Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inference
نویسندگان
چکیده
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 ∗[email protected] †[email protected], work done while GD was at CMU. ‡[email protected] §[email protected] ¶[email protected]
منابع مشابه
Accurate, fast, and model-aware transcript expression quantification with Salmon
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. ...
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Transcript quantification is a central task in the analysis of RNA-seq data. Accurate computational methods for the quantification of transcript abundances are essential for downstream analysis. However, most existing approaches are much slower than is necessary for their degree of accuracy. We introduce Salmon, a novel method and software tool for transcript quantification that exhibits state-...
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تاریخ انتشار 2016