Accurate, fast, and model-aware transcript expression quantification with Salmon

Abstract

Existing methods for quantifying transcript abundance require a fundamental compromise: either use high quality read alignments and experiment-specific models or sacrifice them for speed. We introduce Salmon, a quantification method that overcomes this restriction by combining a novel ‘lightweight’ alignment procedure with a streaming parallel inference algorithm and a feature-rich bias model. These innovations yield both exceptional accuracy and order-of-magnitude speed benefits over traditional alignment-based methods.