Teng, MingxiangLove, Michael I.Davis, Carrie A.Djebali, SarahDobin, AlexanderGraveley, Brenton R.Li, ShengMason, Christopher E.Olson, SaraPervouchine, Dmitri D.Sloan, Cricket A.Wei, XintaoZhan, LijunIrizarry, Rafael A.2017-01-182017-01-182016Teng 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/s13059-016-0940-11474-760Xhttp://hdl.handle.net/10230/27924Obtaining 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 (http://bioconductor.org/packages/rnaseqcomp). Using two independent datasets, we assessed seven competing pipelines. Performance was generally poor, with two methods clearly underperforming and RSEM slightly outperforming the rest.application/pdfeng© 2016 Teng et al.Open Access. 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.Expressió gènica -- Mètodesinfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s13059-016-0940-1info:eu-repo/semantics/openAccess