Salmon provides fast and bias-aware quantification of transcript expression
Nature methods, 2017•nature.com
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.
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.
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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