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PMID: 20459815 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Modeling non-uniformity in short-read rates in RNA-Seq data.

Genome biology ·Vol. 11 ·No. 5 ·2010-00-00 ·Pages R50

Li J, Jiang H, Wong WH

Abstract

After mapping, RNA-Seq data can be summarized by a sequence of read counts commonly modeled as Poisson variables with constant rates along each transcript, which actually fit data poorly. We suggest using variable rates for different positions, and propose two models to predict these rates based on local sequences. These models explain more than 50% of the variations and can lead to improved estimates of gene and isoform expressions for both Illumina and Applied Biosystems data.

MeSH Terms

Animals Apolipoproteins E/genetics Base Sequence Databases, Nucleic Acid Embryo, Mammalian/metabolism Exons/genetics Gene Expression Profiling Gene Expression Regulation Humans Linear Models Mice Models, Genetic Poisson Distribution Protein Isoforms/genetics,metabolism RNA/genetics Sequence Analysis, RNA/methods Statistics, Nonparametric

Chemicals

Apolipoproteins E Protein Isoforms RNA

Authors & Affiliations

3 authors, click to expand affiliations / ORCID

Li Jun

Department of Statistics, Stanford University, Sequoia Hall, 390 Serra Mall, Stanford, CA 94305, USA. junli07@stanford.edu

Jiang Hui

Wong Wing Hung

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Article Info

Journal

Genome biology

Abbr.

Genome Biol

ISSN

1474-760X

DOI

10.1186/gb-2010-11-5-r50

junli07@stanford.edu

Published

2010-00-00

Epub

2010-00-11

Pages

R50

Language

English

Region

England

NLM ID

100960660

PMCID

PMC2898062

Subset

Grants

NHGRI NIH HHS · R01 HG003903 · United States

NHGRI NIH HHS · R01 HG004634 · United States

External Links

PubMed source DOI: 10.1186/gb-2010-11-5-r50 PMC Full Text View journal details

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Modeling non-uniformity in short-read rates in RNA-Seq data.

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