RNA-Seq gene expression estimation with read mapping uncertainty

• Published in: Bioinformatics Vol. 26; no. 4; pp. 493 - 500
• Main Authors: Li, Bo, Ruotti, Victor, Stewart, Ron M., Thomson, James A., Dewey, Colin N.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15.02.2010
• Subjects: Algorithms; Animals; Base Sequence; Biological and medical sciences; Computational Biology - methods; Databases, Genetic; Fundamental and applied biological sciences. Psychology; Gene Expression; Gene Expression Profiling; General aspects; Genome; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Mice; Original Papers; Sequence Analysis, RNA - methods; Software; Zea mays; Zea mays - genetics; Genetic mapping; Gene; RNA; High throughput sequencing; Estimation; Mapping; Gene expression
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btp692

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