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

Rob Patro, Geet Duggal, Carl Kingsford
doi: https://doi.org/10.1101/021592
Rob Patro
1Department of Computer Science, Stony Brook University
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Geet Duggal
2Department of Computational Biology, Carnegie Mellon University
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Carl Kingsford
2Department of Computational Biology, Carnegie Mellon University
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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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 03, 2015.
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Accurate, fast, and model-aware transcript expression quantification with Salmon
Rob Patro, Geet Duggal, Carl Kingsford
bioRxiv 021592; doi: https://doi.org/10.1101/021592
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Accurate, fast, and model-aware transcript expression quantification with Salmon
Rob Patro, Geet Duggal, Carl Kingsford
bioRxiv 021592; doi: https://doi.org/10.1101/021592

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