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 25

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Improving RNA-Seq expression estimates by correcting for fragment bias

Adam Roberts, Cole Trapnell, Julie Donaghey … (2011)

The biochemistry of RNA-Seq library preparation results in cDNA fragments that are not uniformly distributed within the transcripts they represent. This non-uniformity must be accounted for when estimating expression levels, and we show how to perform the needed corrections using a likelihood based approach. We find improvements in expression estimates as measured by correlation with independently performed qRT-PCR and show that correction of bias leads to improved replicability of results across libraries and sequencing technologies.

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Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

Katherine A. Hoadley, Christina Yau, Denise M Wolf … (2014)

Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.

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Streaming fragment assignment for real-time analysis of sequencing experiments

Adam Roberts, Lior Pachter (2012)

We present eXpress, a software package for highly efficient probabilistic assignment of ambiguously mapping sequenced fragments. eXpress uses a streaming algorithm with linear run time and constant memory use. It can determine abundances of sequenced molecules in real time, and can be applied to ChIP-seq, metagenomics and other large-scale sequencing data. We demonstrate its use on RNA-seq data, showing greater efficiency than other quantification methods.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101215604

Journal ID (pubmed-jr-id): 32338

Journal ID (nlm-ta): Nat Methods

Journal ID (iso-abbrev): Nat. Methods

Title: Nature methods

ISSN (Print): 1548-7091

ISSN (Electronic): 1548-7105

Publication date Nihms-submitted: 18 March 2017

Publication date (Electronic): 06 March 2017

Publication date (Print): April 2017

Publication date PMC-release: 19 September 2017

Volume: 14

Issue: 4

Pages: 417-419

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

[*]

rob.patro@ 123456cs.stonybrook.edu

[†]

gduggal@ 123456dnanexus.com , work done while GD was at CMU.

[‡]

mlove@ 123456jimmy.harvard.edu

[§]

rafa@ 123456jimmy.harvard.edu

[¶]

carlk@ 123456cs.cmu.edu

Article

Manuscript ID: NIHMS849351

DOI: 10.1038/nmeth.4197

PMC ID: 5600148

PubMed ID: 28263959

SO-VID: c61f1ce1-5bc4-489f-b582-0bdf9536facb

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

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Abstract

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Most cited references 25

Improving RNA-Seq expression estimates by correcting for fragment bias

Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

Streaming fragment assignment for real-time analysis of sequencing experiments

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