Show simple item record Teng, Mingxiang Love, Michael I. Davis, Carrie A. Djebali, Sarah Dobin, Alexander Graveley, Brenton R. Li, Sheng Mason, Christopher E. Olson, Sara Pervouchine, Dmitri D. Sloan, Cricket A. Wei, Xintao Zhan, Lijun Irizarry, Rafael A. 2017-01-18T08:39:19Z 2017-01-18T08:39:19Z 2016
dc.identifier.citation Teng M, Love MI, Davis CA, Djebali S, Dobin A, Graveley BR et al. A benchmark for RNA-seq quantification pipelines. Genome Biology. 2016;17:74. DOI: 10.1186/s13059-016-0940-1
dc.identifier.issn 1474-760X
dc.description.abstract Obtaining RNA-seq measurements involves a complex data analytical process with a large number of competing algorithms as options. There is much debate about which of these methods provides the best approach. Unfortunately, it is currently difficult to evaluate their performance due in part to a lack of sensitive assessment metrics. We present a series of statistical summaries and plots to evaluate the performance in terms of specificity and sensitivity, available as a R/Bioconductor package ( Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher BioMed Central
dc.relation.ispartof Genome Biology. 2016;17:74
dc.rights © 2016 Teng et al.Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, 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.
dc.subject.other Expressió gènica -- Mètodes
dc.title A benchmark for RNA-seq quantification pipelines
dc.type info:eu-repo/semantics/article
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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