https://github.com/srmcc/dcss_single_cell Cell similarity measure: https://github.com/maggiecrow/MetaNeighbor Dimensionality reduction introduces distortions that can be addressed by high-dimensional PCA https://www.biorxiv.org/content/10.1101/689851v1 Differential Expression Monocle cole-trapnell-lab.github.io/monocle-release/ (2.0 has Census algorithm for differential transcript analysis) scDD: https://github.com/kdkorthauer/scDD ISOP: comparison of isoform pairs in single cells https://github.com/nghiavtr/ISOP D3E: http://hemberg-lab.github.io/D3E/ BASiCS: https://github.com/catavallejos/BASiCS Beta Poisson: https://github.com/nghiavtr/BPSC Zero-inflation correct enables use of DESeq2, etc w/ single cell data: https://github.com/statOmics/zingeR DESingle: https://github.com/miaozhun/DEsingle VAMF: https://github.com/willtownes/vamf-paper Post-clustering: https://github.com/jessemzhang/tn_test Allele-specific expression SCALE accounts for \"burstiness\" of transcription: https://github.com/yuchaojiang/SCALE scBASE: https://github.com/churchill-lab/scBASE Splicing Brie: https://github.com/huangyh09/brie Time-series/ordering/lineage prediction Monocle: Analysis of pseudotime uncertainty: http://cole-trapnell-lab.github.io/monocle-release/ ECLAIR: cell lineage prediction https://github.com/GGiecold/ECLAIR Identification of ordering effects: https://github.com/lengning/OEFinder Slicer: non-linear trajectories https://github.com/jw156605/SLICER Wishbone: identification of bifurcations in developmental trajectories http://www.c2b2.columbia.edu/danapeerlab/html/cyt-download.html SCOUP: https://github.com/hmatsu1226/SCOUP Ouija: https://github.com/kieranrcampbell/ouija SinCell: http://bioconductor.org/packages/release/bioc/html/sincell.html SlingShot: https://github.com/kstreet13/slingshot Cytoscape plugin: http://cytospade.org/ TSCAN: https://github.com/zji90/TSCAN Scimitar: https://github.com/dimenwarper/scimitar TimeSeq: https://cran.r-project.org/web/packages/timeSeq/index.html cellTree: http://bioconductor.org/packages/release/bioc/html/cellTree.html Diffusion pseudiotime: http://www.helmholtz-muenchen.de/icb/research/groups/machine-learning/projects/dpt/index.html KBranches: https://github.com/theislab/kbranches Construction co-expression networks: https://cran.r-project.org/web/packages/LEAP/index.html Differentila expression between trajectories https://github.com/kieranrcampbell/switchde FORKS: https://github.com/macsharma/FORKS PhenoPath: https://github.com/kieranrcampbell/phenopath Pipelines Seurat http://www.satijalab.org/seurat.html SINCERA https://research.cchmc.org/pbge/sincera.html MAST: https://github.com/RGLab/MAST scde (differential expression + gene set over-dispersion): https://github.com/hms-dbmi/scde BaSiCs: Bayesian analysis of single cell data: https://github.com/catavallejos/BASiCS FastProject: https://github.com/YosefLab/FastProject/wiki Citrus: http://chenmengjie.github.io/Citrus/ Tools from Teichmann lab (cellity, celloline, scrnatb): https://github.com/Teichlab/ SCell: https://github.com/diazlab/SCell Scater: http://bioconductor.org/packages/scater HocusPocus: https://github.com/joeburns06/hocuspocus Granatum: https://gitlab.com/uhcclxgg/granatum scPite: https://github.com/LuyiTian/scPipe Scrat: For epigenetic data: https://zhiji.shinyapps.io/scrat/ scanpy: https://github.com/theislab/scanpy SNVs/CNVs DNA SNV calling: https://bitbucket.org/hamimzafar/monovar Ginko: analysis of CNVs in single-cell data: http://qb.cshl.edu/ginkgo/?q=/XWxZEerqqY477b9i4V8F CNV calling: http://genome.cshlp.org/content/early/2016/01/15/gr.198937.115.full.pdf Genotyping: https://bitbucket.org/aroth85/scg/wiki/Home Regulatory networks Gene co-expression: http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004892 SCODE: https://github.com/hmatsu1226/SCODE SCENIC: https://github.com/aertslab/SCENIC Integration: https://github.com/YosefLab/scVI https://github.com/brianhie/scanorama Other scRNA-seq Analysis of 3\' tagging data: https://github.com/garber-lab/ESAT StemID: Prediction of stem cells and lineage information https://github.com/dgrun/StemID Phasing: https://github.com/edsgard/scphaser Classification using sets of known cell type-specific genes: https://github.com/YosefLab/FastProject/wiki UMI counting: https://github.com/vals/umis Imputation of missing values Magic: https://github.com/pkathail/magic/ scImpute: https://github.com/Vivianstats/scImpute DrImpute: https://cran.r-project.org/web/packages/DrImpute/index.html Power analysis: https://github.com/vals/umis/ Pooled perturbation experiments: https://github.com/asncd/MIMOSCA Simulation: http://bioconductor.org/packages/splatter/ Comparison across experiments: https://github.com/hemberg-lab/scmap Demuxlet: using natural genetic variation to demultiple 10x data https://github.com/hyunminkang/apigenome Alignment of multiple single-cell data sets: https://github.com/jw156605/MATCHER Topological analysis: https://github.com/RabadanLab/scTDA Deconvolution of bulk RNA from scRNA: https://github.com/xuranw/MuSiC Methylation Prediction of missing information: https://github.com/cangermueller/deepcpg Mapping: https://github.com/wupengomics/scBS-map ATAC-seq Infer TF variation: https://github.com/GreenleafLab/chromVAR Clustering: https://github.com/timydaley/scABC Somatic QC Bias detection https://www.degruyter.com/downloadpdf/j/jib.2017.14.issue-3/jib-2017-0025/jib-2017-0025.pdf This method is targeted to RNA-Seq, but should work for CNV calling from Exome data as well Deconvolution of signal (allele frequency, gene expression, etc) from heterogeneous tissue data: https://github.com/tedroman/WSCUnmix Remove strand bias artifacts (e.g. FFPE samples): https://github.com/mikdio/SOBDetector Personal reference editor: https://github.com/precisionomics/PRESM Sentieon includes step for \"co-realignment\" which just seems to be merging tumor+normal and doing indel realignment on the merged sample to harmonize indel alignment for better comparability after variant calling. Variant Calling Review of somatic variant callers: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5852328/ FMI abstracts on variant calling from tumor-only sequencing http://cancerres.aacrjournals.org/content/74/19_Supplement/1893 http://ascopubs.org/doi/abs/10.1200/jco.2015.33.15_suppl.11084 Sarek: https://github.com/SciLifeLab/Sarek Comparison of 9 somatic variant callers http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0151664 From 2016; should be updated with MuTect2 and Strelka2 Lancet: new somatic variant caller based on genome graphs https://github.com/nygenome/lancet Non-commercial license Some recommended tools/pipelines for calling low-frequency variants: https://github.com/tamilieberman/TB-diversity-across-organs LoFreq: http://csb5.github.io/lofreq/ ASAP: https://github.com/TGenNorth/ASAP Goby uses deep learning for somatic variant calling: http://campagnelab.org/software/goby/ MC3/PanCan https://www.synapse.org/#!Synapse:syn7214402/wiki/406010 Variant call merger: https://github.com/covingto/pancanmafmerge Ensemble method with random forest integration of results from multiple callers: https://github.com/skandlab/SMuRF Paper: https://www.biorxiv.org/content/early/2018/02/23/270413 http://bioinform.github.io/somaticseq/ TNScope: Sentieon\'s pipeline https://www.biorxiv.org/content/early/2018/01/19/250647 Includes machine learning-based variant filtering xATLAS https://github.com/jfarek/xatlas Uses a logistic regression model based on truth sets (e.g. GIAB) to assign quality scores Consensus calling from multiple callers: https://www.itb.cnr.it/web/bioinformatics/isma Multi-sample caller based on mpileup: https://github.com/IARCbioinfo/needlestack Deep learning https://github.com/jingmeng-bioinformatics/DeepSSV NeuSomatic (non-commercial license) Optimized for FFPE: https://www.ciscall.org/en/ciscall7.html CNA/SV Infer the tumor cell fraction of SV: https://github.com/mcmero/SVclone A study of using shallow WGS on low-quality FFPE samples for CNV calling used QDNASeq for segmentation https://bioconductor.org/packages/release/bioc/html/QDNAseq.html Adjusts for CG content and mappability Found 7M reads was optimal https://www.biorxiv.org/content/early/2017/12/08/231480 Germline CNVs may be predictive of cancer susceptibility: https://www.biorxiv.org/content/early/2018/04/17/303339 Allele-specific SV calling: https://github.com/ma-compbio/Weaver Phasing SVs with noisy data: http://sci-hub.tw/http://liebertpub.com/doi/pdf/10.1089/cmb.2018.0022 SEG: https://github.com/ZhaoS-Lab/SE Multi-sample (from same patient) HATCHet CNValidator Estimate copy number and purity: https://github.com/keyuan/ccube Allele-specific CNA: https://github.com/wheelerb/hsegHMM DEFOR: https://github.com/drzh/defor Joint tumor-normal detection: https://github.com/yongzhuang/TumorCNV Variant Filtering TNER: https://github.com/ctDNA/TNER This is a cool approach for constructing a ChIP-Seq control from integration of multiple public data sets: https://www.biorxiv.org/content/early/2018/03/08/278762. It strikes me that a similar approach could be used to generate matched controls for tumor-only samples. Features used to design a RF classifier: https://www.biorxiv.org/content/biorxiv/early/2019/06/13/670687.full.pdf SGZ method used by FoundationOne for filtering germline variants from tumor-only variant calls https://github.com/jsunfmi/SGZ Tumor Purity IchorCNA Estimates tumor cell fraction from low-pass WGS; should probably be adaptable to deep targeted sequencing https://github.com/broadinstitute/ichorCNA Accurity (tumor-normal): https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty043/4827681 Control for tumor purity differences in methylation analysis: https://www.biorxiv.org/content/early/2018/01/16/248781 PyLOH: https://github.com/uci-cbcl/PyLOH AllFit: https://github.com/KhiabanianLab/All-FIT TPES: using SNVs https://bitbucket.org/l0ka/tpes/src/master/ From methylation: https://github.com/xjtu-omics/MEpurity Tumor Muational Burden http://moat.gersteinlab.org/ ecTMB: https://github.com/bioinform/ecTMB Variant Annotation CHASM and SNV-Box CHASM Predicts functional significance of somatic missense mutations SNV-Box is a database of pre-computed features of all possible amino acid substitutions in the human genome http://wiki.chasmsoftware.org/index.php/Main_Page Not free for commercial use, but it was developed at JHU and Vogelstein is a co-author on at least some of the publications Orchid: framework for annotation and machine learning of cancer variants: https://github.com/wittelab/orchid Discover potential cancer driver elements DriverPower: https://github.com/smshuai/DriverPower NetSig: http://www.lagelab.org/projects/ CanDrA: http://bioinformatics.mdanderson.org/main/CanDrA#CanDrA Predict cancer subtype using biological networks based on somatic variants: https://www.biorxiv.org/content/early/2017/12/03/228031 PCGR: Annotate cancer genomes with clinical information: https://github.com/sigven/pcgr Integrates many public databases Benchmark of methods to identify pathogenic non-coding variation: https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty008/4798701?redirectedFrom=fulltext FASMIC: http://bioinformatics.mdanderson.org/main/FASMIC Database of TCGA alternative splicing events https://www.cell.com/cancer-cell/fulltext/S1535-6108(18)30306-4#secsectitle0080 Consensus driver gene identification pipeline and database of cancer driver genes in TCGA: https://www.cell.com/cell/fulltext/S0092-8674(18)30237-X TCGA pathogenic signalling pathways: https://www.cell.com/cell/fulltext/S0092-8674(18)30359-3?cid=tw%26p Predict cancer type/signature From mutations: https://www.nature.com/articles/nature12477 From SNV+SV calls: https://www.biorxiv.org/content/early/2018/02/18/267500 Machine learning method to predict cancer type from variant calls: https://www.biorxiv.org/content/early/2017/11/05/214494 Prediction of inherited susceptibility to 20 difference cancer types: http://www.pnas.org/content/115/6/1322.short Computing polygenic risk scores and association with cancers: https://www.biorxiv.org/content/early/2017/10/19/205021 Predict driver mutations: https://github.com/KarchinLab/CHASMplus https://bioconductor.org/packages/release/bioc/html/SparseSignatures.html Clonality QuantumClone: https://cran.r-project.org/web/packages/QuantumClone/index.html MOSEM: https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-018-0128-6/MediaObjects/41588_2018_128_MOESM1_ESM.pdf Benchmarking, plus description of CloneFinder: https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/bty469/5040314 MSI Sonics: https://github.com/kzkedzierska/sonics Neoantigen pVACtools: https://github.com/griffithlab/pVACtools NeoPredPipe: https://github.com/MathOnco/NeoPredPipe https://github.com/neoanthill/neoANT-HILL ctDNA/cfDNA TNER: background error reduction https://github.com/ctDNA/TNER Other Bioconductor package with various methods for working with TCGA data: https://bioconductor.org/packages/release/bioc/html/TCGAbiolinks.html maftools: https://bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html Sarek: end-to-end variant calling pipeline in NextFlow https://github.com/SciLifeLab/Sarek Multi-regional sequencing: Mutect2 in multi-sample mode with correction step performs best https://www.biorxiv.org/content/10.1101/655605v1 Subclonality-aware association: https://github.com/Sun-lab/SMASH Regional mutation density classifies cancer type: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006953 Integrated Methods Reviews: http://www.biomedcentral.com/1752-0509/8/S2/I1 Comparison of methods http://www.biomedcentral.com/1752-0509/8/S2/S4 Focusing on integration of RNA-seq and ChIP-seq http://journal.frontiersin.org/article/10.3389/fcell.2014.00051/full Network-based methods: http://rsif.royalsocietypublishing.org/content/12/112/20150571 General-purpose multi-omics integration: mixOmics: R package that implements several multivariate methods, including DIABLO http://mixomics.org/ Non-negative matrix factorization for integration of data sets https://github.com/yangzi4/iNMF omicade4: Integration of multi-omics data using co-inertia analysis https://bioconductor.org/packages/release/bioc/html/omicade4.html Integration of multi-omics data using random forests: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1043-4 Kernel-PCA: http://www.biomedcentral.com/1752-0509/8/S2/S6 Integrative analysis of multiple diverse omics datasets by sparse group multitask regression http://journal.frontiersin.org/article/10.3389/fcell.2014.00062/abstract Joint bi-clustering of multiple data types: http://research.cs.aalto.fi/pml/software/GFAsparse/ Identifying covariance between sequencing data sets: http://github.com/pmb59/fCCAC/ PyPanda: https://github.com/davidvi/pypanda SDA: integrate gene expression across multiple tissues, or multi-omics in a single tissue, for identification of trans-QTL networks: https://jmarchini.org/sda/ HMM for binary classification based on multivariate data: https://github.com/PetarV-/muxstep OmicsIntegrator: https://github.com/fraenkel-lab/OmicsIntegrator Correlation between enriched regions from different data sets: http://malone.bioquant.uni-heidelberg.de/software/mcore R.Jive: https://cran.r-project.org/web/packages/r.jive/ SIFORM: http://bioinformatics.oxfordjournals.org/content/early/2016/07/03/bioinformatics.btw295.full EpiMine: https://sourceforge.net/projects/epimine/ Deep learning-based framework for integrating multiple data types to predict another data type: https://github.com/ueser/FIDDLE Significance-based https://www.bioconductor.org/packages/release/bioc/html/SMITE.html Two-stage CCA identifies non-linear associations: https://github.com/kosyoshida/TSKCCA HMM: https://link.springer.com/protocol/10.1007/978-1-4939-6753-7_10 PGenmi: https://github.com/KnowEnG/pgenmi MOFA: https://github.com/bioFAM/MOFA TensorDecomp: https://github.com/jinghan1018/tensor_decomp Unsupervised: https://github.com/bioFAM/MOFA Avocado: https://noble.gs.washington.edu/proj/avocado/ Miodi https://gitlab.com/algoromics/miodin Multi-tissue: HDTD: http://bioconductor.org/packages/release/bioc/html/HDTD.html IDEAS: https://github.com/yuzhang123/IDEAS Specific data types: Predict gene fusions from WGS and RNA-seq: http://sourceforge.net/p/integrate-fusion/wiki/Home/ NuChart: layer additional omics data on Hi-C ftp://fileserver.itb.cnr.it/nuchart/ Predict expression from H3K27, and identify cis-regulatory elements: http://cistrome.org/MARGE/ GenoSkyline: predict tissue-specific functional regions from epigenomic data http://genocanyon.med.yale.edu/GenoSkyline Integrate DNA and RNA: https://github.com/griffithlab/regtools Network-based: Merging networks: https://github.com/maxconway/SNFtool XMWAS: https://sourceforge.net/projects/xmwas/ Causality Hybrid BN/CMI approach to constructing GRNs: http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1005024 Noise/bias: Bias correction across different assays on the same samples: https://cran.r-project.org/web/packages/MANCIE/ Cell cycle heterogeneity appears to be a minor contributor to noise; instead, library size is the largerst PC by far http://www.nature.com/nbt/journal/v34/n6/full/nbt.3498.html Patient/disease subtyping http://biorxiv.org/content/biorxiv/early/2016/09/03/073189.full.pdf (Matlab code) Imputation of missing data MI-MFA: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1273-5 TensorPute: multi-dimensional imputation https://sites.google.com/site/tensortest2/ Other Identify class-descriminative motifs enriched in subclasses of overlapping annotations: https://github.com/seqcode/sequnwinder R class for integration algorithms: https://bioconductor.org/packages/release/bioc/html/MultiDataSet.html General Programming Resources Generate data type-specific compression formats: http://algorithms.cnag.cat/cargo/ Protocol buffers Protobuf: fast cross-language/platform serialization of fixed-format messages https://developers.google.com/protocol-buffers/ Cap\'n Proto: https://capnproto.org/ IDEs VisualStudio (now free): https://www.visualstudio.com/vs/visual-studio-mac/ Parameter optimization Spearmint: https://github.com/beamandrew/Spearmint Diff/patch/merge for data tables js/python: http://paulfitz.github.io/daff/ R: https://github.com/edwindj/daff Pipe output of a shell command to a website (unfortunately can\'t be used in NIH HPC since nodes do not allow network connections): https://seashells.io/ Debugging Sandsifter: Fuzzer https://github.com/xoreaxeaxeax/sandsifter LivePython: https://github.com/agermanidis/livepython JSON Diff: http://www.jsondiff.com/ Google C++ and python libraries: https://github.com/abseil/abseil-py Share a terminal as a web page: https://github.com/yudai/gotty Spec for CSV files with metadata: http://docs.astropy.org/en/stable/api/astropy.io.ascii.Ecsv.html C/C++ kmers Streaming kmer counting https://github.com/bcgsc/ntCard kmer bloom filters: https://github.com/Kingsford-Group/kbf High-performance concurrent hash table (C++11): https://github.com/efficient/libcuckoo BWT that incorporates genetic variants: https://github.com/iqbal-lab/gramtools Fast bitwise operations on nucleotide sequences: https://github.com/kloetzl/biotwiddle C++ interface to htslib, BWA-MEM, and Fermi (local assembly) (would be useful to build python bindings for this): https://github.com/walaj/SeqLib Minimal perfect hash function: fast, large data sets https://github.com/rizkg/BBHash Counting quotient filter: https://github.com/splatlab/cqf Succinct de Bruijn Graphs: http://alexbowe.com/succinct-debruijn-graphs/ Blazing signature filter: fast pairwise comparison of e.g. gene expression matrices https://github.com/PNNL-Comp-Mass-Spec/bsf-py rr debugger: record and playback executions http://rr-project.org/ clip: command line parser https://github.com/muellan/clipp klib: generic library with fast implementations of striped Smith-Waterman and other NGS-related algorithms https://github.com/attractivechaos/klib Sketch data structures (includes Python bindings): https://github.com/dnbaker/sketch R Tidy data cheatsheet: http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf Multiple variable assignment: https://github.com/nteetor/zeallot Summary statistics: https://github.com/ropenscilabs/skimr Applying tidy principals to GRanges: https://bioconductor.org/packages/release/bioc/html/plyranges Find packages Awesome R: https://github.com/qinwf/awesome-R#integrated-development-environment http://www.computerworld.com/article/2497464/business-intelligence/business-intelligence-60-r-resources-to-improve-your-data-skills.html Cranberries: http://dirk.eddelbuettel.com/cranberries/ METACRAN: identify R packages http://www.r-pkg.org/ Database MonetDB - embeddable column-store DB with R integration (MonetDB.R) Data Cleaning daff: diff/merge for data frames - https://github.com/edwindj/daff dplyr chunked processing of large files: https://github.com/edwindj/chunked magrittr/pipes debugging: https://github.com/gaborcsardi/tamper Join tables on inexact matching: https://github.com/dgrtwo/fuzzyjoin Tidy text: http://juliasilge.com/blog/Life-Changing-Magic/ Reporting MarkDeep for R documentation: http://casual-effects.com/markdeep/ Nozzle: https://confluence.broadinstitute.org/display/GDAC/Nozzle Misc Access to Google spreadsheets from R: https://github.com/jennybc/googlesheets Advanced table formatting in knitr: https://github.com/renkun-ken/formattable Access data frames using SQL: sqldf package Developing R packages: https://github.com/jtleek/rpackages Work with PDF files: https://cran.r-project.org/web/packages/pdftools/index.html Language-agnostic data frame format: https://github.com/wesm/feather Make for R: https://github.com/richfitz/maker Find root of current package: https://krlmlr.github.io/here/ Work with genomic ranges using dplyr-like syntax. Python Data structures/formats Chunked, compressed, disk-based arrays: https://github.com/alimanfoo/zarr Tabular data Working with tabular data: http://docs.python-tablib.org/en/latest/ Apache Arrow Pandas Vaesx:https://github.com/vaexio/vaex GFA: https://github.com/ggonnella/gfapy/tree/master/gfapy A regular expression scanner: https://github.com/mitsuhiko/python-regex-scanner API for interacting with databases: https://github.com/kennethreitz/records RStudio for python: https://www.yhat.com/products/rodeo Debugging boltons.debugutils: The entire boltons package has lots of useful stuff, but debugutils is particularly cool - you can add one line of code to enable you to drop into a debugger on signal (e.g. Ctrl-C): https://boltons.readthedocs.io/en/latest/debugutils.html Web-based debugger: https://github.com/alexmojaki/birdseye Stats Non-negative matrix factorization: https://github.com/ccshao/nimfa Statsmodels: http://www.statsmodels.org/stable/index.html R formulas in python: https://github.com/pydata/patsy pyrasite: code injection into running applications Dexy: documentation Event loops for asynchronous programming curio gevent Fast microservices: https://github.com/squeaky-pl/japronto dill: alternative serialization arrow: alternative to datetime Template for scientific projects: https://github.com/uwescience/shablona FFI GO transplier: https://github.com/google/grumpy Calling Rust libraries from python: https://medium.com/@caulagi/complementing-python-with-rust-657a8cb3d066#.6in8v0bte pyjamas: javascript bridge Disabling python garbage collection speeds up programs: https://engineering.instagram.com/dismissing-python-garbage-collection-at-instagram-4dca40b29172#.ri55nyjdu (only safe when the lifecycle is straight-forward for all objects, an thus reference counting is sufficient for memory management) Easily implementing function proxies/wrappers: http://wrapt.readthedocs.io/en/latest/ Cache system: https://bitbucket.org/zzzeek/dogpile.cache Parse TOML (an enhanced config-file spec): https://github.com/uiri/toml Web scraping Goose: https://github.com/grangier/python-goose ScraPy: https://scrapy.org/ Requestium: https://github.com/tryolabs/requestium Visualize python code execution time as a heatmap in a Jupyter notebook: https://github.com/csurfer/pyheatmagic Graph genomes High-level API for working with GFA (i.e. graph genomes): https://github.com/ggonnella/gfapy https://github.com/jambler24/GenGraph Fast and memory-efficient object counting: https://github.com/RaRe-Technologies/bounter Fast string matching: https://bergvca.github.io/2017/10/14/super-fast-string-matching.html Trace system calls in multiprocessing context: https://github.com/pinterest/ptracer skbio: http://scikit-bio.org/docs/0.4.1/index.html Extract keywords from text: https://github.com/vi3k6i5/flashtext Working with time-series data: https://github.com/RJT1990/pyflux Probability distributions and other related functions: https://pomegranate.readthedocs.io/en/latest/ Global optimization: https://github.com/chrisstroemel/Simple HPAT: framework for automatically parallizing numpy and pandas code using MPI https://github.com/IntelLabs/hpat CuPy: Numpy-like implementation of GPU-accelerated array operations https://github.com/cupy/cupy Command-line interface to download and manage reference genomes: https://github.com/simonvh/genomepy API for working with HGVS-formatted variants: https://github.com/biocommons/hgvs B+ Tree: Hash table backed by on-disk storage https://github.com/NicolasLM/bplustree Chunked, compressed ndarrays http://zarr.readthedocs.io/en/latest/index.html Easy colored/styled printing in command line apps https://github.com/UltimateHackers/hue Launching a subprocess in a pseudo-terminal (e.g. for accepting passwords) https://github.com/pexpect/ptyprocess Launch an editor from python: https://github.com/fmoo/python-editor VCF: ReadVCF: http://alimanfoo.github.io/2017/06/14/read-vcf.html Faster alternative to pyvcf: https://github.com/brentp/cyvcf2 BAM: Pure python BAM parser https://github.com/betteridiot/bamnostic Alternative to pandas for larger-than-memory datasets: https://vaex.readthedocs.io/en/latest/ HPC Spark: https://spark.apache.org/downloads.html https://amplab-extras.github.io/SparkR-pkg/ Ibis: http://blog.cloudera.com/blog/2015/07/ibis-on-impala-python-at-scale-for-data-science/ Petuum: http://petuum.github.io/ Flink: https://flink.apache.org/ Dask: http://dask.pydata.org/en/latest/ Efficient tabular storage: http://matthewrocklin.com/blog/work/2015/08/28/Storage/ Common runtime for various libraries (e.g. Pandas, TensorFlow) that speeds up execution when interfacing the libraries with each other: https://weld-project.github.io/ Command Line (OSX/Linux) Diff tables: https://github.com/paulfitz/daff Miller - work with tables http://johnkerl.org/miller/doc/build.html Databases CockroachDB: based on Google\'s distributed database https://github.com/cockroachdb/cockroach In-memory key-value db in python: https://github.com/paxos-bankchain/subconscious Reproducibility/Containerization Packrat: http://rstudio.github.io/packrat/walkthrough.html Docker http://arxiv.org/pdf/1410.0846v1.pdf http://bioboxes.org/available-bioboxes/ http://ivory.idyll.org/blog//2015-docker-and-replicating-papers.html GUI for running Docker images locally: https://kitematic.com/ Convert Docker images to AWS Lambda container images: https://github.com/awslabs/aws-lambda-container-image-converter Programmatic access to Docker from Python: https://github.com/docker/docker-py Jupyter notebooks http://jupyter.org/ http://mybinder.org/ RISE: presentations from Jupyter notebooks https://github.com/damianavila/RISE Convert to markdown: https://github.com/aaren/notedown Jupyter notebooks on Azure: https://notebooks.azure.com/ Diff/merge: https://github.com/jupyter/nbdime Web-based IDE built on Jupyter: https://github.com/jupyterlab/jupyterlab Convert Jupyter notebook to Sphinx documentation: https://matthew-brett.github.io/nb2plots Diagram editor: https://blog.jupyter.org/a-diagram-editor-for-jupyterlab-a254121ff919 Jupyter alternatives https://nteract.io/ http://www.unnotebook.com/ GUI frontend to IPython: http://nwhitehead.github.io/pineapple/ Stencila: interesting alternative to Jupyter notebooks and R markdown https://stenci.la/ Bioinformatics software containers BioContainers: https://github.com/BioContainers Continuous analysis: https://github.com/greenelab/continuous_analysis Creating reproducible workflows with R Markdown documents: https://jdblischak.github.io/workflowr/ Popper: http://falsifiable.us/ Building Pipelines Luigi https://github.com/spotify/luigi Flo http://flo.readthedocs.org/en/latest/index.html Qsubsec: template language for defining SGE workflows https://github.com/alastair-droop/qsubsec Nextflow and Nextflow Workbench: https://www.nextflow.io/ and http://campagnelab.org/software/nextflow-workbench/ https://github.com/assemblerflow/assemblerflow SUSHI: https://github.com/uzh/sushi WorkflowR: https://github.com/jdblischak/workflowr Data manager for R: https://cran.r-project.org/web/packages/repo/index.html SnakeMake SnakeChunks: components for SnakeMake https://github.com/SnakeChunks/SnakeChunks Create command line programs from SnakeMake workflows: https://github.com/nh13/snakeparse GUI: http://sequana.readthedocs.io/en/master/sequanix.html dgsh: bash variant that has primitives for parallelizing tasks https://www2.dmst.aueb.gr/dds/sw/dgsh Rabix: visual editor for CWL pipelines http://rabix.io/ (deprecated?) List of workflow systems: https://github.com/common-workflow-language/common-workflow-language/wiki/Existing-Workflow-systems Experimental directory structure: alternative to HDF5 https://github.com/CINPLA/exdir/ Structured projects that can be run on multiple workflow engines: http://reana.readthedocs.io/ Invoke: http://docs.pyinvoke.org/en/latest/ Toil: http://toil.readthedocs.io/en/latest/installation.html Snakemake Pipelines for several genomic/epigenomic analyses https://github.com/maxplanck-ie/snakepipes Reflow: workflow management system developed by Grail https://github.com/grailbio/reflow drake: https://github.com/ropensci/drake https://github.com/calebwin/pipelines Software Distribution Executable archives (xar): https://github.com/facebookincubator/xar Other Automated system for sequencing core facilities using microservices architecture: https://www.biorxiv.org/content/early/2017/11/06/214858 Aether manages bidding for AWS and Azure credits: http://aether.kosticlab.org/ Statistics/Machine Learning Search for papers: https://www.semanticscholar.org/ Common probability distributions http://blog.cloudera.com/blog/2015/12/common-probability-distributions-the-data-scientists-crib-sheet/ How to share data with a statistician: https://github.com/jtleek/datasharing Precision-recall curves: https://cran.r-project.org/web/packages/precrec/index.html Alternative: http://link.springer.com/article/10.1007/s10994-009-5119-5 R package for subsetting data into training/testing/validation sets: https://cran.r-project.org/web/packages/STPGA/index.html Second-generation p-values: https://github.com/lucymcgowan/sgpvalue PMML: interchange format for trained models https://en.wikipedia.org/wiki/Predictive_Model_Markup_Language Convert sequence alignments into features for machine learning: https://github.com/ekg/hhga Methods/algorithms Lists https://github.com/rushter/MLAlgorithms Decision tree methods R randomForest package FuzzyForests are an extension of random forests for classification in which subsets of variables/features are highly correlated https://github.com/OHDSI/FuzzyForest CatBoost: https://github.com/catboost 2D Boosting: https://github.com/kundajelab/boosting2D/ New performance metric: https://cran.r-project.org/web/packages/IPMRF/IPMRF.pdf Modrian forests https://scikit-garden.github.io/examples/MondrianTreeRegressor/ Clustering Help selecting biclustering algorithm: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1487-1 DBScan https://cran.r-project.org/web/packages/dbscan/dbscan.pdf KODAMA: https://cran.r-project.org/web/packages/KODAMA/index.html t-SNE: alternative to PCA and MDS: http://lvdmaaten.github.io/tsne/ http://distill.pub/2016/misread-tsne/ UMAP: https://github.com/lmcinnes/umap Compressive k-means: https://arxiv.org/pdf/1610.08738.pdf Sparse convex: https://arxiv.org/pdf/1601.04586.pdf Multivariate CRAN packages: http://cran.r-project.org/web/views/Multivariate.html Multivariate analysis of covariance (MANCOVA): http://en.wikipedia.org/wiki/MANCOVA Correlation https://www.biorxiv.org/content/biorxiv/early/2018/05/25/330498.full.pdf Nonnegative Matrix Factorization: https://cran.r-project.org/web/packages/NMF/vignettes/NMF-vignette.pdf Tensor factorization: https://cran.r-project.org/package=tensorBF Identification of correlated features within or between datasets: https://github.com/siskac/discordant Bayesian alternatives to standard R functions: https://github.com/rasmusab/bayesian_first_aid Bayesian regression modeling: brms and rstanarm are R packages based on stan JAGS http://jeromyanglim.blogspot.com/2012/04/getting-started-with-jags-rjags-and.html MCMC http://www.stat.umn.edu/geyer/mcmc/library/mcmc/doc/demo.pdf Multiple test correction FDR for multi-dimensional pairwise comparisons (e.g. RNA-seq): http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0937-5 Local false signal rate: an alternative to FDR that operates on standard error estimates rather than p-values: https://github.com/stephens999/ashr MTC weighted by variant: effect http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3507.html Parallelizable FDR correction: http://bioinformatics.oxfordjournals.org/content/early/2016/02/25/bioinformatics.btw029.short IHW: http://bioconductor.org/packages/release/bioc/html/IHW.html BonEV: Bonferonni FDR correction https://cran.r-project.org/web/packages/BonEV/index.html Myriads: http://myriads.webs.uvigo.es/ Analysis of mutual information: https://github.com/jkleinj/arMI Fast Bayesian alternative to lasso and ElasticNet for feature selection and effect estimation: https://cran.r-project.org/web/packages/EBglmnet/index.html Genome-wide generalized addative models: https://master.bioconductor.org/packages/3.3/bioc/html/GenoGAM.html Causal inference test: https://cran.r-project.org/web/packages/cit/index.html Iterative denoising tree: https://github.com/youngser/behaviotypes/blob/master/doidt.r Reed-Sololmon error correction https://github.com/tomerfiliba/reedsolomon https://github.com/brownan/Reed-Solomon https://github.com/catid/wirehair Fast exact calculation of p-values in Friedman rank sum test: http://www.ru.nl/publish/pages/726696/friedmanrsd.zip The Friedman test is for testing whether any columns are consistently different from other columns in a matrix Automated generation of ML pipelines: https://github.com/rhiever/tpot/tree/tpot-mdr Forecasting from time-series data: https://facebookincubator.github.io/prophet/ (this is a retail-centric model from Facebook, but could be adapted to biological data) Visualizing ML features: https://github.com/pair-code/facets Topological analysis: https://github.com/MLWave/kepler-mapper https://github.com/RabadanLab/sakmapper http://danifold.net/mapper/ https://github.com/paultpearson/TDAmapper Feature selection Workflows: https://github.com/enriquea/feseR FeatureSelect: https://github.com/LBBSoft/FeatureSelect Iterative random forest: https://github.com/sumbose/iRF Compares method for outlier detection: https://cran.r-project.org/web/packages/OutliersO3/index.html Comparison of inference methods: http://jenniferhill7.wixsite.com/acic-2016/competition Polynomial regression: https://github.com/matloff/polyreg Alternative importance score for random forrest classifiers: https://github.com/StephanSeifert/SurrogateMinimalDepth Feature generation from sequences: https://github.com/mrzResearchArena/PyFeat/ Block random forest method for multi-omic data: https://github.com/bips-hb/blockForest Python Tools for data mining, NLP, ML, network analysis: http://www.clips.ua.ac.be/pages/pattern Linear mixed-model solver https://github.com/nickFurlotte/pylmm Exact inference in HMMs with large number of hidden states: https://github.com/regevs/factorial_hmm scikit-learn-compatible package for graph statistics: https://graspy.neurodata.io/ Deep Learning Reading Papers https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap Books https://hackerlists.com/free-machine-learning-books/ http://www.deeplearningbook.org/contents/intro.html Platforms Aetros: http://aetros.com/ Libraries List: http://www.teglor.com/b/deep-learning-libraries-language-cm569/ Keras: https://github.com/fchollet/keras Chainer: https://www.oreilly.com/learning/complex-neural-networks-made-easy-by-chainer biologicaly-focused neural networks https://github.com/kundajelab/dragonn/tree/master/dragonn analysis of features in deep neural networks https://github.com/kundajelab/deeplift API to add fuzzy logic: https://fuzzy.ai/docs Edward: probabalistic modeling, inference, and criticism; build on TensorFlow https://github.com/blei-lab/edward VectorFlow: specifically designed for sparse data https://github.com/Netflix/vectorflow Architectures: http://www.asimovinstitute.org/neural-network-zoo/ Deep residual: http://image-net.org/challenges/talks/ilsvrc2015_deep_residual_learning_kaiminghe.pdf https://arxiv.org/abs/1512.03385 Wide residual: https://arxiv.org/abs/1605.07146 Time-delay http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=809100 tag=1 Semi-supervised classification of nodes in a graph: https://github.com/tkipf/gcn Adversarial: https://arxiv.org/abs/1406.2661 Recurrent scalable deep kernels: https://arxiv.org/abs/1610.08936 Multiplicative LSTM: https://arxiv.org/abs/1609.07959 Graph CNN: https://arxiv.org/abs/1609.02907 Encoding variable-length sequences: https://arxiv.org/abs/1505.01504 dna2vec: https://pnpnpn.github.io/dna2vec/ Interactive sequence generation from RNNs: https://arxiv.org/abs/1612.04687 DNNs with external memory: http://www.nature.com/nature/journal/v538/n7626/full/nature20101.html The Predictron: https://arxiv.org/abs/1612.08810 Associative LSTM: https://arxiv.org/abs/1602.03032 Conditional variational autoencoders: http://ijdykeman.github.io/ml/2016/12/21/cvae.html Group equivariant CNN: http://jmlr.org/proceedings/papers/v48/cohenc16.pdf QuickNet: https://arxiv.org/abs/1701.02291 Collection of pre-trained tensorflow models: https://github.com/tensorflow/models/tree/master/research Generalized framework for genomic signal recognition: https://zenodo.org/record/1117159 Using autoencoders to impute missing values: https://github.com/gevaertlab/DAPL Tools Generate synthetic training data: https://hazyresearch.github.io/snorkel/ Tensorflow playground: http://playground.tensorflow.org/ DeepVis: http://yosinski.com/deepvis https://medium.com/@shivon/the-current-state-of-machine-intelligence-3-0-e4d305da032e#.gw0ywcpkv Visualize hyperparameters: https://github.com/ContextLab/hypertools Convolutional layers for DNA sequence: https://github.com/koonimaru/DeepGMAP Interpret neuron importance: https://arxiv.org/abs/1807.09946 Non-bio networks that might be applied srez: https://github.com/david-gpu/srez Deep learning with text: https://explosion.ai/blog/deep-learning-formula-nlp Web APIs Google Prediction: https://cloud.google.com/prediction AWS ML: https://aws.amazon.com/machine-learning Data Sets https://medium.com/@olivercameron/20-weird-wonderful-datasets-for-machine-learning-c70fc89b73d5#.9e5byk1mo Text classification Automatic text summarization: https://pypi.python.org/pypi/sumy fastText: https://github.com/facebookresearch/fastText Word2vec: models for predicting missing words https://en.m.wikipedia.org/wiki/Word2vec Visualization Breve is a mac application that displays large tables in a way that makes it easy to identify patterns and missing data http://breve.designhumanities.org/ Types of plots: http://www.informationisbeautifulawards.com/showcase/611-the-graphic-continuum Line graph + heat map: http://www.fastcodesign.com/3052450/an-easy-intuitive-tool-for-making-sense-of-your-data http://datavizproject.com/ Making colorblind-friendly figures: http://bconnelly.net/2013/10/creating-colorblind-friendly-figures/ Examples: http://www.visualcomplexity.com/vc/ Feedback: http://helpmeviz.com/ VDA Lab: https://bitbucket.org/vda-lab/ Visualization of GO results: http://cran.r-project.org/web/packages/GOplot/vignettes/GOplot_vignette.html Fluff: publication-quality genomics plots http://fluff.readthedocs.org Visualization of feature density along the genome: https://github.com/sguizard/DensityMap Circos-like visualization of chromosome structure with support for multiple data types https://rondo.ws Comparison of different types of heatmaps: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1442-6 Interactively build simulations and animated interaction diagrams: http://ncase.me/loopy/ Plotly: Charting library with multiple language bindings https://github.com/plotly/plotly.py Open-access visualization research: http://oavis.steveharoz.com/ Carto Colors: https://carto.com/carto-colors/ libvips: fast imgae processing library (C++ and python bindings) https://github.com/jcupitt/libvips Generate flow charts, etc using markdown-like syntax: https://github.com/knsv/mermaid VCF CircosVCF Visualization of variant calls from a VCF on a CIRCOS plot CircosVCF: http://legolas.ariel.ac.il/~tools/CircosVCF https://github.com/compbiocore/VariantVisualization.jl Interactive reports: https://github.com/Genomon-Project/paplot Make plots from regions of BAM files Useful for very deep sequencing SamPlot: https://github.com/ryanlayer/samplot Visual inspection: Viper: https://github.com/MarWoes/viper https://www.biorxiv.org/content/early/2018/02/21/266262 Venn diagrams: https://github.com/vqf/nVenn BBC Visual and Data Journalism guide Cookbook: https://bbc.github.io/rcookbook/ bbplot package: https://github.com/bbc/bbplot Networks Human-like Orthogonal Layout: https://github.com/skieffer/hola Gephi: http://gephi.org/ Phylogenetic trees scripts from Holt lab: https://github.com/katholt/plotTree web-based http://microreact.org/showcase/ TreeLink: https://github.com/allendecid/TreeLink JavaScript libraries: https://github.com/tntvis R Comparison of libraries: http://lisacharlotterost.github.io/2016/05/17/one-chart-code/ http://ouzor.github.io/blog/2014/11/21/interactive-visualizations.html D3 from R: http://christophergandrud.github.io/networkD3/ SVG device: https://github.com/hadley/svglite/blob/master/README.md Multilayer data plotted on a Hilbert curve: http://www.bioconductor.org/packages/devel/bioc/html/HilbertCurve.html Visualize local epigenetic neighborhood of a SNP: http://bioconductor.org/packages/release/bioc/html/SNPhood.html CIRCOS plots: https://cggl.horticulture.wisc.edu/software/ Nice looking boxplots: https://github.com/mw55309/perceptions Make multiplanel figures from a combination of plot types: https://cran.r-project.org/web/packages/multipanelfigure/ KaryotypeR: http://bioconductor.org/packages/release/bioc/html/karyoploteR.html kmerPyramid: https://github.com/jkruppa/kmerPyramid shinyCircos: https://github.com/venyao/shinyCircos PCA+clustering: http://r-forge.r-project.org/projects/thresher/ Create pallete from colors in an image: https://github.com/AndreaCirilloAC/paletter clustree: Plot custer trees, for determining number of clusters to use: https://cran.r-project.org/web/packages/clustree/ ggplot2 Gallery of extensions: http://www.ggplot2-exts.org/gallery/ Themes Cowplot - improve default ggplot: http://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html ggplot2 theme for publication-quality figures: https://github.com/robertwilson190/ggplot2-theme HBR Themes: https://github.com/hrbrmstr/hrbrthemes xkcd-style plots: http://xkcd.r-forge.r-project.org/ Color palattes Color scales with clustering (would want to adapt this to ggplot): https://github.com/schne ggSci: https://ggsci.net/ ggtree: phylogenetic trees https://bioconductor.org/packages/release/bioc/html/ggtree.html geomnet: network visualization ggrepel: displaying text labels with minimal overlapping https://github.com/slowkow/ggrepelrd/d3-scale-cluster ggforce: many extensions to ggplot ggalt: many extensions to ggplot ggraph: plotting graphs/networks ggedit: interactive plot editor (Shiny gadget) ggRandomForests https://cran.r-project.org/web/packages/ggRandomForests/index.html superheat: pretty heatmaps https://github.com/rlbarter/superheat biplots https://github.com/vqv/ggbiplot logos: https://github.com/omarwagih/ggseqlogo Make any ggplot interactive: http://rpubs.com/turnersd/ggplotly-demo ggiraph: https://github.com/davidgohel/ggiraph Beeswarm: https://github.com/eclarke/ggbeeswarm: plot overlapping points without jitter ggtern: Ternary diagrams https://bitbucket.org/nicholasehamilton/ggtern ggpubr: Publication-ready plots, including barplot alternatives: http://www.sthda.com/english/rpkgs/ggpubr/ ggarrange, for flexible multi-panel figures joyplots: https://github.com/clauswilke/ggjoy colorbindr: test effect of colorblindness on readability of plots https://github.com/clauswilke/colorblindr Marginal plots: https://twitter.com/ClausWilke/status/900776341494276096 gggenes: https://github.com/wilkox/gggenes egg: combine multiple plots https://cran.rstudio.com/web/packages/egg/ ggSeqLogo: https://omarwagih.github.io/ggseqlogo/ Plotting background data: https://drsimonj.svbtle.com/plotting-background-data-for-groups-with-ggplot2 Theme using IBM Plex fonts: https://github.com/juliasilge/silgelib/blob/master/R/graphing.R Patchwork: https://github.com/thomasp85/patchwork ggupset: https://github.com/const-ae/ggupset Plot Types Correlograms: corrgram package DiagrammR http://rich-iannone.github.io/DiagrammeR/ Gene word clouds: http://genomespot.blogspot.co.uk/2014/10/geneclouds-unconventional-genetics-data.html?m=1 Upset plots: https://cran.r-project.org/web/packages/UpSetR/index.html Genomic data: https://bioconductor.org/packages/GenVisR hextri: multiclass hexagonal bins https://cran.r-project.org/web/packages/hextri/vignettes/hexbin-classes.html trellis: https://www.bioconductor.org/packages/release/bioc/html/gtrellis.html Complex heat maps: http://www.bioconductor.org/packages/devel/bioc/html/ComplexHeatmap.html Includes ability to generate upset plots Scatterplot Matrix: http://bl.ocks.org/mbostock/4063663 Beeswarm: https://flowingdata.com/2016/09/08/beeswarm-plot-in-r-to-show-distributions/ http://flowingdata.com/2016/10/25/r-graph-gallery/ Tilegrams: http://flowingdata.com/2016/10/13/tilegrams-in-r/ Karyotype plots: http://bioconductor.org/packages/devel/bioc/html/karyoploteR.html Ridgeplots (for multi-sample/category time-series data): https://github.com/halhen/viz-pub/blob/master/sports-time-of-day/2_gen_chart.R (TODO: make a dedicated geom for this) In python: https://github.com/mwaskom/seaborn/pull/1238 Half-violin plot: https://gist.github.com/jbburant/b3bd4961f3f5b03aeb542ed33a8fe062 Data Types Shushi: publication-quality figures from multiple data types https://github.com/dphansti/Sushi EpiViz: visualization of epigenomic data sets in R, http://epiviz.github.io/ Interaction data: https://github.com/kcakdemir/HiCPlotter Network visualization from R using vis.js: http://dataknowledge.github.io/visNetwork/ Differential expression from RNA-seq: http://bioconductor.org/packages/devel/bioc/html/Glimma.html Interactive https://gist.github.com/jcheng5/cbcc3b439a949deb544b Interactive charts: https://benjaminlmoore.wordpress.com/2015/05/19/interactive-charts-in-r/ http://www.htmlwidgets.org/showcase_leaflet.html Interactive ROC plots: https://github.com/sachsmc/plotROC Dull: create interactive web applications - https://github.com/nteetor/dull Python Graphic of python libraries for visualization: https://pbs.twimg.com/media/C9QBxNsU0AEokR_.jpg Seaborn: http://web.stanford.edu/~mwaskom/software/seaborn/index.html Tutorial: http://www.jesshamrick.com/2016/04/13/reproducible-plots/ Vincent: https://github.com/wrobstory/vincent Pyxley (Shiny for python): http://multithreaded.stitchfix.com/blog/2015/07/16/pyxley/ https://github.com/svaksha/pythonidae/blob/master/Computer-Graphics.md XKCD-style plots: http://jakevdp.github.io/blog/2012/10/07/xkcd-style-plots-in-matplotlib/?utm_content=buffera9a76 utm_medium=social utm_source=twitter.com utm_campaign=buffer Phylogenetic trees: http://etetoolkit.org/ Altair: https://altair-viz.github.io/ Visualization dashboard: https://github.com/facebookresearch/visdom Holoviews: http://holoviews.org/ GGplot clone: https://github.com/has2k1/plotnine https://python-graph-gallery.com/ Plot genome tracks: https://github.com/deeptools/pyGenomeTracks PyGal: http://pygal.org/en/stable/ Web based/interactive Gleam: https://github.com/dgrtwo/gleam Spyre: https://github.com/adamhajari/spyre Bokeh: http://bokeh.pydata.org/docs/user_guide/charts.html Dash: for building data-centric web apps https://github.com/plotly/dash 2D visualization of DNA sequences: https://github.com/Lab41/squiggle Dashboards: https://panel.pyviz.org/ Javascript vis.js Javascript libraries http://blog.webkid.io/javascript-chart-libraries/ D3.js: https://pub.beakernotebook.com/#/publications/560c9f9b-14e6-4d95-8e78-cc0a60bf4e5a?fullscreen=false Scraping/JS rendering: https://splash.readthedocs.org/en/latest/ Circos for Javascript: http://bioinfo.ibp.ac.cn/biocircos/ VegaLite: ggplot-like framework built on top of Vega, which is built on D3.js: https://vega.github.io/vega-lite/ GPU rendering: https://stardustjs.github.io Dataviz components built on top of D3: http://nivo.rocks/?ref=producthunt#/components Semiotic: https://emeeks.github.io/semiotic/#/ Genome browser components: https://github.com/Zhong-Lab-UCSD/Genomic-Interactive-Visualization-Engine Muze: https://www.charts.com/muze Examples Cool interactive visualization of differential data: http://graphics.wsj.com/gender-pay-gap/ Publication/Archiving Licenses: http://choosealicense.com/licenses/ APIs for literature search: http://libguides.mit.edu/apis Assessing credit for bioinformatics software authorship: http://depsy.org/ Icons for presentations: http://cameronneylon.net/blog/some-slides-for-granting-permissions-or-not-in-presentations/ Continuous analysis: https://github.com/greenelab/continuous_analysis A nice template for bootstrapping your own academic website using GitHub: https://academicpages.github.io/ Desktop app for searching/managing papers: https://github.com/codeforscience/sciencefair Recommend papers to cite based on your bibliography: http://labs.semanticscholar.org/citeomatic/ Word choices:Prepare papers for any journal format: https://typeset.io/ Slideboards: Mashup of slides and FAQ to explain a publication http://slideboard.herokuapp.com/ Generate manuscripts on GitHub: https://github.com/greenelab/manubot-rootstock General-purpose archival format: https://github.com/fair-research/bdbag/ Code/Data sharing DOI for code http://zenodo.org/ https://guides.github.com/activities/citable-code/ https://mozillascience.github.io/code-research-object/ Ruby library for fetching metadata for DOI: https://rubygems.org/gems/terrier CodeOcean: web platform to run algorithms https://codeocean.com GitHub: https://github.com/blog/1986-announcing-git-large-file-storage-lfs Amazon CodeCommit: http://aws.amazon.com/codecommit/ Data sharing Dat: https://datproject.org/ Academic Torrents: http://academictorrents.com/ FigShare: https://figshare.com/ Patterns for data sharing: http://project-if.github.io/data-permissions-catalogue/ Globus is an open source toolkit for transferring large data files; it implements the GridFTP protocol https://www.globus.org/ bbcp is multi-stream scp (for point-to-point large file transfer) https://www.olcf.ornl.gov/kb_articles/transferring-data-with-bbcp/ Quilt: package manager for data https://quiltdata.com/ Git plugin for version-control of data files: https://github.com/ctjacobs/git-rdm OSF API: https://test-api.osf.io/v2/docs/ Data project management: https://www.datazar.com Execute code from GitHub project with Jupyter notebooks: http://mybinder.org/ DataRetriever: http://www.data-retriever.org/ ThinkLab: https://thinklab.com/ ResearchObject: http://www.researchobject.org/ DataLAD: http://datalad.org/ Publishing/sharing Jupyter notebooks: https://kyso.io/ QRI: https://qri.io/ Journals Distill: Online, ML-focused journal http://distill.pub/journal/ JORS: https://openresearchsoftware.metajnl.com/ JOSS: http://joss.theoj.org/ Writing Scripts to identify \"bad smells\" in science writing (would want to convert this to python): http://matt.might.net/articles/shell-scripts-for-passive-voice-weasel-words-duplicates/ Collaborative writing DraftIn: https://draftin.com/documents Quip: http://quip.com Canvas: https://usecanvas.com/ Templates: InDesign template for preprint: https://github.com/cleterrier/ManuscriptTools/blob/master/biorxiv_template_CC2015.indd Rmarkdown templates for journal articles https://github.com/rstudio/rticles GitHub template for authoring papers: https://github.com/peerj/paper-now Two-column rmarkdown template: http://dirk.eddelbuettel.com/code/pinp.html Convert between (R)markdown and iPython notebooks: https://github.com/aaren/notedown Publication-quality tables for markdown: https://cran.r-project.org/web/packages/flextable/index.html Pandoc scholar: https://github.com/pandoc-scholar/pandoc-scholar Nice paper showing example of generating manuscripts for two different journals: https://peerj.com/preprints/2648.pdf Convert DOI to Bibtex entry Online equation editor: https://www.mathcha.io/ Simplified markup language for equations: http://asciimath.org/ Editoria: https://editoria.pub/ Texture: https://github.com/substance/texture Scientific writing template for R Markdown: https://rstudio.github.io/radix/ Posters Using RMarkdown: https://github.com/brentthorne/posterdown \"Better Poster\" template in R-markdown: https://github.com/brentthorne/posterdown Slides Using Markdown: https://github.com/arnehilmann/markdeck CV Using RMarkdown: https://ropenscilabs.github.io/vitae/ Promising methods without software implementation SDR gene set analysis http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-0928-6 ABBA http://abba.systems-genetics.net EpiTensor (3D genomes from 1D data): http://www.nature.com.ezproxy.nihlibrary.nih.gov/ncomms/2016/160310/ncomms10812/full/ncomms10812.html MOCHA: identifying modulators of transcriptional regulation from gene expression http://www.nature.com.ezproxy.nihlibrary.nih.gov/articles/srep22656 HiC deconvolution: http://www.pnas.org.ezproxy.nihlibrary.nih.gov/content/early/2016/03/04/1512577113.full MR_eQTL: https://github.com/PrincetonUniversity/MR_eQTL Method for multi-omics integration: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1122-6 Dynamic Bayesian network for predicting TF binding from DNase-seq: http://bioinformatics.oxfordjournals.org/content/26/12/i334.long De-noising ChIP-seq using DNNs: http://biorxiv.org/content/biorxiv/early/2016/05/07/052118.full.pdf DNA clustering: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-321 Bayesian identification of bifurcations in single-cell data: http://biorxiv.org/content/biorxiv/early/2016/09/21/076547.full.pdf Prediction of promoter-enhancer interactions: http://biorxiv.org/content/biorxiv/early/2016/11/02/085241.full.pdf Mocap: TFBS prediction from ATAC-seq http://biorxiv.org/content/biorxiv/early/2016/10/27/083998.full.pdf Statistical method to determine deviation from time linearity in epigenetic aging: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005183 Multi-tissue eQTL meta-analysis: http://biorxiv.org/content/early/2017/01/16/100701 Missing value imputation evaluator: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1429-3 Simulated annealing for biological network alignment: https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btx090/2996219/SANA-Simulated-Annealing-far-outperforms-many TF binding site prediction using memory-matching networks: https://arxiv.org/pdf/1702.06760.pdf V-ALIGN: alignmnet on genome graphs http://www.biorxiv.org/content/biorxiv/early/2017/04/06/124941.full.pdf Self-organizing maps for single-cell http://www.biorxiv.org/content/biorxiv/early/2017/04/05/124693.full.pdf New locality sensitive hashing method: http://www.biorxiv.org/content/biorxiv/early/2017/08/25/180471.full.pdf Extracting knowledge from RFs: https://www.biorxiv.org/content/early/2017/11/10/217695 Bloom filters without false-positives across a constrained set of values: http://lendulet.tmit.bme.hu/lendulet_website/wp-content/papercite-data/pdf/kiss2018bloom.pdf Adaptive pruning of boosted trees: https://arxiv.org/pdf/1805.07592.pdf Calculations to predict VAF for a mutation in a somatic sample: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6148761/ Zip Trees: https://arxiv.org/pdf/1806.06726.pdf Order Min Hash: https://www.biorxiv.org/content/10.1101/534446v1 Phasing by VAF in tumor-normal samples: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2753-1