Salmon provides fast and bias-aware quantification of transcript expression

Abstract

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.