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Salmon: fast and bias-aware quantification of transcript expression using dual-phase inference

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      Abstract

      We introduce Salmon, a method for quantifying transcript abundance from RNA-seq reads that is accurate and fast. Salmon is the first transcriptome-wide quantifier to correct for fragment GC content bias, which we demonstrate substantially improves the accuracy of abundance estimates and the reliability of subsequent differential expression analysis. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure.

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      Most cited references 32

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        Is Open Access

        The Cancer Genome Atlas Pan-Cancer analysis project.

        The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
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          Near-optimal probabilistic RNA-seq quantification.

          We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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            Author and article information

            Affiliations
            [1 ]Department of Computer Science, Stony Brook University
            [2 ]DNAnexus, 1975 W El Camino Real, Suite 101 Mountain View, CA 94040
            [3 ]Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Department of Biostatistics, Harvard TH Chan School of Public Health
            [4 ]Computational Biology Department, Carnegie Mellon University
            Author notes
            Journal
            101215604
            32338
            Nat Methods
            Nat. Methods
            Nature methods
            1548-7091
            1548-7105
            18 March 2017
            06 March 2017
            April 2017
            19 September 2017
            : 14
            : 4
            : 417-419
            28263959 5600148 10.1038/nmeth.4197 NIHMS849351

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