GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data | BMC Bioinformatics

  • 1.

    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. doi.org/10.1146/annurev-biodatasci-072018-021255.

    Article 

    Google Scholar
     

  • 2.

    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. doi.org/10.1186/s13059-016-0881-8.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 3.

    Love MI, Anders S, Kim V, Huber W. RNA-Seq workflow: gene-level exploratory analysis and differential expression. F1000Research. 2015;4:1070. doi.org/10.12688/f1000research.7035.1.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 4.

    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. doi.org/10.12688/f1000research.8987.2.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 5.

    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. doi.org/10.1038/75556.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 6.

    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. doi.org/10.1093/nar/gky1055.

  • 7.

    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. doi.org/10.1093/nar/gkw1092.

    CAS 
    Article 

    Google Scholar
     

  • 8.

    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. doi.org/10.1093/nar/gky962.

    CAS 
    Article 

    Google Scholar
     

  • 9.

    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. doi.org/10.1093/nar/gkx1132.

  • 10.

    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. doi.org/10.1093/bioinformatics/btr260.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 11.

    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. doi.org/10.1016/j.cels.2015.12.004.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 12.

    Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):1002375. doi.org/10.1371/journal.pcbi.1002375.

    CAS 
    Article 

    Google Scholar
     

  • 13.

    Xie C, Jauhari S, Mora A. Popularity and performance of bioinformatics software: the case of gene set analysis. BMC Bioinform. 2021;22(1):191. doi.org/10.1186/s12859-021-04124-5.

    Article 

    Google Scholar
     

  • 14.

    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. doi.org/10.1073/pnas.0506580102.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 15.

    Nguyen T, Mitrea C, Draghici S. Network-based approaches for pathway level analysis. Curr Protoc Bioinform. 2018;61(1):8–25182524. doi.org/10.1002/cpbi.42.

    Article 

    Google Scholar
     

  • 16.

    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. doi.org/10.1093/bib/bbz158.

    Article 
    PubMed Central 

    Google Scholar
     

  • 17.

    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. doi.org/10.2147/AABC.S63534.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 18.

    Supek F, Škunca N, Visualizing GO annotations. In: The gene ontology handbook, vol. 1446. Humana Press; 2017. p. 207–20. doi.org/10.1007/978-1-4939-3743-1.

  • 19.

    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. doi.org/10.1093/bioinformatics/bti551.

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • 20.

    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. doi.org/10.1093/bioinformatics/btp101.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 21.

    Mlecnik B, Galon J, Bindea G. Comprehensive functional analysis of large lists of genes and proteins. J Proteomics. 2018;171:2–10. doi.org/10.1016/j.jprot.2017.03.016.

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • 22.

    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. doi.org/10.1186/1471-2105-10-48.

    Article 

    Google Scholar
     

  • 23.

    Supek F, Bošnjak M, Škunca N, Šmuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE. 2011;6(7):21800. doi.org/10.1371/journal.pone.0021800.

    CAS 
    Article 

    Google Scholar
     

  • 24.

    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. doi.org/10.1093/bioinformatics/btv300.

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • 25.

    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. doi.org/10.1093/nar/gkx382.

    CAS 
    Article 

    Google Scholar
     

  • 26.

    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. doi.org/10.1186/s12859-017-1600-5.

    CAS 
    Article 

    Google Scholar
     

  • 27.

    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. doi.org/10.1093/nar/gkz401.

    CAS 
    Article 

    Google Scholar
     

  • 28.

    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. doi.org/10.1186/s12859-019-2671-2.

    Article 

    Google Scholar
     

  • 29.

    Zhu J, Zhao Q, Katsevich E, Sabatti C. Exploratory gene ontology analysis with interactive visualization. Sci Rep. 2019;9(1):1–9. doi.org/10.1038/s41598-019-42178-x.

    CAS 
    Article 

    Google Scholar
     

  • 30.

    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. doi.org/10.1186/s12859-019-2960-9.

    Article 

    Google Scholar
     

  • 31.

    Federico A, Monti S. hypeR: an R package for geneset enrichment workflows. Bioinformatics. 2020;36(4):1307–8. doi.org/10.1093/bioinformatics/btz700.

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • 32.

    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. doi.org/10.1093/bioinformatics/bty798.

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • 33.

    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. doi.org/10.1038/s41467-019-09234-6.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 34.

    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. doi.org/10.3389/fgene.2019.00858.

    CAS 
    Article 

    Google Scholar
     

  • 35.

    Ge SX, Jung D, Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics. 2020;36(8):2628–9. doi.org/10.1093/bioinformatics/btz931.

    CAS 
    Article 

    Google Scholar
     

  • 36.

    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. doi.org/10.1186/s13040-019-0204-1.

    CAS 
    Article 

    Google Scholar
     

  • 37.

    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. doi.org/10.1093/nar/gky1131.

    CAS 
    Article 

    Google Scholar
     

  • 38.

    Tokar T, Pastrello C, Jurisica I. GSOAP: a tool for visualisation of gene set over-representation analysis. Bioinformatics. 2020. doi.org/10.1093/bioinformatics/btaa001.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 39.

    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. doi.org/10.1186/s12859-020-3447-4.

    Article 

    Google Scholar
     

  • 40.

    Kim J, Yoon S, Nam D. netGO: R-Shiny package for network-integrated pathway enrichment analysis. Bioinformatics. 2020. doi.org/10.1093/bioinformatics/btaa077.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 41.

    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. doi.org/10.1007/978-1-0716-1307-8.

    Chapter 

    Google Scholar
     

  • 42.

    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. doi.org/10.1093/nargab/lqz019.

    CAS 
    Article 

    Google Scholar
     

  • 43.

    Sandve GK, Nekrutenko A, Taylor J, Hovig E. Ten simple rules for reproducible computational research. PLoS Comput Biol. 2013;9(10):1003285. doi.org/10.1371/journal.pcbi.1003285.

    Article 

    Google Scholar
     

  • 44.

    Marini F, Binder H. Development of applications for interactive and reproducible research: a case study. Genom Computl Biol. 2016;3(1):39. doi.org/10.18547/gcb.2017.vol3.iss1.e39.

    Article 

    Google Scholar
     

  • 45.

    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. doi.org/10.1093/gigascience/giaa056.

    Article 

    Google Scholar
     

  • 46.

    Knuth DE. Literate programming. Comput J. 1984;27(2):97–111. doi.org/10.1093/comjnl/27.2.97.

    Article 

    Google Scholar
     

  • 47.

    Marini F, Binder H. pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components. BMC Bioinform. 2019;20(1):331. doi.org/10.1186/s12859-019-2879-1.

    Article 

    Google Scholar
     

  • 48.

    Marini F, Linke J, Binder H. ideal: an R/Bioconductor package for interactive differential expression analysis. BMC Bioinform. 2020;21(1):565. doi.org/10.1186/s12859-020-03819-5.

    Article 

    Google Scholar
     

  • 49.

    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. doi.org/10.1093/bib/bbv036.

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • 50.

    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. doi.org/10.1038/nmeth.3252.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 51.

    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. doi.org/10.1101/590562.

    Article 

    Google Scholar
     

  • 52.

    Chang W, Cheng J, Allaire J, Xie Y, McPherson J. Shiny: web application framework for R. (2020). R package version 1.4.0.2. CRAN.R-project.org/package=shiny.

  • 53.

    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. doi.org/10.1038/s41588-018-0046-7.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 54.

    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. doi.org/10.1016/j.celrep.2019.12.097.

    CAS 
    Article 
    PubMed 

    Google Scholar
     

  • 55.

    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. doi.org/10.1186/s13059-014-0550-8.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 56.

    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. doi.org/10.1093/nar/gkz966.

    CAS 
    Article 

    Google Scholar
     

  • 57.

    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. doi.org/10.1093/nar/gky955.

    CAS 
    Article 

    Google Scholar
     

  • 58.

    Granjon D. bs4Dash: a ‘Bootstrap 4’ Version of ‘shinydashboard’. 2019. rinterface.github.io/bs4Dash/index.html, github.com/RinteRface/bs4Dash.

  • 59.

    Chang W, Borges Ribeiro B. Shinydashboard: create dashboards with ‘Shiny’. (2018). R package version 0.7.1. CRAN.R-project.org/package=shinydashboard.

  • 60.

    Ganz C. rintrojs: a wrapper for the intro. js library. J Open Source Softw. 2016;1(6):2016. doi.org/10.21105/joss.00063.

    Article 

    Google Scholar
     

  • 61.

    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. doi.org/10.1093/bioinformatics/btl140.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 62.

    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. doi.org/10.1089/omi.2011.0118.

    CAS 
    Article 

    Google Scholar
     

  • 63.

    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. doi.org/10.1038/nprot.2008.211.

    CAS 
    Article 

    Google Scholar
     

  • 64.

    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. doi.org/10.1093/nar/gkw377.

  • 65.

    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. doi.org/10.1038/s41596-018-0103-9.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 66.

    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. doi.org/10.1093/nar/gkz369.

    CAS 
    Article 

    Google Scholar
     

  • 67.

    Korotkevich G, Sukhov V, Budin N, Shpak B, Artyomov MN, Sergushichev A. Fast gene set enrichment analysis. bioRxiv. 2021. doi.org/10.1101/060012.

    Article 

    Google Scholar
     

  • 68.

    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. doi.org/10.1093/nar/gkw1071.

  • 69.

    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. doi.org/10.1002/cpbi.5.

    Article 

    Google Scholar
     

  • 70.

    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. doi.org/10.1038/s41588-018-0154-4.

    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 71.

    Xie Y. Dynamic Documents with R and Knitr, p. 188. Chapman & Hall/CRC; 2013. doi.org/10.18637/jss.v056.b02. arXiv:arXiv:1501.0228. www.crcpress.com/product/isbn/9781482203530.

  • 72.

    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. doi.org/10.1371/journal.pcbi.1007007.

    CAS 
    Article 

    Google Scholar
     

  • 73.

    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. doi.org/10.5334/jors.ay.

    Article 

    Google Scholar
     

  • 74.

    Rue-Albrecht K, Marini F, Soneson C, Lun ATL. iSEE: interactive summarized experiment explorer. F1000Research. 2018;7:741. doi.org/10.12688/f1000research.14966.1.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 75.

    Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C, Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017. doi.org/10.1038/nmeth.4197. arXiv:1505.02710.

  • 76.

    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.

  • 77.

    Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform. 2013;14:12. doi.org/10.1186/1471-2105-14-7.

    Article 

    Google Scholar
     

  • 78.

    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. doi.org/10.1371/journal.pone.0013984.

    CAS 
    Article 

    Google Scholar
     

  • 79.

    Pomaznoy M, Ha B, Peters B. GOnet: a tool for interactive gene ontology analysis. BMC Bioinform. 2018;19(1):1–8. doi.org/10.1186/s12859-018-2533-3.

    CAS 
    Article 

    Google Scholar
     

  • 80.

    Almende BV, Thieurmel B, Robert T. visNetwork: network visualization using ‘vis.js’ library. (2019). R package version 2.0.9. CRAN.R-project.org/package=visNetwork.

  • 81.

    Domagalski R, Neal ZP, Sagan B. Backbone: an R package for extracting the backbone of bipartite projections. PLoS ONE. 2021;16(1):0244363. doi.org/10.1371/journal.pone.0244363.

    CAS 
    Article 

    Google Scholar
     

  • 82.

    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. doi.org/10.1186/s12859-016-0884-1.

  • 83.

    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. doi.org/10.1093/bioinformatics/btw623.

    CAS 
    Article 

    Google Scholar
     

  • 84.

    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. doi.org/10.1186/s12864-019-5738-6.

    Article 

    Google Scholar
     

  • Read more here: Source link