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Nat Methods. 2017 Apr;14(4):417-419. doi: 10.1038/nmeth.4197. Epub 2017 Mar 6.

Salmon provides fast and bias-aware quantification of transcript expression.

Author information

1
Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.
2
DNAnexus, Mountain View, California, USA.
3
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Cambridge, Massachusetts, USA.
4
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts, USA.
5
Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.

Abstract

We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA-seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. It is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which, as we demonstrate here, substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.

PMID:
28263959
PMCID:
PMC5600148
DOI:
10.1038/nmeth.4197
[Indexed for MEDLINE]
Free PMC Article

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