EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Lee, Soohyun; Seo, Chae; Alver, Burak; Lee, Sanghyuk; Park, Peter
• Is Part Of: BMC bioinformatics. Volume 16:Issue 1(2015)
• Page Start: 1
• Page End: 16
• Publication Date: 2015-12
• Subjects: Expression quantification -- Isoforms -- Multi-reads -- Optimization -- Suffix array; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0704-z
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background RNA-seq has been widely used for genome-wide expression profiling. RNA-seq data typically consists of tens of millions of short sequenced reads from different transcripts. However, due to sequence similarity among genes and among isoforms, the source of a given read is often ambiguous. Existing approaches for estimating expression levels from RNA-seq reads tend to compromise between accuracy and computational cost. Results We introduce a new approach for quantifying transcript abundance from RNA-seq data. EMSAR (Estimation by Mappability-based Segmentation And Reclustering) groups reads according to the set of transcripts to which they are mapped and finds maximum likelihood estimates using a joint Poisson model for each optimal set of segments of transcripts. The method uses nearly all mapped reads, including those mapped to multiple genes. With an efficient transcriptome indexing based on modified suffix arrays, EMSAR minimizes the use of CPU time and memory while achieving accuracy comparable to the best existing methods. Conclusions EMSAR is a method for quantifying transcripts from RNA-seq data with high accuracy and low computational cost. EMSAR is available athttps://github.com/parklab/emsar