title: Salmon provides fast and bias-aware quantification of transcript expression.
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.
authors.affiliation: Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.
authors.forename: Rob
authors.initials: R
authors.lastname: Patro
authors.affiliation: DNAnexus, Mountain View, California, USA.
authors.forename: Geet
authors.initials: G
authors.lastname: Duggal
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Cambridge, Massachusetts, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts, USA.
authors.forename: Michael I
authors.initials: MI
authors.lastname: Love
authors.affiliation: Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Cambridge, Massachusetts, USA.|Department of Biostatistics, Harvard T.H. Chan School of Public Health, Cambridge, Massachusetts, USA.
authors.forename: Rafael A
authors.initials: RA
authors.lastname: Irizarry
authors.affiliation: Computational Biology Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
authors.forename: Carl
authors.initials: C
authors.lastname: Kingsford
mesh_terms: D000465:Algorithms; D001482:Base Composition; D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data; D017423:Sequence Analysis, RNA / Q000379:methods* / Q000706:statistics & numerical data