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Genome Biol. 2010;11(5):R50. doi: 10.1186/gb-2010-11-5-r50. Epub 2010 May 11.

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

Author information

1
Department of Statistics, Stanford University, Sequoia Hall, 390 Serra Mall, Stanford, CA 94305, USA. junli07@stanford.edu

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.

PMID:
20459815
PMCID:
PMC2898062
DOI:
10.1186/gb-2010-11-5-r50
[Indexed for MEDLINE]
Free PMC Article

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