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A continually expanding collection of RNA-seq tools

MIT License PR's Welcome

RNA-seq related tools and genomics data analysis resources. Please, contribute and get in touch! See MDmisc notes for other programming and genomics-related notes.

Table of content

Pipelines

Preprocessing

Analysis

Quality control

Clustering

  • clust - Python package for identification of consistently coexpressed clusters of genes, within and across datasets. Consensus clustering principle. ~50% of genes do not cluster well and thus shouldn't be considered. Compared with seven tools (cross-clustering, k-means, SOMs, MCL, HC, Click, WGCNA) using seven different cluster validation metrics. Outperforms all, produces more focused and significant functional enrichment results. https://github.com/BaselAbujamous/clust
    • Abu-Jamous, Basel, and Steven Kelly. “Clust: Automatic Extraction of Optimal Co-Expressed Gene Clusters from Gene Expression Data.” Genome Biology 19, no. 1 (December 2018). https://doi.org/10.1186/s13059-018-1536-8.

Timecourse

  • LPWC - Lag Penalized Weighted Correlation, a similarity measure to group pairs of time series that are not perfectly synchronized. Review of previous approaches (hierarchical clustering, partition-based, Bayesian models). Correlation-based, with the lag penalty for shift, two options to select it. Best for 5 or more time points, for shorter time course - either use high penalty or another tool STEM. Tested on simulated data (ImpulsDE data) using the adjusted Rand index. https://gitter-lab.github.io/LPWC/

  • Time course gene expression analysis review. Biological scenarios requiring a time-course, analytical approaches, Table 1 - software for time course analysis (EDGE, BETR, clustering tools, network analysis).

    • Bar-Joseph, Ziv, Anthony Gitter, and Itamar Simon. “Studying and Modelling Dynamic Biological Processes Using Time-Series Gene Expression Data.” Nature Reviews. Genetics 13, no. 8 (July 18, 2012): 552–64. https://doi.org/10.1038/nrg3244.

  • DREM 2.0 - time course analysis of gene expression data. Detects patterns of gene expression changes and the corresponding transcription factors driving them, motif discovery using protein-DNA (ChIP-seq, ChIP-chip, computational) data, differential motif analysis (DECOD method). Hidden Markov Model-based algorithm. Java tool, GUI and command line interface. http://wwhttp://sb.cs.cmu.edu/drem/

    • Schulz, Marcel H, William E Devanny, Anthony Gitter, Shan Zhong, Jason Ernst, and Ziv Bar-Joseph. “DREM 2.0: Improved Reconstruction of Dynamic Regulatory Networks from Time-Series Expression Data.” BMC Systems Biology 6, no. 1 (2012): 104. https://doi.org/10.1186/1752-0509-6-104.

Differential expression

Functional enrichment

  • GSEABenchmarking - an R package for systematic testing of gene set enrichment analyses. 10 major enrichment analyses tested on runtime, % significant sets, type I error rate, relevance to phenotype. Microarray and TCGA RNA-seq data. Best performing - overrepresentation analysis, aka Fisher's, hypergeometric test. Tweet by Levi Waldron

  • PaintOmics 3 - web tool for KEGG pathway enrichment analysis and visualization of gene expression (also, metabolite, protein, region-based data) over pathway diagrams. Competitors: MapMan, KaPPA-View, Pathview Web. Auto-detection of IDs. Analyzes fold change, time course. http://www.paintomics.org/

    • Hernández-de-Diego, Rafael, Sonia Tarazona, Carlos Martínez-Mira, Leandro Balzano-Nogueira, Pedro Furió-Tarí, Georgios J. Pappas, and Ana Conesa. “PaintOmics 3: A Web Resource for the Pathway Analysis and Visualization of Multi-Omics Data.” Nucleic Acids Research 46, no. W1 (July 2, 2018): W503–9. https://doi.org/10.1093/nar/gky466.

  • CEMiTool - gene co-expression analysis, reimplements WGCNA, includes selection of a soft-thresholding power using Cauchi distribution, gene enrichment analysis and, optionally, PPI network. Good overview of WGCNA algorithm. https://bioconductor.org/packages/release/bioc/html/CEMiTool.html

    • Russo, Pedro S. T., Gustavo R. Ferreira, Lucas E. Cardozo, Matheus C. Bürger, Raul Arias-Carrasco, Sandra R. Maruyama, Thiago D. C. Hirata, et al. “CEMiTool: A Bioconductor Package for Performing Comprehensive Modular Co-Expression Analyses.” BMC Bioinformatics 19, no. 1 (20 2018): 56. https://doi.org/10.1186/s12859-018-2053-1.

  • EnrichmentBrowser - R package for microarray/RNA-seq normalization, ID mapping, differential analysis, functional enrichment (many methods) and network analyses and visualization. https://bioconductor.org/packages/release/bioc/html/EnrichmentBrowser.html

    • Geistlinger, Ludwig, Gergely Csaba, and Ralf Zimmer. “Bioconductor’s EnrichmentBrowser: Seamless Navigation through Combined Results of Set- & Network-Based Enrichment Analysis.” BMC Bioinformatics 17 (January 20, 2016): 45. https://doi.org/10.1186/s12859-016-0884-1.

  • data_analysis_portals.xlsx - 25 data analysis portals, from the Metascape paper. Source

  • gProfileR - enrichment of gene lists, from GO to KEGG and others, organism-specific, ortholog conversion

  • Metascape - multi-gene lists functional analysis, auto gene ID recognition, >40 databases. http://metascape.org/gp/index.html#/main/step1

    • Zhou, Yingyao, Bin Zhou, Lars Pache, Max Chang, Alireza Hadj Khodabakhshi, Olga Tanaseichuk, Christopher Benner, and Sumit K. Chanda. “Metascape Provides a Biologist-Oriented Resource for the Analysis of Systems-Level Datasets.” Nature Communications 10, no. 1 (December 2019): 1523. https://doi.org/10.1038/s41467-019-09234-6.

  • Positional Gene Enrichment analysis of gene sets for high resolution identification of overrepresented chromosomal regions. http://silico.biotoul.fr/pge/

Transcription regulators

  • ChEA3 - predicting regulatory TFs for sets of user-provided genes. Improved backend reference gene set data (six datasets), ranking of the most significantly enriched TFs. Benchmarking against several other TF prioritization tools (overviewed in intro). Docker, https://hub.docker.com/r/maayanlab/chea3, API, web-interface and downloadable data https://amp.pharm.mssm.edu/chea3/

    • Keenan, Alexandra B., Denis Torre, Alexander Lachmann, Ariel K. Leong, Megan L. Wojciechowicz, Vivian Utti, Kathleen M. Jagodnik, Eryk Kropiwnicki, Zichen Wang, and Avi Ma’ayan. “ChEA3: Transcription Factor Enrichment Analysis by Orthogonal Omics Integration.” Nucleic Acids Research, May 22, 2019. https://doi.org/10.1093/nar/gkz446.

  • RABIT - find TFs regulating a list of genes. Integrated ChIP-seq and gene expression data, regression framework. Tested in experimental KO data, tumor-profiling cohorts. http://rabit.dfci.harvard.edu/

    • Jiang, Peng, Matthew L. Freedman, Jun S. Liu, and Xiaole Shirley Liu. “Inference of Transcriptional Regulation in Cancers.” Proceedings of the National Academy of Sciences 112, no. 25 (June 23, 2015): 7731–36. https://doi.org/10.1073/pnas.1424272112.

  • RcisTarget - finding enriched motifs in cis-regulatory regions in a gene list. https://github.com/aertslab/RcisTarget

Non-canonical RNAs

Alternative splicing

  • Benchmarking review, two types of alternative splicing analysis: differential splicing and differential isoform detection. DESeq2, DEXSeq, Limma and NOISeq perform well overall. https://github.com/gamerino/benchmarkingDiffExprAndSpl

    • Merino, Gabriela A, Ana Conesa, and Elmer A Fernández. “A Benchmarking of Workflows for Detecting Differential Splicing and Differential Expression at Isoform Level in Human RNA-Seq Studies.” Briefings in Bioinformatics 20, no. 2 (March 25, 2019): 471–81. https://doi.org/10.1093/bib/bbx122.

  • MAJIQ - local splicing variation analysis. Detects canonical and alternative splicing events. Quantifies as Percent Selected In (PSI). Differential splicing as delta PSI. Visualization using VOILA package. Python 3. https://majiq.biociphers.org/

    • Vaquero-Garcia, Jorge, Alejandro Barrera, Matthew R. Gazzara, Juan González-Vallinas, Nicholas F. Lahens, John B. Hogenesch, Kristen W. Lynch, and Yoseph Barash. “A New View of Transcriptome Complexity and Regulation through the Lens of Local Splicing Variations.” ELife 5 (February 1, 2016): e11752. https://doi.org/10.7554/eLife.11752.

  • MISO (Mixture-of-Isoforms) is a probabilistic framework that quantitates the expression level of alternatively spliced genes from RNA-Seq data, and identifies differentially regulated isoforms or exons across samples. - By modeling the generative process by which reads are produced from isoforms in RNA-Seq, the MISO model uses Bayesian inference to compute the probability that a read originated from a particular isoform.- MISO treats the expression level of a set of isoforms as a random variable and estimates a distribution over the values of this variable. - The estimation algorithm is based on sampling, and falls in the family of techniques known as Markov Chain Monte Carlo (“MCMC”). https://miso.readthedocs.io/en/fastmiso/

    • Katz, Yarden, Eric T. Wang, Edoardo M. Airoldi, and Christopher B. Burge. “Analysis and Design of RNA Sequencing Experiments for Identifying Isoform Regulation.” Nature Methods 7, no. 12 (December 2010): 1009–15. https://doi.org/10.1038/nmeth.1528.

  • RegTools - integration of somatic variants from DNA-seq and splice junctions from RNA-seq data to identify variants causing aberrant splicing in cancer. https://regtools.readthedocs.io/en/latest/

    • Feng, Yang-Yang, Avinash Ramu, Kelsy C Cotto, Zachary L Skidmore, Jason Kunisaki, Donald F Conrad, Yiing Lin, et al. “RegTools: Integrated Analysis of Genomic and Transcriptomic Data for Discovery of Splicing Variants in Cancer,” November 25, 2018. https://doi.org/10.1101/436634.

  • rMATS alternative splicing detection tool. Using paired samples.RNA-seq depth and alternative splicing power - 200M reads minimum. rMATS-turbo - 100X faster implementation, Tweet

    • Shen, Shihao, Juw Won Park, Zhi-xiang Lu, Lan Lin, Michael D. Henry, Ying Nian Wu, Qing Zhou, and Yi Xing. “RMATS: Robust and Flexible Detection of Differential Alternative Splicing from Replicate RNA-Seq Data.” Proceedings of the National Academy of Sciences of the United States of America 111, no. 51 (December 23, 2014): E5593-5601. https://doi.org/10.1073/pnas.1419161111.

  • tappAS - functional impact of alternative splicing. Input - transcript-level count matrix. http://tappas.org/

  • vast-tools - A toolset for profiling alternative splicing events in RNA-Seq data. https://github.com/vastgroup/vast-tools

    • Irimia, Manuel, Robert J. Weatheritt, Jonathan D. Ellis, Neelroop N. Parikshak, Thomas Gonatopoulos-Pournatzis, Mariana Babor, Mathieu Quesnel-Vallières, et al. “A Highly Conserved Program of Neuronal Microexons Is Misregulated in Autistic Brains.” Cell 159, no. 7 (December 18, 2014): 1511–23. https://doi.org/10.1016/j.cell.2014.11.035.

miRNAs

  • PharmacomiR - miRNA-drug associations analysis

  • microRNAome - read counts for microRNAs across tissues, cell-types, and cancer cell-lines, SummarizedExperiment R package

  • miRNAmeConverter - Convert miRNA Names to Different miRBase Versions

  • MIENTURNET - web tool for miRNA-target enrichment analysis, prioritization, network visualization, functional enrichment for microRNA target genes. http://userver.bio.uniroma1.it/apps/mienturnet/

    • Licursi, Valerio, Federica Conte, Giulia Fiscon, and Paola Paci. “MIENTURNET: An Interactive Web Tool for MicroRNA-Target Enrichment and Network-Based Analysis.” BMC Bioinformatics 20, no. 1 (December 2019): 545. https://doi.org/10.1186/s12859-019-3105-x.

  • miRDB - database for miRNA target prediction and functional annotations. The targets were predicted by MirTarget from RNA-seq and CLIP-seq data. Five species: human, mouse, rat, dog and chicken. Custom target prediction. Cell line-specific. Integrative analysis of target prediction and Gene Ontology data. http://mirdb.org

    • Chen, Yuhao, and Xiaowei Wang. “MiRDB: An Online Database for Prediction of Functional MicroRNA Targets.” Nucleic Acids Research, August 31, 2019, gkz757. https://doi.org/10.1093/nar/gkz757.

  • miRsponge - identification and analysis of miRNA sponge interaction networks and modules. Seven methods for miRNA sponge interaction detection (miRHomology, pc, sppc, hermes, ppc, muTaME, and cernia), and integrative method, description of each method. Four module detection methods (FN, MCL, LINKCOMM, MCODE), description of each. Enrichment analyses - disease (DO, DisGeNet, Network of Cancer Genes), functions (GO, KEGG, REACTOME). Survival analysis.

    • Zhang, Junpeng, Lin Liu, Taosheng Xu, Yong Xie, Chunwen Zhao, Jiuyong Li, and Thuc Duy Le. “MiRsponge: An R/Bioconductor Package for the Identification and Analysis of MiRNA Sponge Interaction Networks and Modules.” BioRxiv, January 1, 2018, 507749. https://doi.org/10.1101/507749.

  • MirGeneDB - standardized microRNA database, 1288 microRNA families across 45 species. Downloadable, FASTA, GFF, BED files. Nomenclature refs 19, 20. http://mirgenedb.org/

    • Fromm, Bastian, Diana Domanska, Eirik Hoye, Vladimir Ovchinnikov, Wenjing Kang, Ernesto Aparicio-Puerta, Morten Johansen, et al. “MirGeneDB 2.0: The Metazoan MicroRNA Complement.” BioRxiv, August 13, 2019. https://doi.org/10.1101/258749.

lncRNAs

  • lncRNAKB - database of long noncoding RNAs. lncRNAs are typically less conserved, expressed low on average and highly tissue-specific. Combines six resources (CHESS, LNCipedia, NONCODE, FANTOM, MiTranscriptome, BIGTranscriptome). Information about tissue-specific expression, eQTL, WGCNA co-expression to predict functions in a tissue-specific manner, random forest prediction of protein-coding score. Data: GTF gene annotation, tissue-specific expression (TPM, counts, eQTL). http://psychiatry.som.jhmi.edu/lncrnakb/, https://www.rna-seqblog.com/lncrnakb-a-comprehensive-knowledgebase-of-long-non-coding-rnas/

    • Seifuddin, Fayaz, Komudi Singh, Abhilash Suresh, Yun-Ching Chen, Vijender Chaitankar, Ilker Tunc, Xiangbo Ruan, et al. “LncRNAKB: A Comprehensive Knowledgebase of Long Non-Coding RNAs.” Preprint. Bioinformatics, June 13, 2019. https://doi.org/10.1101/669994.

  • UClncR - detecting and quantifying expression of unknown and known lncRNAs. Works for unstranded and stranded RNA-seq. Incorporates StringTie, Sebnif for novel lncRNA detection, iSeeRNA for assessing noncoding potential. Annotates lncRNAs by the nearby protein-coding genes. Tested on real data using Gencode annotations with parts of lncRNA annotations removed. http://bioinformaticstools.mayo.edu/research/uclncr-pipeline/

    • Sun, Zhifu, Asha Nair, Xianfeng Chen, Naresh Prodduturi, Junwen Wang, and Jean-Pierre Kocher. “UClncR: Ultrafast and Comprehensive Long Non-Coding RNA Detection from RNA-Seq.” Scientific Reports 7, no. 1 (December 2017): 14196. https://doi.org/10.1038/s41598-017-14595-3.

circRNAs

Gene fusion

Isoforms

CNVs and Structural variations

  • SuperFreq - CNV analysis from exome data adapted for RNA-seq data. Based on log fold-change variance estimation with the neighbour correction. R package, input - BAM files (reference normal needed), variant calls from samtools or other tools, output - visualization of CNAs, other variant-related plots

  • CaSpER - identification of CNVs from  RNA-seq data, bulk and single-cell (full-transcript only, like SMART-seq). Utilized multi-scale smoothed global gene expression profile and B-allele frequency (BAF) signal profile, detects concordant shifts in signal using a 5-state HMM (homozygous deletion, heterozygous deletion, neutral, one-copy-amplification, high-copy-amplification). Reconstructs subclonal CNV architecture for scRNA-seq data. Tested on GBM scRNA-seq, TCGA, other. Compared with HoneyBADGER. R code and tutorials

  • CNVkit-RNA - CNV estimation from RNA-seq data. Improved moving average approach, corrects for GC content, gene expression level, gene length, correlation of gene expression and CNV (estimated from TCGA). Docs, Video tutorial

  • InferCNV - Inferring copy number alterations from tumor single cell RNA-Seq data. R package. GitHub wiki. Part of Trinity Cancer Transcriptome Analysis Toolkit

  • SQUID - transcriptomic structural variation caller. Genome segment graph, then rearrange segments so that as many read alignments as possible are concordant with the rearranged sequence. Compared with MUMmer3, DELLY2, LUMPY in simulated settings, and with SOAPfuse, deFuse, FusionCatcher, JAFFA, INTEGRATE tools using real data. https://github.com/Kingsford-Group/squid

  • transindel - Indel caller for DNA-seq or RNA-seq, https://github.com/cauyrd/transIndel

Networks

  • GENIE3 - random forest regression detection of gene modules. Input - expression matrix, output - gene x gene square co-regulation matrix. https://github.com/aertslab/GENIE3

Integrative

  • DIABLO - multi-omics analysis method. Overview of previous methods (SNF, Bayesian Consensus Clustering, NMF, JIVE, sGCCA, MOFA, others). Method extends sGCCA multivariate dimensionality reduction that uses SVD and selects co-expressed (correlated) variables from several omics datasets. Methods, model, iterative solution. Design matrix specifies which omics datasets are connected. Variable selection for biomarkers identification.  Visualization options. Part of mixOmics R package, http://mixomics.org/, https://mixomicsteam.github.io/Bookdown/intro.html

    • Singh, Amrit, Casey P Shannon, Benoît Gautier, Florian Rohart, Michaël Vacher, Scott J Tebbutt, and Kim-Anh Lê Cao. “DIABLO: An Integrative Approach for Identifying Key Molecular Drivers from Multi-Omics Assays.” Edited by Inanc Birol. Bioinformatics 35, no. 17 (September 1, 2019): 3055–62. https://doi.org/10.1093/bioinformatics/bty1054.

  • MANCIE - matrix analysis and normalization by concordant information enhancement. Bias correction and data integration of distinct genomic profiles on the same samples. Match matrices by rows, run correlation for each row, replace the associated row with modified values using a PCA procedure, Methods. Tested on integration of DHS and gene expression data, TCGA and METABRIC data. R package, https://cran.r-project.org/web/packages/MANCIE/

  • JIVE - Joint and Individual Variation Explained. Decomposition of (X) multiple (i) omics datasets into three terms: low-rank (constrained) matrices capturing joint variation (J), plus structured variation (A_i) and residual noise. Data are row-centered and scaled by its total variation. Main constrain: the rows of joint and individual matrices should be orthogonal. Estimate matrices by iteratively minimizing ||R||^2 (R=X-J-A). Relationship to PCA, CCA, PLS. Illustrated on TCGA GBM gene expression, methylation, and miRNA data, with interpretation. Matlab code https://genome.unc.edu/jive/, r.jive package, https://cran.r-project.org/web/packages/r.jive/vignettes/BRCA_Example.html

    • Lock, Eric F., Katherine A. Hoadley, J. S. Marron, and Andrew B. Nobel. “JOINT AND INDIVIDUAL VARIATION EXPLAINED (JIVE) FOR INTEGRATED ANALYSIS OF MULTIPLE DATA TYPES.” The Annals of Applied Statistics 7, no. 1 (March 1, 2013): 523–42. https://doi.org/10.1214/12-AOAS597.

  • List of software packages for multi-omics analysis, by Mike Love. https://github.com/mikelove/awesome-multi-omics. Slides for the talk "Assessing consistency of unsupervised multi-omics methods". https://docs.google.com/presentation/d/1QAaweEc32JzhWHl7YenLdT9w8JUjwaTExe_uve2s22U/edit#slide=id.p

Classification

Visualization

Data

Genes

  • List of gene lists for genomic analyses - GitHub repo with tab-separated annotated lists

  • CREEDS - database of manually (and automatically) extracted gene signatures. Single gene perturbations, disease signatures, single drug perturbations. Batch effect correction, when necessary. Overall, good agreement with MSigDb C2. Characteristic Direction (CD) method to detect differential genes. API access in R

  • OGEE - Online GEne Essentiality database for multiple organisms, including human and mouse

  • HGNChelper - Identify and Correct Invalid HGNC Human Gene Symbols and MGI Mouse Gene Symbols

  • gencode_regions - Extract 3'UTR, 5'UTR, CDS, Promoter, Genes, Introns etc from GTF files, by Saket Choudhary

  • Extract intron boundaries per transcript

  • CHESS - Comprehensive Human Expressed SequenceS, database of novel genes, identified from GTeX data, protein-coding and lncRNA

  • GTEx Visualizations - web-based visualization tools for exploring tissue-specific gene expression and regulation

  • ideogram - Chromosome visualization with D3.js. Examples. ideogRam R wrapper

  • Enhancer-promoter (EP) pairs from Thurman et al., (2012)

  • pysradb - Python package for interacting with SRAdb and downloading datasets from SRA, by Saket Choudhary. Documentation

Misc