Estimation of alternative splicing isoform frequencies from RNA-Seq data
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Accurate estimation of gene expression levels from DGE sequencing data
ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
Maximum likelihood estimation of incomplete genomic spectrum from HTS data
WABI'11 Proceedings of the 11th international conference on Algorithms in bioinformatics
A robust method for transcript quantification with RNA-seq data
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
An integer programming approach to novel transcript reconstruction from paired-end RNA-Seq reads
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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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