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Genome Biol. 2016 Apr 23;17:74. doi: 10.1186/s13059-016-0940-1.

A benchmark for RNA-seq quantification pipelines.

Author information

1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA.
2
Department of Biostatistics, Harvard TH Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.
3
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
4
Functional Genomics Group, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY, 11724, USA.
5
Bioinformatics and Genomics Programme, Centre for Genomic Regulation (CRG) and UPF, Doctor Aiguader, 88, Barcelona, 08003, Spain.
6
Department of Genetics and Genome Sciences, Institute for System Genomics, UConn Health Center, Farmington, CT, 06030, USA.
7
Department of Physiology and Biophysics, Weill Cornell Medical College, New York, New York, USA.
8
Department of Genetics, Stanford University, 300 Pasteur Drive, MC-5477, Stanford, CA, 94305, USA.
9
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA. rafa@jimmy.harvard.edu.
10
Department of Biostatistics, Harvard TH Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA. rafa@jimmy.harvard.edu.

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.

PMID:
27107712
PMCID:
PMC4842274
DOI:
10.1186/s13059-016-0940-1
[Indexed for MEDLINE]
Free PMC Article

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