Transcript quantification is a central task in the analysis of RNA-seq data. Accurate computational methods for the quantification of transcript abundances are essential for downstream analysis. However, most existing approaches are much slower than is necessary for their degree of accuracy.
Researchers from Stony Brook University have developed Salmon, a novel method and software tool for transcript quantification that exhibits state-of-the-art accuracy while being significantly faster than most other tools. Salmon achieves this through the combined application of a two-phase inference procedure, a reduced data representation, and a novel lightweight read alignment algorithm.
Overview of Salmon’s method and components. Salmon excepts either raw or aligned reads as input, performs an online inference when processing fragments or alignments, builds equivalence classes over these fragments and subsequently refines abundance estimates using an offline inference algorithm on a reduced representation of the data.
Availability – Salmon is written in C++11, and is available under the GPL v3 license as open-source software at https://combine-lab.github.io/salmon.