Salmon provides fast and bias-aware quantification of transcript expression

Salmon is a computational tool that uses sample-specific models and a dual-phase inference procedure to correct biases in RNA-seq data and rapidly quantify transcript abundances. We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA–seq reads. Salmon combines a new...

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Veröffentlicht in:Nature methods Jg. 14; H. 4; S. 417 - 419
Hauptverfasser: Patro, Rob, Duggal, Geet, Love, Michael I, Irizarry, Rafael A, Kingsford, Carl
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Nature Publishing Group US 01.04.2017
Nature Publishing Group
Schlagworte:
631/114/2415
631/114/794
631/208/212/2019
Abundance
Algorithms
Base Composition
Bayes Theorem
Bias
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical Engineering/Biotechnology
brief-communication
Gene expression
Gene Expression Profiling - methods
Gene Expression Profiling - statistics & numerical data
Life Sciences
Proteomics
Ribonucleic acid
RNA
Sensitivity analysis
Sequence Analysis, RNA - methods
Sequence Analysis, RNA - statistics & numerical data
Transcription
ISSN:1548-7091, 1548-7105, 1548-7105
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Salmon is a computational tool that uses sample-specific models and a dual-phase inference procedure to correct biases in RNA-seq data and rapidly quantify transcript abundances. 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.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/nmeth.4197