Van den Berge K, Hembach KM, Soneson C, Tiberi S, Clement L, Love MI, Patro R, Robinson MD. RNA sequencing data: Hitchhikers guide to expression analysis. Annu Rev Biomed Data Sci. 2019;2(1):139–73. https://doi.org/10.1146/annurev-biodatasci-072018-021255.
Article
Google Scholar
Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szcześniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for RNA-seq data analysis. Genome Biol. 2016;17(1):13. https://doi.org/10.1186/s13059-016-0881-8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Love MI, Anders S, Kim V, Huber W. RNA-Seq workflow: gene-level exploratory analysis and differential expression. F1000Research. 2015;4:1070. https://doi.org/10.12688/f1000research.7035.1.
Article
PubMed
PubMed Central
Google Scholar
Chen Y, Lun ATL, Smyth GK. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research. 2016;5:1438. https://doi.org/10.12688/f1000research.8987.2.
Article
PubMed
PubMed Central
Google Scholar
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25–9. https://doi.org/10.1038/75556.
Article
CAS
PubMed
PubMed Central
Google Scholar
Carbon S, Douglass E, Dunn N, Good B, Harris NL, Lewis SE, Mungall CJ, Basu S, Chisholm RL, Dodson RJ, Hartline E, Fey P, Thomas PD, Albou LP, Ebert D, Kesling MJ, Mi H, Muruganujan A, Huang X, Poudel S, Mushayahama T, Hu JC, LaBonte SA, Siegele DA, Antonazzo G, Attrill H, Brown NH, Fexova S, Garapati P, Jones TEM, Marygold SJ, Millburn GH, Rey AJ, Trovisco V, Dos Santos G, Emmert DB, Falls K, Zhou P, Goodman JL, Strelets VB, Thurmond J, Courtot M, Osumi DS, Parkinson H, Roncaglia P, Acencio ML, Kuiper M, Lreid A, Logie C, Lovering RC, Huntley RP, Denny P, Campbell NH, Kramarz B, Acquaah V, Ahmad SH, Chen H, Rawson JH, Chibucos MC, Giglio M, Nadendla S, Tauber R, Duesbury MJ, Del NT, Meldal BHM, Perfetto L, Porras P, Orchard S, Shrivastava A, Xie Z, Chang HY, Finn RD, Mitchell AL, Rawlings ND, Richardson L, Sangrador-Vegas A, Blake JA, Christie KR, Dolan ME, Drabkin HJ, Hill DP, Ni L, Sitnikov D, Harris MA, Oliver SG, Rutherford K, Wood V, Hayles J, Bahler J, Lock A, Bolton ER, De Pons J, Dwinell M, Hayman GT, Laulederkind SJF, Shimoyama M, Tutaj M, Wang SJ, D’Eustachio P, Matthews L, Balhoff JP, Aleksander SA, Binkley G, Dunn BL, Cherry JM, Engel SR, Gondwe F, Karra K, MacPherson KA, Miyasato SR, Nash RS, Ng PC, Sheppard TK, Shrivatsav Vp A, Simison M, Skrzypek MS, Weng S, Wong ED, Feuermann M, Gaudet P, Bakker E, Berardini TZ, Reiser L, Subramaniam S, Huala E, Arighi C, Auchincloss A, Axelsen K, Argoud GP, Bateman A, Bely B, Blatter MC, Boutet E, Breuza L, Bridge A, Britto R, Bye-A-Jee H, Casals-Casas C, Coudert E, Estreicher A, Famiglietti L, Garmiri P, Georghiou G, Gos A, Gruaz-Gumowski N, Hatton-Ellis E, Hinz U, Hulo C, Ignatchenko A, Jungo F, Keller G, Laiho K, Lemercier P, Lieberherr D, Lussi Y, Mac-Dougall A, Magrane M, Martin MJ, Masson P, Natale DA, Hyka NN, Pedruzzi I, Pichler K, Poux S, Rivoire C, Rodriguez-Lopez M, Sawford T, Speretta E, Shypitsyna A, Stutz A, Sundaram S, Tognolli M, Tyagi N, Warner K, Zaru R, Wu C, Chan J, Cho J, Gao S, Grove C, Harrison MC, Howe K, Lee R, Mendel J, Muller HM, Raciti D, Van Auken K, Berriman M, Stein L, Sternberg PW, Howe D, Toro S, Westerfield M. The gene ontology resource: 20 years and still going strong. Nucleic Acids Res. 2019;47(D1):330–8. https://doi.org/10.1093/nar/gky1055.
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):353–61. https://doi.org/10.1093/nar/gkw1092.
Article
CAS
Google Scholar
Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47(D1):590–5. https://doi.org/10.1093/nar/gky962.
Article
CAS
Google Scholar
Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B, Milacic M, Roca CD, Rothfels K, Sevilla C, Shamovsky V, Shorser S, Varusai T, Viteri G, Weiser J, Wu G, Stein L, Hermjakob H, D’Eustachio P. The reactome pathway knowledgebase. Nucleic Acids Res. 2018;46(D1):649–55. https://doi.org/10.1093/nar/gkx1132.
Liberzon A., Subramanian A., Pinchback R., Thorvaldsdottir H., Tamayo P., Mesirov J.P. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–40. https://doi.org/10.1093/bioinformatics/btr260.
Article
CAS
PubMed
PubMed Central
Google Scholar
Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database Hallmark gene set collection. Cell Syst. 2015;1(6):417–25. https://doi.org/10.1016/j.cels.2015.12.004.
Article
CAS
PubMed
PubMed Central
Google Scholar
Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):1002375. https://doi.org/10.1371/journal.pcbi.1002375.
Article
CAS
Google Scholar
Xie C, Jauhari S, Mora A. Popularity and performance of bioinformatics software: the case of gene set analysis. BMC Bioinform. 2021;22(1):191. https://doi.org/10.1186/s12859-021-04124-5.
Article
Google Scholar
Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545–50. https://doi.org/10.1073/pnas.0506580102.
Article
CAS
PubMed
PubMed Central
Google Scholar
Nguyen T, Mitrea C, Draghici S. Network-based approaches for pathway level analysis. Curr Protoc Bioinform. 2018;61(1):8–25182524. https://doi.org/10.1002/cpbi.42.
Article
Google Scholar
Geistlinger L, Csaba G, Santarelli M, Ramos M, Schiffer L, Turaga N, Law C, Davis S, Carey V, Morgan M, Zimmer R, Waldron L. Toward a gold standard for benchmarking gene set enrichment analysis. Brief Bioinform. 2020. https://doi.org/10.1093/bib/bbz158.
Article
PubMed Central
Google Scholar
Villaveces JM, Koti P, Habermann BH. Tools for visualization and analysis of molecular networks, pathways, and -omics data. Adv Appl Bioinform Chem. 2015;8(1):11–22. https://doi.org/10.2147/AABC.S63534.
Article
PubMed
PubMed Central
Google Scholar
Supek F, Škunca N, Visualizing GO annotations. In: The gene ontology handbook, vol. 1446. Humana Press; 2017. p. 207–20. https://doi.org/10.1007/978-1-4939-3743-1.
Maere S, Heymans K, Kuiper M. BiNGO: a Cytoscape plugin to assess over representation of gene ontology categories in biological networks. Bioinformatics. 2005;21(16):3448–9. https://doi.org/10.1093/bioinformatics/bti551.
Article
CAS
PubMed
Google Scholar
Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z, Galon J. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091–3. https://doi.org/10.1093/bioinformatics/btp101.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mlecnik B, Galon J, Bindea G. Comprehensive functional analysis of large lists of genes and proteins. J Proteomics. 2018;171:2–10. https://doi.org/10.1016/j.jprot.2017.03.016.
Article
CAS
PubMed
Google Scholar
Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinform. 2009;10(1):48. https://doi.org/10.1186/1471-2105-10-48.
Article
Google Scholar
Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE. 2011;6(7):21800. https://doi.org/10.1371/journal.pone.0021800.
Article
CAS
Google Scholar
Walter W, Sánchez-Cabo F, Ricote M. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics. 2015;31(17):2912–4. https://doi.org/10.1093/bioinformatics/btv300.
Article
CAS
PubMed
Google Scholar
Tian T, Liu Y., Yan H, You Q., Yi X., Du Z., Xu W., Su Z. AgriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucleic Acids Res. 2017;45(W1):122–9. https://doi.org/10.1093/nar/gkx382.
Article
CAS
Google Scholar
Wei Q, Khan IK, Ding Z, Yerneni S, Kihara D. NaviGO: interactive tool for visualization and functional similarity and coherence analysis with gene ontology. BMC Bioinform. 2017;18(1):177. https://doi.org/10.1186/s12859-017-1600-5.
Article
CAS
Google Scholar
Liao Y, Wang J, Jaehnig EJ, Shi Z, Zhang B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 2019;47(W1):199–205. https://doi.org/10.1093/nar/gkz401.
Article
CAS
Google Scholar
Kuznetsova I, Lugmayr A, Siira SJ, Rackham O, Filipovska A. CirGO: an alternative circular way of visualising gene ontology terms. BMC Bioinform. 2019;20(1):84. https://doi.org/10.1186/s12859-019-2671-2.
Article
Google Scholar
Zhu J, Zhao Q, Katsevich E, Sabatti C. Exploratory gene ontology analysis with interactive visualization. Sci Rep. 2019;9(1):1–9. https://doi.org/10.1038/s41598-019-42178-x.
Article
CAS
Google Scholar
Hale ML, Thapa I, Ghersi D. FunSet: an open-source software and web server for performing and displaying gene ontology enrichment analysis. BMC Bioinform. 2019;20(1):359. https://doi.org/10.1186/s12859-019-2960-9.
Article
Google Scholar
Federico A, Monti S. hypeR: an R package for geneset enrichment workflows. Bioinformatics. 2020;36(4):1307–8. https://doi.org/10.1093/bioinformatics/btz700.
Article
CAS
PubMed
Google Scholar
Liu X, Han M, Zhao C, Chang C, Zhu Y, Ge C, Yin R, Zhan Y, Li C, Yu M, He F, Yang X. KeggExp: a web server for visual integration of KEGG pathways and expression profile data. Bioinformatics. 2019;35(8):1430–2. https://doi.org/10.1093/bioinformatics/bty798.
Article
CAS
PubMed
Google Scholar
Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, Benner C, Chanda SK. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523. https://doi.org/10.1038/s41467-019-09234-6.
Article
CAS
PubMed
PubMed Central
Google Scholar
Ulgen E, Ozisik O, Sezerman O.U. pathfindR: an R package for comprehensive identification of enriched pathways in omics data through active subnetworks. Front Genet. 2019;10(SEP):1–33. https://doi.org/10.3389/fgene.2019.00858.
Article
CAS
Google Scholar
Ge SX, Jung D, Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics. 2020;36(8):2628–9. https://doi.org/10.1093/bioinformatics/btz931.
Article
CAS
PubMed
Google Scholar
Brionne A, Juanchich A, Hennequet-Antier C. ViSEAGO: a bioconductor package for clustering biological functions using gene ontology and semantic similarity. BioData Min. 2019;12(1):1–13. https://doi.org/10.1186/s13040-019-0204-1.
Article
CAS
Google Scholar
Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, von Mering C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):607–13. https://doi.org/10.1093/nar/gky1131.
Article
CAS
Google Scholar
Tokar T, Pastrello C, Jurisica I. GSOAP: a tool for visualisation of gene set over-representation analysis. Bioinformatics. 2020. https://doi.org/10.1093/bioinformatics/btaa001.
Article
PubMed
PubMed Central
Google Scholar
Wang G, Oh D-H, Dassanayake M. GOMCL: a toolkit to cluster, evaluate, and extract non-redundant associations of gene ontology-based functions. BMC Bioinform. 2020;21(1):139. https://doi.org/10.1186/s12859-020-3447-4.
Article
Google Scholar
Kim J, Yoon S, Nam D. netGO: R-Shiny package for network-integrated pathway enrichment analysis. Bioinformatics. 2020. https://doi.org/10.1093/bioinformatics/btaa077.
Article
PubMed
PubMed Central
Google Scholar
Calura E, Martini P. Summarizing RNA-Seq data or differentially expressed genes using gene set, network, or pathway analysis. In: Picardi E, editor. RNA bioinformatics, chap 9, vol. 2284. Humana; 2021. p. 147–79. https://doi.org/10.1007/978-1-0716-1307-8.
Chapter
Google Scholar
Akhmedov M, Martinelli A, Geiger R, Kwee I. Omics Playground: a comprehensive self-service platform for visualization, analytics and exploration of big omics data. NAR Genom Bioinform. 2020;2(1):1–10. https://doi.org/10.1093/nargab/lqz019.
Article
CAS
Google Scholar
Sandve GK, Nekrutenko A, Taylor J, Hovig E. Ten simple rules for reproducible computational research. PLoS Comput Biol. 2013;9(10):1003285. https://doi.org/10.1371/journal.pcbi.1003285.
Article
Google Scholar
Marini F, Binder H. Development of applications for interactive and reproducible research: a case study. Genom Computl Biol. 2016;3(1):39. https://doi.org/10.18547/gcb.2017.vol3.iss1.e39.
Article
Google Scholar
Brito JJ, Li J, Moore JH, Greene CS, Nogoy NA, Garmire LX, Mangul S. Recommendations to enhance rigor and reproducibility in biomedical research. GigaScience. 2020;9(6):1–6. https://doi.org/10.1093/gigascience/giaa056.
Article
Google Scholar
Knuth DE. Literate programming. Comput J. 1984;27(2):97–111. https://doi.org/10.1093/comjnl/27.2.97.
Article
Google Scholar
Marini F, Binder H. pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components. BMC Bioinform. 2019;20(1):331. https://doi.org/10.1186/s12859-019-2879-1.
Article
Google Scholar
Marini F, Linke J, Binder H. ideal: an R/Bioconductor package for interactive differential expression analysis. BMC Bioinform. 2020;21(1):565. https://doi.org/10.1186/s12859-020-03819-5.
Article
Google Scholar
Poplawski A, Marini F, Hess M, Zeller T, Mazur J, Binder H. Systematically evaluating interfaces for RNA-seq analysis from a life scientist perspective. Brief Bioinform. 2016;17(2):213–23. https://doi.org/10.1093/bib/bbv036.
Article
CAS
PubMed
Google Scholar
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry R, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shannon P, Smyth GK, Tenenbaum D, Waldron L, Morgan M. Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods. 2015;12(2):115–21. https://doi.org/10.1038/nmeth.3252.
Article
CAS
PubMed
PubMed Central
Google Scholar
Amezquita R, Carey V, Carpp L, Geistlinger L, Lun A, Marini F, Rue-Albrecht K, Risso D, Soneson C, Waldron L, Pagès H, Smith M, Huber W, Morgan M, Gottardo R, Hicks S. Orchestrating single-cell analysis with bioconductor. BioRxiv. 2019. https://doi.org/10.1101/590562.
Article
Google Scholar
Chang W, Cheng J, Allaire J, Xie Y, McPherson J. Shiny: web application framework for R. (2020). R package version 1.4.0.2. https://CRAN.R-project.org/package=shiny.
Alasoo K, Rodrigues J, Mukhopadhyay S, Knights AJ, Mann AL, Kundu K, Hale C, Dougan G, Gaffney DJ. Shared genetic effects on chromatin and gene expression indicate a role for enhancer priming in immune response. Nat Genet. 2018;50(3):424–31. https://doi.org/10.1038/s41588-018-0046-7.
Article
CAS
PubMed
PubMed Central
Google Scholar
Mohebiany AN, Ramphal NS, Karram K, Di Liberto G, Novkovic T, Klein M, Marini F, Kreutzfeldt M, Härtner F, Lacher SM, Bopp T, Mittmann T, Merkler D, Waisman A. Microglial A20 protects the brain from CD8 T-cell-mediated immunopathology. Cell Rep. 2020;30(5):1585–15976. https://doi.org/10.1016/j.celrep.2019.12.097.
Article
CAS
PubMed
Google Scholar
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8.
Article
CAS
PubMed
PubMed Central
Google Scholar
Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, Amode MR, Armean IM, Azov AG, Bennett R, Bhai J, Billis K, Boddu S, Marugán JC, Cummins C, Davidson C, Dodiya K, Fatima R, Gall A, Giron CG, Gil L, Grego T, Haggerty L, Haskell E, Hourlier T, Izuogu OG, Janacek SH, Juettemann T, Kay M, Lavidas I, Le T, Lemos D, Martinez JG, Maurel T, McDowall M, McMahon A, Mohanan S, Moore B, Nuhn M, Oheh DN, Parker A, Parton A, Patricio M, Sakthivel MP, Abdul Salam AI, Schmitt BM, Schuilenburg H, Sheppard D, Sycheva M, Szuba M, Taylor K, Thormann A, Threadgold G, Vullo A, Walts B, Winterbottom A, Zadissa A, Chakiachvili M, Flint B, Frankish A, Hunt SE, IIsley G, Kostadima M, Langridge N, Loveland JE, Martin FJ, Morales J, Mudge JM, Muffato M, Perry E, Ruffier M, Trevanion SJ, Cunningham F, Howe KL, Zerbino DR, Flicek P. Ensembl 2020. Nucleic Acids Res. 2019;48(D1):682–8. https://doi.org/10.1093/nar/gkz966.
Article
CAS
Google Scholar
Frankish A, Diekhans M, Ferreira AM, Johnson R, Jungreis I, Loveland J, Mudge JM, Sisu C, Wright J, Armstrong J, Barnes I, Berry A, Bignell A, Carbonell Sala S, Chrast J, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, García Girón C, Gonzalez JM, Grego T, Hardy M, Hourlier T, Hunt T, Izuogu OG, Lagarde J, Martin FJ, Martínez L, Mohanan S, Muir P, Navarro FCP, Parker A, Pei B, Pozo F, Ruffier M, Schmitt BM, Stapleton E, Suner MM, Sycheva I, Uszczynska-Ratajczak B, Xu J, Yates A, Zerbino D, Zhang Y, Aken B, Choudhary JS, Gerstein M, Guigó R, Hubbard TJP, Kellis M, Paten B, Reymond A, Tress ML, Flicek P. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 2019;47(D1):766–73. https://doi.org/10.1093/nar/gky955.
Article
CAS
Google Scholar
Granjon D. bs4Dash: a ‘Bootstrap 4’ Version of ‘shinydashboard’. 2019. https://rinterface.github.io/bs4Dash/index.html, https://github.com/RinteRface/bs4Dash.
Chang W, Borges Ribeiro B. Shinydashboard: create dashboards with ‘Shiny’. (2018). R package version 0.7.1. https://CRAN.R-project.org/package=shinydashboard.
Ganz C. rintrojs: a wrapper for the intro. js library. J Open Source Softw. 2016;1(6):2016. https://doi.org/10.21105/joss.00063.
Article
Google Scholar
Alexa A, Rahnenführer J, Lengauer T. Improved scoring of functional groups from gene expression data by decorrelating GO graph structure. Bioinformatics. 2006;22(13):1600–7. https://doi.org/10.1093/bioinformatics/btl140.
Article
CAS
PubMed
Google Scholar
Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS J Integr Biol. 2012;16(5):284–7. https://doi.org/10.1089/omi.2011.0118.
Article
CAS
Google Scholar
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. https://doi.org/10.1038/nprot.2008.211.
Article
CAS
Google Scholar
Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A, McDermott MG, Monteiro CD, Gundersen GW, Ma’ayan A. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):90–7. https://doi.org/10.1093/nar/gkw377.
Reimand J, Isserlin R, Voisin V, Kucera M, Tannus-Lopes C, Rostamianfar A, Wadi L, Meyer M, Wong J, Xu C, Merico D, Bader GD. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc. 2019;14(2):482–517. https://doi.org/10.1038/s41596-018-0103-9.
Article
CAS
PubMed
PubMed Central
Google Scholar
Raudvere U, Kolberg L, Kuzmin I, Arak T, Adler P, Peterson H, Vilo J. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47(W1):191–8. https://doi.org/10.1093/nar/gkz369.
Article
CAS
Google Scholar
Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021. https://doi.org/10.1101/060012.
Article
Google Scholar
Agarwala R, Barrett T, Beck J, Benson DA, Bollin C, Bolton E, Bourexis D, Brister JR, Bryant SH, Canese K, Charowhas C, Clark K, DiCuccio M, Dondoshansky I, Feolo M, Funk K, Geer LY, Gorelenkov V, Hlavina W, Hoeppner M, Holmes B, Johnson M, Khotomlianski V, Kimchi A, Kimelman M, Kitts P, Klimke W, Krasnov S, Kuznetsov A, Landrum MJ, Landsman D, Lee JM, Lipman DJ, Lu Z, Madden TL, Madej T, Marchler-Bauer A, Karsch-Mizrachi I, Murphy T, Orris R, Ostell J, O’Sullivan C, Palanigobu V, Panchenko AR, Phan L, Pruitt KD, Rodarmer K, Rubinstein W, Sayers EW, Schneider V, Schoch CL, Schuler GD, Sherry ST, Sirotkin K, Siyan K, Slotta D, Soboleva A, Soussov V, Starchenko G, Tatusova TA, Todorov K, Trawick BW, Vakatov D, Wang Y, Ward M, Wilbur WJ, Yaschenko E, Zbicz K. Database resources of the national center for biotechnology information. Nucleic Acids Res. 2017;45(D1):12–7. https://doi.org/10.1093/nar/gkw1071.
Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, Stein TI, Nudel R, Lieder I, Mazor Y, Kaplan S, Dahary D, Warshawsky D, Guan-Golan Y, Kohn A, Rappaport N, Safran M, Lancet D. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinform. 2016;54(1):1–30113033. https://doi.org/10.1002/cpbi.5.
Article
Google Scholar
Gamazon ER, Segrè AV, van de Bunt M, Wen X, Xi HS, Hormozdiari F, Ongen H, Konkashbaev A, Derks EM, Aguet F, Quan J, Nicolae DL, Eskin E, Kellis M, Getz G, McCarthy MI, Dermitzakis ET, Cox NJ, Ardlie KG. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat Genet. 2018;50(7):956–67. https://doi.org/10.1038/s41588-018-0154-4.
Article
CAS
PubMed
PubMed Central
Google Scholar
Xie Y. Dynamic Documents with R and Knitr, p. 188. Chapman & Hall/CRC; 2013. https://doi.org/10.18637/jss.v056.b02. arXiv:arXiv:1501.0228. http://www.crcpress.com/product/isbn/9781482203530.
Rule A, Birmingham A, Zuniga C, Altintas I, Huang SC, Knight R, Moshiri N, Nguyen MH, Rosenthal SB, Pérez F, Rose PW. Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. PLoS Comput Biol. 2019;15(7):1–8. https://doi.org/10.1371/journal.pcbi.1007007.
Article
CAS
Google Scholar
Stodden V, Miguez S. Best practices for computational science: software infrastructure and environments for reproducible and extensible research. J Open Res Softw. 2014;2(1):21. https://doi.org/10.5334/jors.ay.
Article
Google Scholar
Rue-Albrecht K, Marini F, Soneson C, Lun ATL. iSEE: interactive summarized experiment explorer. F1000Research. 2018;7:741. https://doi.org/10.12688/f1000research.14966.1.
Article
PubMed
PubMed Central
Google Scholar
Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C, Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017. https://doi.org/10.1038/nmeth.4197. arXiv:1505.02710.
Lun ATL, Chen Y, Smyth GK. It’s DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. In: Mathé E, Davis S, editors. Statistical genomics, chap. 19. Humana Press; 2016. p. 391–416.
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform. 2013;14:12. https://doi.org/10.1186/1471-2105-14-7.
Article
Google Scholar
Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE. 2010;5(11):13984. https://doi.org/10.1371/journal.pone.0013984.
Article
CAS
Google Scholar
Pomaznoy M, Ha B, Peters B. GOnet: a tool for interactive gene ontology analysis. BMC Bioinform. 2018;19(1):1–8. https://doi.org/10.1186/s12859-018-2533-3.
Article
CAS
Google Scholar
Almende BV, Thieurmel B, Robert T. visNetwork: network visualization using ‘vis.js’ library. (2019). R package version 2.0.9. https://CRAN.R-project.org/package=visNetwork.
Domagalski R, Neal ZP, Sagan B. Backbone: an R package for extracting the backbone of bipartite projections. PLoS ONE. 2021;16(1):0244363. https://doi.org/10.1371/journal.pone.0244363.
Article
CAS
Google Scholar
Geistlinger L, Csaba G, Zimmer R. Bioconductor’s EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis. BMC Bioinform. 2016;17(1):45. https://doi.org/10.1186/s12859-016-0884-1.
Alhamdoosh M, Ng M, Wilson NJ, Sheridan JM, Huynh H, Wilson MJ, Ritchie ME. Combining multiple tools outperforms individual methods in gene set enrichment analyses. Bioinformatics. 2016;33:623. https://doi.org/10.1093/bioinformatics/btw623.
Article
CAS
Google Scholar
Yoon S, Kim J, Kim S-K, Baik B, Chi S-M, Kim S-Y, Nam D. GScluster: network-weighted gene-set clustering analysis. BMC Genom. 2019;20(1):352. https://doi.org/10.1186/s12864-019-5738-6.
Article
Google Scholar