Salmon provides fast and bias-aware quantification of transcript expression
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Published on: February 23, 2024
Rob Patro1, Geet Duggal2, Michael I Love3,4
1Department of Computer Science, Stony Brook University, Stony Brook, New York, USA.
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.
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