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/PacBio
  - 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
- Genome browsers
- 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
- Blog post on lessons and tools for filtering of gnomAD annotations https://www.whiffinlab.org/blog/six-lessons-for-variant-interpretation
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://gdc.cancer.gov/
- 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/
RNA Atlas https://hgserver1.amc.nl/cgi-bin/r2/main.cgi?&dscope=RNAATLAS&option=about_dscope
COLO829 sequenced on multiple short- and long-read platforms https://www.biorxiv.org/content/10.1101/2020.10.15.340497v1?rss=1
HGDP
- SV calls ftp://ngs.sanger.ac.uk/scratch/project/team19/HGDP_SV/

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
Useful cheat-sheets: https://github.com/NBISweden/GAAS/tree/master/annotation/CheatSheet

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
Wrapper around AMQs to increase speed and decrease false-positive rate https://github.com/lrobidou/findere

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/
VarFish: https://github.com/bihealth/varfish-server

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
CIViC: https://civicdb.org/home
Search publications and clinical trials by genes/variants/drugs https://vist.informatik.hu-berlin.de/
Allele frequencies https://www.ncbi.nlm.nih.gov/snp/docs/gsr/alfa/
Harmonized cancer variants https://search.cancervariants.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
  - https://github.com/divonlan/genozip
SAM/BAM/CRAM
- Read filtering and profiling: https://github.com/jwalabroad/VariantBam
- SQL-like querying https://github.com/maragkakislab/samql/
- encoding Nanopore data: https://github.com/novoalab/modPhred
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
- Index and subset VCF files: https://github.com/tomkurowski/tersect
- Variant of BCF format encoding genotypes using sparse vectors https://github.com/statgen/savvy
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
- https://github.com/lh3/bedtk
- perturbation testing of region sets: http://bedshift.databio.org/en/latest/
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/
Generic format for storing alignment incidence data https://github.com/churchill-lab/alntools

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
- Wavelet https://github.com/william-denault/WaveletScreening
- Integrates total and allele-specific read counts https://github.com/hakyimlab/mixqtl
- Apex https://corbinq.github.io/apex/
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/
- https://github.com/liliulab/treemap
Ancestry
- SNPweights: https://www.hsph.harvard.edu/alkes-price/software/
- Visualize population structure: https://github.com/kr-colab/popvae
- https://github.com/sriramlab/SCOPE
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
- DENTIST: QC for summary statistics

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
Imputation QC: https://github.com/inzilico/imputeqc
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
Clustering of peaks: https://github.com/BackofenLab/StoatyDive
On graphs: https://github.com/pinellolab/GRAFIMO

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
- SVs https://github.com/gersteinlab/SVFX
- https://data.broadinstitute.org/alkesgroup/LDSCORE/Kim_annotboost/
- Frequency conservation score http://bioinfo.cnic.es/FCS
- Rare variants: http://www.liulab.science/metarnn.html
- SVs
  - https://github.com/gersteinlab/SVFX
  - https://github.com/andrewSharo/StrVCTVRE
- Splice variants https://github.com/gagneurlab/MMSplice_MTSplice
- Missense variants https://github.com/jiaying2508/LYRUS
- Long reads: https://github.com/TheJacksonLaboratory/SvAnna
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
- https://github.com/GuanLab/Leopard
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
Perturbation of genomic intervals http://bedshift.databio.org/en/latest/

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
- check/correct https://github.com/mutalyzer/mutalyzer
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
Pharmacogenomic: https://github.com/PharmGKB/PharmCAT
SV
- AnnotSV: http://lbgi.fr/AnnotSV/
- Curation/annotation by depth: https://github.com/brentp/duphold
Protein sequence and structure annotation for variants https://www.ebi.ac.uk/thornton-srv/databases/cgi-bin/DisaStr/GetPage.pl?varmap=TRUE
https://github.com/mulinlab/VarNote
Allele Dispersion Score: how population-specific is a variant? https://github.com/wassermanlab/ADS

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
- Across large number of datasets https://github.com/kamimrcht/REINDEER
- denoising https://github.com/Christina-hshi/CQF-deNoise
k-mer hashing: https://github.com/czbiohub/kmer-hashing
Sequence dimensionality reduction
- Strobemers https://www.biorxiv.org/content/10.1101/2021.01.28.428549v2
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/
multi-mapping reads
- MMR: https://github.com/ratschlab/mmr
- SmartMap: https://github.com/shah-rohan/SmartMap
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
Genomic intervals
- bedtools http://bedtools.readthedocs.io/en/latest/
- bedops alternative/additional BED operations http://bedops.readthedocs.io/en/latest/
- comparison of interval sets: https://github.com/deepstanding/seqpare
- Intersection and visualization of multiple gene/region sets: https://bitbucket.org/CBGR/intervene
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
Liftover
- Segment liftover: https://github.com/baudisgroup/segment-liftover
- Liftover alignments from one reference to another: https://github.com/CMU-SAFARI/AirLift
- Identying unstable positions between genome versions https://github.com/cathaloruaidh/genomeBuildConversion
Recover unaligned reads: https://github.com/VCCRI/Scavenger
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
Coverage
- Fast coverage estimate from BAM index: https://github.com/brentp/goleft/tree/master/indexcov
- Quantification and normalization of coverage peaks: https://github.com/ncbi/BAMscale
- Library for distributed coverage calculation using Spark https://github.com/ZSI-Bio/bdg-sequila
- https://github.com/telatin/bamtocov
Additional tools built on GATK4 https://bimberlab.github.io/DISCVRSeq/toolDoc/index.html
Query compressed GFF/GTF files https://github.com/qm2/gpress
Sort reads and remove duplicates (for compression and to accelerate alignment): https://github.com/bioinformatics-polito/BioSeqZip
Detect clusters of coordinates https://bioconductor.org/packages/release/bioc/html/fcScan.html
Create consensus genomes https://github.com/kaminow/consdb
Score arbitrary regions by sequence difference from a reference population https://github.com/machine2learn/QGS
Remap alignments from one reference to another https://github.com/CMU-SAFARI/AirLift
Sample quality classifier https://github.com/salbrec/seqQscorer
grabseqs: https://github.com/louiejtaylor/grabseqs
Read depth
- https://bioconductor.org/packages/release/bioc/html/megadepth.html
- https://bitbucket.org/ustenzel/bathometer/src

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
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
Identify inter-sample contamination: https://github.com/Griffan/VerifyBamID
https://github.com/DecodeGenetics/read_haps
Learn features from manual ENCODE dataset classifications and use to automate QC https://github.com/salbrec/seqQscorer
RabbitQC
determine organism, assay type, and sequencing platform from k-mer frequencies https://github.com/DaehwanKimLab/seqwho
https://github.com/reneshbedre/HTSeqQC
https://github.com/Griffan/FASTQuick
Empirical quality filtering: https://github.com/bodeolukolu/ngsComposer
https://github.com/DaehwanKimLab/seqwho

ChIP-seq

Pre-processing
- Quality assessment: https://github.com/rnakato/SSP
- Allocation of multi-mapping reads: https://github.com/keleslab/permseq
- Mapping: https://github.com/haowenz/chromap
Peak calling
- GC-aware peak caller: https://bioconductor.org/packages/devel/bioc/html/gcapc.htmlR
- 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
- https://github.com/biocore-ntnu/epic2
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
Peak quantification (for any data type) https://github.com/ncbi/BAMscale
QC https://bioconductor.org/packages/devel/bioc/html/ChIC.html
MAnorm2 https://github.com/tushiqi/MAnorm2
Nucleotide-matched background sequences for motif enrichment https://bitbucket.org/CBGR/biasaway/src/master/
Motif discovery:
- K-mer based without peak calling https://github.com/menzel/nopeak

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
Predict targets of accessible regions: https://github.com/cole-trapnell-lab/cicero-release
Correcting transposition bias in ATAC-seq: https://www.biorxiv.org/content/biorxiv/early/2019/01/
ATAC-seq
- MACS settings https://github.com/macs3-project/MACS/discussions/435
- ATAC-seq peak caller: https://github.com/LiuLabUB/HMMRATAC
- esATAC: ATAC-Seq pipeline https://bioconductor.org/packages/release/bioc/html/esATAC.html
- ATAC-Seq QC: https://bioconductor.org/packages/release/bioc/html/ATACseqQC.html
- Denoising of low-covereage/noisy ATAC-seq: https://github.com/clara-genomics/AtacWorks 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/
- PEPATAC http://pepatac.databio.org/en/latest/
Deep learning-based peak caller https://github.com/LHentges/LanceOtron
Deep learning-based denoiser https://github.com/clara-parabricks/AtacWorks

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
Call CNVs and translocations https://github.com/parklab/HiNT
qc: https://github.com/cerebis/qc3C

DNA

Read Filtering
- Removing redundant reads from deep sequencing data: https://git.informatik.uni-kiel.de/axw/Bignorm
- Duplicate removal
  - Pardre: https://sourceforge.net/projects/pardre/
  - Nubeam-dedup: https://github.com/daihang16/nubeamdedup
  - https://github.com/yuansliu/minirmd
Read 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
- https://github.com/fkallen/CARE
- Long read
  - https://github.com/lbroseus/TALC
  - https://github.com/DecodeGenetics/Ratatosk
Alignment
- Recommendations:
  - https://github.com/CCDG/Pipeline-Standardization
  - These are optimized for germline variant calling, however there are a few interesting pointss
    - 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
- SNP-aware: https://github.com/weiquan/SALT
- Long read
  - https://github.com/MohammadJRS/SureMap
  - https://github.com/isovic/graphmap
  - https://github.com/ocxtal/minialign
  - for repeat regions https://github.com/marbl/Winnowmap
  - https://github.com/NWPU-903PR/smsMap
  - Vulcan - dual-mode alignment https://gitlab.com/treangenlab/vulcan
  - https://github.com/ChaissonLab/LRA
- 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
  - Selecting variants for graph genome: https://www.biorxiv.org/content/early/2018/04/30/311720
  - https://github.com/maickrau/GraphAligner
- Pan-genome
  - Build pan-genome index https://github.com/SJTU-CGM/HUPAN
  - PanGenie https://bitbucket.org/jana_ebler/pangenie/src/master/
- 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
  - Realign poorly aligning reads to population-specific major-allele references https://www.biorxiv.org/content/10.1101/2020.03.03.975219v3.full.pdf
- Mapper with accurate MAPQ scores https://github.com/gui11aume/mmp
- Heuristic-free aligner with guaranteed optimal alignment https://github.com/langmead-lab/vargas
- https://github.com/ekg/wfmash
- Other
  - Optical computing: https://www.optalysys.com/
  - FPGA: http://edicogenome.com/dragen-bioit-platform/
  - Completeness statistics for MSA: https://github.com/thomaskf/AliStat
  - Suffix array with learned data model to accelerate queries: https://github.com/mkirsche/sapling
  - Prediction of identity scores for large datasets https://github.com/BioinformaticsToolsmith/Identity
  - Puffaligner: https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings
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
  - Ra
  - Raven
  - https://github.com/phasebook/phasebook
- Pre-masking repeats: https://github.com/mills-lab/PALMER
- Pan-genome graphs
  - https://github.com/lh3/minigraph
  - Extracting paths using haplotype information https://github.com/tomokveld/CHOP
  - https://github.com/LeilyR/Multi-genome-Reference
- New measure of assembly contiguity https://lh3.github.io/2020/04/08/a-new-metric-on-assembly-contiguity
- Linked reads: https://github.com/lauren-mak/Ariadne
Post-alignment
- QC: https://github.com/achon/sramm
- Base quality recalibration:
  - https://github.com/swainechen/lacer
  - https://github.com/broadinstitute/firepony
- 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
- Compression of quality scores:
  - Lossy https://github.com/jkbonfield/crumble
  - Lossless: https://github.com/SCUT-CCNL/LCQS
- Denoising (error correction + quality score adjustment): https://github.com/ihwang/SAMDUDE
- Fix reference allele bias of the Isaac aligner https://github.com/danchubb/FixVAF
- Merge duplicate reads: https://github.com/alevar/tiebrush
- remove cDNA contamination https://github.com/rheinbaylab/cDNA-detector
- SQL-like read querying and filtering https://github.com/maragkakislab/samql/
- Long read
  - Realigns reads in segmental duplication regions https://gitlab.com/tprodanov/duplomap
Variant calling
- QC
  - https://github.com/nebfield/snpQT
- Reference-free:
  - Kevlar: https://github.com/dib-lab/kevlar
  - RUFUS: https://github.com/jandrewrfarrell/RUFUS
- Deep learning
  - 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
    - Trio calling pipeline https://github.com/VCCRI/dv-trio/
  - Deep learning will increase accuracy in the nest GATK release: https://gatkforums.broadinstitute.org/gatk/discussion/10996/deep-learning-in-gatk4
  - Nvidia https://github.com/clara-genomics/DL4VC
  - Train DNN variant calling models (commercial): https://magnolia.sh/
- 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
- 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/
  - https://github.com/atgenomix/connectedreads
- 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
  - Qscore correction prior to SNP calling from Nanopore https://github.com/rocioespci/minIONLogReg
  - ONT https://www.biorxiv.org/content/10.1101/2020.10.22.350009v1
- Hybrid
  - https://github.com/anands-repo/hello
- 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
- Align and call simultaneously: https://github.com/hsinnan75/MapCaller
- For reads with UMIs: https://gitlab.com/vincent-sater/umi-varcal_v3
- Varlociraptor: call given candidate variants and filter based on FDR https://varlociraptor.github.io/docs/calling/
  - Works for somatic or an arbitrary experimental design (described in a config file)
- Wrapper around multiple variant callers that simplifies parallelization https://github.com/lh3/unicall
- For finding mutations in mutated sample (vs normal): https://github.com/ykat0/bivartect
- Rare variants: https://github.com/frankMusacchia/VarGenius-HZD
- Single-cell
  - https://github.com/prosolo/prosolo
de novo mutations (trio variant calling)
- https://github.com/jbelyeu/unfazed
- https://github.com/sbg/geck
- Prediction of de novo mutations from trio variant calling data https://github.com/james-guevara/synthdnm
- https://github.com/yongzhuang/DNMFilter_Indel
- Using multiple reference assemblies: https://github.com/mcveanlab/Corticall
- filtering: https://github.com/brentp/slivar
- DeepTrio https://github.com/google/deepvariant/tree/r1.1/deeptrio
- https://github.com/Genome-Bioinformatics-RadboudUMC/DeNovoCNN
- VG-DeepTrio https://github.com/vgteam/vg_wdl
Variant filtering/post-processing
- 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
- IonTorrent: https://www.github.com/aditya-88/intelli-ngs
- 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
  - https://figshare.shef.ac.uk/articles/Suspect_loci_BED_files/9995945
- Another RF classifier: https://github.com/avallonking/ForestQC
- snpToolkit: https://snptoolkit.readthedocs.io/en/latest/index.html
- VariFAST https://github.com/bioxsjtu/VariFAST
- Sample control variants: https://github.com/mulinlab/vSampler
- Detect contamination https://github.com/Ancestry/VCFcontam
- Merge SNVs into MNVs https://github.com/Sentieon/sentieon-scripts/tree/master/merge_mnp
- Long read: https://github.com/arangrhie/merfin/
Genotyping
- ebGenotyping: https://cran.r-project.org/web/packages/ebGenotyping/ebGenotyping.pdf
- FastGT: http://bioinfo.ut.ee/FastGT/
- Compression and search of genotype data: http://sun.aei.polsl.pl/REFRESH/gtc
- Using variation graph: https://github.com/Kingsford-Group/variantstore
- 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
- https://bioinfo.ut.ee/KATK/
- https://github.com/ivargr/kage
- Low-pass/GBS
  - https://github.com/stevenandrewyates/GBSmode
Imputation
- loimpute: impute from low-pass sequencing
- GLIMPSE: https://odelaneau.github.io/GLIMPSE/
- Impute parental genotypes from siblings https://github.com/AlexTISYoung/SNIPar
- QUILT: https://github.com/rwdavies/QUILT
- https://github.com/OpenMendel/MendelImpute.jl
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
- Reporting
  - https://github.com/JH-Zhou/HandyCNV
  - 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
- https://github.com/sandmanns/CopyDetective
- 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
- https://github.com/kunalkathuria/SVXplorer
- https://lh3.github.io/2021/01/11/minigraph-as-a-multi-assembly-sv-caller
- https://github.com/hartwigmedical/hmftools/tree/master/sv-linx
- https://github.com/ETCHING-team/ETCHING
- https://github.com/weiyuchung/CONY
- from haploid or diploid genome-genome alignments https://github.com/eldariont/svim-asm
- merging, filtering, feature extraction: https://github.com/dermasugita/Viola-SV
- https://github.com/kcleal/dysgu
- https://github.com/tf2/CNest
- https://github.com/alok123t/HyINDEL
- https://github.com/yaozhong/deepIntraSV
- https://github.com/TheJacksonLaboratory/JAX-CNV
- CNVnator
  - Python extension: https://github.com/abyzovlab/CNVpytor
- Ensemble caller:
  - https://github.com/HaoKeLab/ensembleCNV
  - Parliament2 https://github.com/dnanexus/parliament2
  - Different variant callers excel at calling different types of SVs: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1720-5
  - https://github.com/wonderful1/CNV-PG
  - https://bitbucket.org/sonnhammergroup/metacnv
- Population-scale:
  - https://github.com/hall-lab/svtools
  - STIX: A tool for determining the frequency of SVs in population data https://github.com/ryanlayer/stix
  - https://github.com/kehrlab/PopDel
- ML/Deep learning-based callers:
  - https://github.com/CSuperlei/DeepSV
  - Specifically for duplications and deletions: https://github.com/tomh1lll/dudeml
  - DL-CNV: https://www.aimspress.com/article/10.3934/mbe.2020011/fulltext.html
  - https://github.com/lxwgcool/EigenDel
  - https://github.com/mlinderm/npsv
  - https://github.com/luojunwei/BreakNet
  - https://github.com/SimCab-CHU/ifCNV
- Fusion detection: https://github.com/sgilab/JuLI
- Genotyping
  - Benchmarks: https://academic.oup.com/gigascience/article/8/9/giz110/5565134
  - Alignment-free SV genotyping: https://github.com/Parsoa/NebulousSerendipity
  - Genotyping tandem repeats: https://github.com/mcfrith/tandem-genotypes
  - Graph-based genotyping: https://github.com/illumina/paragraph
  - Long-read: https://github.com/llecompte/SVJedi
  - Deep learning-based https://github.com/mchowdh200/samplot-ml
- Annotation
  - ACMG https://github.com/Genotek/ClassifyCNV
- Merging
  - Jasmine
  - https://github.com/stjude/indelPost
  - https://github.com/dermasugita/Viola-SV
  - https://github.com/maiziex/MARS
- Linked read:
  - https://github.com/sgreer77/gemtools
  - https://github.com/BilkentCompGen/valor
- Savvy: uses off-target reads https://github.com/rdemolgen/SavvySuite
- svtk: utilies for consolidating, filtering, resolving, and annotating structural variants
  - https://github.com/xjtu-omics/Makohttps://github.com/talkowski-lab/svtk
- Alignment-free:
  - https://github.com/smortezah/smashpp
  - https://github.com/Parsoa/Nebula (SVs)
  - https://github.com/nicolaprezza/ebwt2InDel
- UMIs https://gitlab.com/pierrejulien.viailly/mcna/
- 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
  - Deep learning on paired WGS-WES https://github.com/ciceklab/DECoNT
  - https://github.com/fredsanto/coverageMaster
  - ML model for polishing WES CNV calls https://github.com/ciceklab/DECoNT
  - https://gitlab.com/sequentiateampublic/isocnv
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
- https://github.com/rlmcl/rescan
- https://github.com/bahlolab/superSTR
HLA
- arcasHLA: https://github.com/RabadanLab/arcasHLA
- https://github.com/WansonChoi/HATK
mtDNA
- https://github.com/linzhi2013/MitoZ
- Haplogroup classification: https://github.com/seppinho/haplogrep-cmd
- https://github.com/KCCG/mity
Repeat calling
- REPdenovo: https://github.com/Reedwarbler/REPdenovo
Polygenic scores
- https://github.com/brentp/nimpress
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/targeted
  - Canary: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1950-z
  - pyAmpli: https://mbeyens.github.io/pyAmpli/
  - amplimap: https://github.com/koelling/amplimap
- 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
- PECaller/PEMapper: population caller, does not produce BAM files https://github.com/wingolab-org/pecaller
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
- https://github.com/daviddaiweizhang/fraposa
- https://github.com/AI-sandbox/gnomix
- https://github.com/williamslab/papi
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
- Using RNA-seq allele-specific expression https://github.com/0xTCG/haptreex/
- For de novo SNVs and SVs in trios https://github.com/jbelyeu/unfazed
- Consensus: https://github.com/ziadbkh/consHap
- Estimation from allele frequency data https://github.com/MartaPelizzola/haploSep
- Long reads: https://github.com/twolinin/LongPhase/
- https://github.com/rhysf/HaplotypeTools
- https://github.com/dmiller903/trioPhaser
- https://github.com/twolinin/LongPhase/
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/
- pangenome graphs
  - https://github.com/lh3/minigraph
  - https://github.com/pangenome/pggb
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
Dimensionality reduction
- https://github.com/yhg926/public_kssd
- https://github.com/medvedevgroup/UST/
Sex determination
- https://github.com/liusihan/seGMM
- XYalign: sex chromosome copy number estimation from sequencing https://github.com/SexChrLab/XYalign
Other
- VCF compression and data extraction: https://github.com/kedartatwawadi/GTRAC
- Run length encoded multi-sample BWT + server: https://github.com/wtsi-svi/ReadServer
- Liftover between genome assemblies: https://github.com/verilylifesciences/genomewarp
- Generate artificial human genomes https://gitlab.inria.fr/ml_genetics/public/artificial_genomes
- Simluate gene-disrupting variants https://github.com/wassermanlab/GeneBreaker

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
https://github.com/Boyle-Lab/TRACE

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
- Low-input: https://github.com/Dangertrip/LiBis
- https://github.com/smithlabcode/abismal
- https://daehwankimlab.github.io/hisat2/hisat3n
- Handle multi-mapping reads https://github.com/lmylynn/EM-MUL
- Abismal: mapping with a 2-letter alphabet https://github.com/smithlabcode/abismal
SNP calling
- BS-SNPer: fast SNP calling from bisulfite-converted sequencing reads https://github.com/hellbelly/BS-Snper
Haplotype calling
- https://github.com/ZhouQiangwei/MethHaplo
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
- Differential allele-specific methylation: https://github.com/markrobinsonuzh/DAMEfinder
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
Pipelines
- https://github.com/MarWoes/wg-blimp
- https://github.com/Christensen-Lab-Dartmouth/MethylNet
- https://github.com/jlab-code/MethylStar
- https://github.com/NuttyLogic/BSBolt
- https://github.com/sarahet/RLM
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/PacBio

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/
SV calling: https://github.com/tjiangHIT/cuteSV
Removing low-quality read segments: https://bitbucket.org/berkeleylab/jgi-miniscrub/src/master/

RNA

Reviews
- https://arxiv.org/abs/2010.02391
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
- Prefilter reads to discard those that won't align to genes of interest https://github.com/AlgoLab/shark
- Use hash of reference transcriptome to convert between transcriptome builds https://bioconductor.org/packages/tximeta
- infer strandedness https://github.com/betsig/how_are_we_stranded_here
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:
  - http://bioinf.wehi.edu.au/featureCounts/
  - featureCounts replacement with better handling of multi-mapping reads: https://bitbucket.org/mzytnicki/multi-mapping-counter/src/master/
  - Faster version of HTSeq/featureCount: https://github.com/qinzhu/VERSE
- D-GEX: Quantification of whole-transcriptome gene expression from landmark genes https://github.com/uci-cbcl/D-GEX
- 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
- XAEM: http://fafner.meb.ki.se/biostatwiki/xaem/
- Segmentation https://github.com/HCBravoLab/yanagi
- Group and collapse transcripts for more accurate counts https://github.com/COMBINE-lab/terminus
- https://github.com/Kingsford-Group/subgraphquant
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
- Abundance estimates using likelihood approximation https://github.com/dcjones/polee
- Merge paired-ends into fragments https://github.com/Shao-Group/rnabridge-align
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
- Fusion detection: https://github.com/bcgsc/pavfinder
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/
- With or w/o genotypes: https://gitlab.com/evigorito/baseqtl
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
- BLMRM https://github.com/JingXieMIZZOU/BLMRM
- https://github.com/santiagosnchez/CompMap
- https://github.com/ncjllld/byase
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
- Alternative splicing https://github.com/raphaelleman/SpliceLauncher
- splice QTL pipeline https://github.com/guigolab/sqtlseeker2-nf
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
- Bridging of paired-end reads prior to assembly https://github.com/Shao-Group/coral
- Ensemble:
  - https://sourceforge.net/projects/transcriptomeassembly/files/TransBorrow/
  - http://bioinfolab.unl.edu/emlab/consemble/
- Graph: https://github.com/jonassibbesen/vgrna-project-paper
- MINTIE: https://github.com/Oshlack/MINTIE
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
    - https://github.com/suhrig/arriba
Long read
- bambu: reference-guided transcript discovery and quantification
- https://github.com/vladimirsouza/lrRNAseqVariantCalling
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)
- Predict CNA from gene expression: http://wang-lab.ust.hk/software/Software.html
- Predict age: https://github.com/LBMC/RAPToR

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
Loom: http://linnarssonlab.org/loompy/index.html
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
- Predict coverage increase from deeper sequencing: https://github.com/smithlabcode/preseq
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
- Benchmarking https://github.com/cellgeni/batchbench
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
- https://github.com/dnbaker/minicore
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
Transcriptome assembly
- https://github.com/bcgsc/RNA-Bloom
Sketching: https://github.com/bendemeo/hopper
Allele-specific expression
- SCALE accounts for "burstiness" of transcription: https://github.com/yuchaojiang/SCALE
- scBASE: https://github.com/churchill-lab/scBASE
- https://github.com/anna-saukkonen/PAC
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
- pegasus: https://github.com/klarman-cell-observatory/pegasus
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
- Variant calling from RNA-seq: https://github.com/adamcornish/red_panda
- https://github.com/HorvathLab/NGS/tree/master/SCReadCounts
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
- https://github.com/bioinfoDZ/RISC/
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
- Minimum metadata for reporting scRNA-seq experiments: https://arxiv.org/abs/1910.14623
DNA-seq
- haplotype inference: https://github.com/raphael-group/chisel
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
  - https://github.com/QuKunLab/APEC
- CNV https://github.com/spcdot/CopyscAT
HLA
- https://github.com/10XGenomics/scHLAcount

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
- Remove DNA contamination using microhaplotypes https://www.biorxiv.org/content/10.1101/2020.12.18.423488v1
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.
Correct FFPE artifacts
- https://github.com/QingliGuo/FFPEsig
- https://github.com/mmaitenat/ideafix
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
  - Supports linked read data
- 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
  - https://github.com/mingyi-wang/somatic-combiner
- 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
- Optimized for rare variants in ultra-deep sequencing http://github.com/cibiobcg/abemus
- Learn mutation profile to refine varinat calls: https://github.com/bmannakee/batcaver
- Reference-free: https://github.com/izaak-coleman/GeDi
- FFPE samples https://github.com/adamallo/ITHE
- https://github.com/genetronhealth/uvc
- Validating somatic variants in tumor-normal sequencing using differences in k-mer profiles between tumor and normal samples https://github.com/compbio/kmerVC
- https://github.com/genetronhealth/uvc
- Tumor-only
  - https://github.com/pllittle/UNMASC
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
- Normalization: https://github.com/baudisgroup/mecan4cna
- Allele-specific CNV calling for multi-sample tumor/normal https://github.com/imgag/ClinCNV
- Background correction prior to CNV calling: https://github.com/mskilab/dryclean
- https://github.com/ExpressionAnalysis/CNV_Radar
- QC: https://caravagnalab.github.io/CNAqc/
- Long read: https://github.com/friend1ws/nanomonsv
- Low-depth https://github.com/crukci-bioinformatics/rascal
- Integrate WGS and RNA-seq for gene fusion detection: https://github.com/princessmaximacenter/fusion-sq
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
- Likelihood model for high-depth sequencing https://github.com/RainyEricYe/LFMD
- LOHGIC germline classifier and LOH prediction: https://github.com/KhiabanianLab/Genomics_Oncology_Platform/blob/master/Platform.py
- https://github.com/cpwardell/FiNGS
- SV simulation/filtering for FFPE data https://bioconductor.org/packages/release/bioc/html/SimFFPE.html
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
  - https://github.com/mwsill/RFpurify
- DeepPurity https://www.biorxiv.org/content/10.1101/805135v1.full.pdf (no open-source code)
Joint calling
- CN, tumor purity, and LOH https://bioconductor.org/packages/release/bioc/html/PureCN.html
- purity, ploidy, and allele-specific CN https://github.com/Crick-CancerGenomics/ascat
- purity, ploidy, SV, and CN in tumor-normal: https://github.com/hartwigmedical/gridss-purple-linx
Tumor Muational Burden
- http://moat.gersteinlab.org/
- ecTMB: https://github.com/bioinform/ecTMB
- https://bioserver.ieo.it/shiny/app/tmbler
- Normalizing mutation count by read depth https://github.com/OmnesRes/depth_norm
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
- https://www.bioconductor.org/packages/release/bioc/html/musicatk.html
- https://github.com/ma-compbio/MutSpace
- https://github.com/UMCUGenetics/CUPLR
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
- Tracking variants across longitudinal samples: https://github.com/ChristofferFlensburg/SuperFreq
- Reconstruction of clone- and haplotype-specific karyotypes https://github.com/aganezov/RCK
MSI
- Sonics: https://github.com/kzkedzierska/sonics
- MSIsensor-pro https://github.com/xjtu-omics/msisensor-pro
- https://github.com/YixuanWang1120/ELMSI (requires tumor-normal)
- https://github.com/GeneCast/btMSI-CAST
- https://github.com/mskcc/mimsi
- https://github.com/861934367/MSIFinder
MMR
- https://github.com/xqzou/MMRDetect
Neoantigen
- pVACtools: https://github.com/griffithlab/pVACtools
- NeoPredPipe: https://github.com/MathOnco/NeoPredPipe
- https://github.com/neoanthill/neoANT-HILL
- Using phased somatic mutations https://github.com/pdxgx/neoepiscope
- https://github.com/koesterlab/microphaser
- https://github.com/icbi-lab/nextNEOpi
HRD
- Genomic Scar Analysis - call copy number and detect HRD from targeted capture sequencing https://github.com/shaominghui/GSA
ctDNA/cfDNA
- TNER: background error reduction https://github.com/ctDNA/TNER
- SV calling https://github.com/liuhc8/Aperture
- TruNeo pipeline https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03869-9
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
- Hotspots https://github.gersteinlab.org/nimbus
- Phase germline variants using tumor copy number https://github.com/bowentan/CNAHap

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
- Association of gene expression with SVs https://github.com/chadcreighton/SVExpress
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
- index: https://github.com/Malfoy/BLight
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/
Load PLINK data into Dask arrays https://github.com/limix/pandas-plink

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/
Sequence embedding
- https://github.com/krishnanlab/pecanpy
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
- Estimate k: https://github.com/RoheLab/gdim
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/
Library for preprocessing various NGS formats for input into Keras or other DL libraries: https://github.com/BIMSBbioinfo/janggu

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
- secml: https://gitlab.com/secml/secml
- https://github.com/BIMSBbioinfo/janggu
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

Genome browsers

Graph
- https://github.com/MoMI-G/MoMI-G
- https://github.com/docpaa/mumdex/

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/
https://github.com/harbourlab/SparK

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/
Git for data (tables): https://github.com/liquidata-inc/dolt
Data version control https://dvc.org/

Journals

Distill: Online, ML-focused journal http://distill.pub/journal/
JORS: https://openresearchsoftware.metajnl.com/
JOSS: http://joss.theoj.org/
Open source publishing platform: https://libero.pub/

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/
Extension of MarkDown that handles diagraMs, equations, etc https://casual-effects.com/markdeep/

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
Variance stabilization of genomic signals https://www.biorxiv.org/content/10.1101/2020.01.31.929174v1
Fast computation of edit distance https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3184-8

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Metadata

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/PacBio

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

Genome browsers

Networks

Phylogenetic trees

R

ggplot2

Plot Types

Data Types

Interactive

Python

Javascript

Examples

Publication/Archiving

Code/Data sharing

Journals

biotools
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