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A benchmark for RNA-seq quantification pipelines

delete2016-04-23
delete156
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OA
AI
M
Mingxiang Teng
M
Michael I. Love
C
Carrie Davis
S
Sarah Djebali
A
Alexander Dobin
B
Brenton R. Graveley
李胜 (Sheng Li)
C
Christopher E. Mason
S
Sara Olson
D
Dmitri D. Pervouchine
C
Cricket A. Sloan
X
Xintao Wei
L
Lijun Zhan
R
Rafael A. Irizarry *
DOI:10.1186/s13059-016-0940-1delete
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Abstract

Abstract

En 中文
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.
Keywords:
GENE-EXPRESSION
CELL
TRANSCRIPTOMES
NORMALIZATION
ABUNDANCE
ALIGNMENT
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G
Genome Biology
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B
barcelona institute of science & technology
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Harvard University
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Harvard T.H. Chan School of Public Health
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Pompeu Fabra University
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centre de regulacio genomica (crg)
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