Abstract
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.
Keywords
Poisson distributionRNA-SeqCount dataComputational biologySequence (biology)Variable (mathematics)Constant (computer programming)Computer scienceStatisticsGeneBiologyMathematicsGeneticsGene expressionTranscriptome
Affiliated Institutions
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Publication Info
- Year
- 2010
- Type
- article
- Volume
- 11
- Issue
- 5
- Pages
- R50-R50
- Citations
- 202
- Access
- Closed
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Cite This
Jun Li,
Hui Jiang,
Wing Hung Wong
(2010).
Modeling non-uniformity in short-read rates in RNA-Seq data.
Genome biology
, 11
(5)
, R50-R50.
https://doi.org/10.1186/gb-2010-11-5-r50
Identifiers
- DOI
- 10.1186/gb-2010-11-5-r50