A benchmark for RNA-seq quantification pipelines

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A benchmark for RNA-seq quantification pipelines

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Title: A benchmark for RNA-seq quantification pipelines
Author: Teng, Mingxiang; Love, Michael I.; Davis, Carrie A.; Djebali, Sarah; Dobin, Alexander; Graveley, Brenton R.; Li, Sheng; Mason, Christopher E.; Olson, Sara; Pervouchine, Dmitri; Sloan, Cricket A.; Wei, Xintao; Zhan, Lijun; Irizarry, Rafael A.

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Citation: Teng, M., M. I. Love, C. A. Davis, S. Djebali, A. Dobin, B. R. Graveley, S. Li, et al. 2016. “A benchmark for RNA-seq quantification pipelines.” Genome Biology 17 (1): 74. doi:10.1186/s13059-016-0940-1. http://dx.doi.org/10.1186/s13059-016-0940-1.
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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 (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. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-0940-1) contains supplementary material, which is available to authorized users.
Published Version: doi:10.1186/s13059-016-0940-1
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842274/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:26860249
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