Modeling non-uniformity in short-read rates in RNA-Seq data.
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.
Li
Jun
J
Department of Statistics, Stanford University, Sequoia Hall, 390 Serra Mall, Stanford, CA 94305, USA. junli07@stanford.edu
Jiang
Hui
H
Wong
Wing Hung
WH
D000818:Animals; D001057:Apolipoproteins E / Q000235:genetics; D001483:Base Sequence; D030561:Databases, Nucleic Acid; D004622:Embryo, Mammalian / Q000378:metabolism; D005091:Exons / Q000235:genetics; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D016014:Linear Models; D051379:Mice; D008957:Models, Genetic; D016012:Poisson Distribution; D020033:Protein Isoforms / Q000235:genetics / Q000378:metabolism; D012313:RNA / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000379:methods*; D018709:Statistics, Nonparametric
D001057:Apolipoproteins E; D020033:Protein Isoforms; D012313:RNA
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