A benchmark for RNA-seq quantification pipelines. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Teng, Mingxiang; Love, Michael; Davis, Carrie; Djebali, Sarah; Dobin, Alexander; Graveley, Brenton; Li, Sheng; Mason, Christopher; Olson, Sara; Pervouchine, Dmitri; Sloan, Cricket; Wei, Xintao; Zhan, Lijun; Irizarry, Rafael
• Is Part Of: Genome biology. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-12
• Subjects: Genomes -- Periodicals; Biology -- Periodicals; Molecular biology -- Periodicals; 572.8633
• DOI: 10.1186/s13059-016-0940-1
• Languages: English
• ISSNs: 1474-760X

Abstract

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.