RNA-Seq gene expression estimation with read mapping uncertainty

Motivation: RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which the...

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Bibliographic Details
Published in:Bioinformatics Vol. 26; no. 4; pp. 493 - 500
Main Authors: Li, Bo, Ruotti, Victor, Stewart, Ron M., Thomson, James A., Dewey, Colin N.
Format: Journal Article
Language:English
Published: Oxford Oxford University Press 15.02.2010
Subjects:
Algorithms
Animals
Base Sequence
Biological and medical sciences
Computational Biology - methods
Databases, Genetic
Fundamental and applied biological sciences. Psychology
Gene Expression
Gene Expression Profiling
General aspects
Genome
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Mice
Original Papers
Sequence Analysis, RNA - methods
Software
Zea mays
Zea mays - genetics
Genetic mapping
Gene
RNA
High throughput sequencing
Estimation
Mapping
Gene expression
ISSN:1367-4803, 1367-4811, 1460-2059, 1367-4811
Online Access:Get full text
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Summary:Motivation: RNA-Seq is a promising new technology for accurately measuring gene expression levels. Expression estimation with RNA-Seq requires the mapping of relatively short sequencing reads to a reference genome or transcript set. Because reads are generally shorter than transcripts from which they are derived, a single read may map to multiple genes and isoforms, complicating expression analyses. Previous computational methods either discard reads that map to multiple locations or allocate them to genes heuristically. Results: We present a generative statistical model and associated inference methods that handle read mapping uncertainty in a principled manner. Through simulations parameterized by real RNA-Seq data, we show that our method is more accurate than previous methods. Our improved accuracy is the result of handling read mapping uncertainty with a statistical model and the estimation of gene expression levels as the sum of isoform expression levels. Unlike previous methods, our method is capable of modeling non-uniform read distributions. Simulations with our method indicate that a read length of 20–25 bases is optimal for gene-level expression estimation from mouse and maize RNA-Seq data when sequencing throughput is fixed. Availability: An initial C++ implementation of our method that was used for the results presented in this article is available at http://deweylab.biostat.wisc.edu/rsem. Contact: cdewey@biostat.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics on
Bibliography:To whom correspondence should be addressed.
istex:33C262CA405AB4AD6C01E52F21BBA3B75FCC38A9
ArticleID:btp692
ark:/67375/HXZ-5PZQD461-J
Associate Editor: Joaquin Dopazo
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1367-4803
1367-4811
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btp692