RT Journal Article SR Electronic T1 Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference JF bioRxiv FD Cold Spring Harbor Laboratory SP 021592 DO 10.1101/021592 A1 Rob Patro A1 Geet Duggal A1 Michael I Love A1 Rafael A Irizarry A1 Carl Kingsford YR 2016 UL http://biorxiv.org/content/early/2016/08/30/021592.abstract AB We introduce Salmon, a new method for quantifying transcript abundance from RNA-seq reads that is highly-accurate and very fast. Salmon is the first transcriptome-wide quantifier to model and correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis compared to existing methods that do not account for these biases. Salmon achieves its speed and accuracy by combining a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. These innovations yield both exceptional accuracy and order-of-magnitude speed benefits over alignment-based methods.