A benchmark for RNA-seq quantification pipelines
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Author
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
Note: Order does not necessarily reflect citation order of authors.
Published Version
https://doi.org/10.1186/s13059-016-0940-1Metadata
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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.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.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4842274/pdf/Terms of Use
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