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A survey of best practices for RNA-seq data analysis

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dc.contributor.author Conesa, Ana es_ES
dc.contributor.author Madrigal, Pedro es_ES
dc.contributor.author Tarazona Campos, Sonia es_ES
dc.contributor.author Gómez Cabrero, David es_ES
dc.contributor.author Cervera, Alejandra es_ES
dc.contributor.author McPherson, Andrew es_ES
dc.contributor.author Wojciech Szczesniak, Michal es_ES
dc.contributor.author Gaffney, Daniel J. es_ES
dc.contributor.author Elo, Laura L. es_ES
dc.contributor.author Zhang, Xuegong es_ES
dc.contributor.author Mortazavi, Ali es_ES
dc.date.accessioned 2017-04-27T14:21:50Z
dc.date.available 2017-04-27T14:21:50Z
dc.date.issued 2016-01
dc.identifier.issn 1474-760X
dc.identifier.uri http://hdl.handle.net/10251/80137
dc.description This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. es_ES
dc.description The Erratum to this article has been published in Genome Biology 2016 17:181 es_ES
dc.description.abstract [EN] RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics. es_ES
dc.description.sponsorship The authors would like to thank Michael Love and Harold Pimentel for helpful suggestions on the initial draft of the manuscript. AC, ST, AM, DGC were supported by the FP7 STATegra project (grant 36000). Research in AC’s laboratory was supported by MINECO grant BIO2012-40244 and co-funded with European Regional Development Funds (ERDF). Research in PM’s laboratory is supported by ERC starting grant Relieve-IMDs and by a core support grant from the Wellcome Trust and MRC to the Wellcome TrustMedical Research Council Cambridge Stem Cell Institute. XZ was supported by the National Basic Research Program of China (2012CB316504). LLE was supported by JDRF (grant number 2-2013-32) and by the Sigrid Juselius Foundation. ACe was supported by the Academy of Finland (Center of Excellence in Cancer Genetics Research).
dc.language Inglés es_ES
dc.publisher BioMed Central es_ES
dc.relation.ispartof Genome Biology es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject RNA-seq analysis es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title A survey of best practices for RNA-seq data analysis es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1186/s13059-016-0881-8
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//BIO2012-40244/ES/DESARROLLO DE RECURSOS COMPUTACIONALES PARA LA CARACTERIZACION Y ANOTACION FUNCIONAL DE ARN NO CODIFICANTE./ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7 STATegra/36000/EU/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NKRDPC//2012CB316504/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/JDRF//2-2013-32/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Facultad de Administración y Dirección de Empresas - Facultat d'Administració i Direcció d'Empreses es_ES
dc.description.bibliographicCitation Conesa, A.; Madrigal, P.; Tarazona Campos, S.; Gómez Cabrero, D.; Cervera, A.; Mcpherson, A.; Wojciech Szczesniak, M.... (2016). A survey of best practices for RNA-seq data analysis. Genome Biology. 17(13). https://doi.org/10.1186/s13059-016-0881-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1186/s13059-016-0881-8 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 17 es_ES
dc.description.issue 13 es_ES
dc.relation.senia 306161 es_ES
dc.identifier.pmid 26813401 en_EN
dc.identifier.pmcid PMC4728800 en_EN
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad
dc.contributor.funder Juvenile Diabetes Research Foundation, EEUU
dc.contributor.funder National Key Research and Development Program of China es_ES
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