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Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference

By Rob Patro, Geet Duggal, Michael I. Love, Rafael A. Irizarry, Carl Kingsford

Posted 27 Jun 2015
bioRxiv DOI: 10.1101/021592 (published DOI: 10.1038/nmeth.4197)

We introduce Salmon, a new method for quantifying transcript abundance from RNA-seq reads that is highly-accurate and very fast. Salmon is the first transcriptome-wide quantifier to model and correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis compared to existing methods that do not account for these biases. Salmon achieves its speed and accuracy by combining a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. These innovations yield both exceptional accuracy and order-of-magnitude speed benefits over alignment-based methods.

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