Wei Li1, Tao Jiang. 1. Department of Computer Science and Engineering, University of California, Riverside, Riverside CA 92507, USA. liw@cs.ucr.edu
Abstract
MOTIVATION: RNA-Seq uses the high-throughput sequencing technology to identify and quantify transcriptome at an unprecedented high resolution and low cost. However, RNA-Seq reads are usually not uniformly distributed and biases in RNA-Seq data post great challenges in many applications including transcriptome assembly and the expression level estimation of genes or isoforms. Much effort has been made in the literature to calibrate the expression level estimation from biased RNA-Seq data, but the effect of biases on transcriptome assembly remains largely unexplored. RESULTS: Here, we propose a statistical framework for both transcriptome assembly and isoform expression level estimation from biased RNA-Seq data. Using a quasi-multinomial distribution model, our method is able to capture various types of RNA-Seq biases, including positional, sequencing and mappability biases. Our experimental results on simulated and real RNA-Seq datasets exhibit interesting effects of RNA-Seq biases on both transcriptome assembly and isoform expression level estimation. The advantage of our method is clearly shown in the experimental analysis by its high sensitivity and precision in transcriptome assembly and the high concordance of its estimated expression levels with quantitative reverse transcription-polymerase chain reaction data. AVAILABILITY: CEM is freely available at http://www.cs.ucr.edu/~liw/cem.html. CONTACT: liw@cs.ucr.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: RNA-Seq uses the high-throughput sequencing technology to identify and quantify transcriptome at an unprecedented high resolution and low cost. However, RNA-Seq reads are usually not uniformly distributed and biases in RNA-Seq data post great challenges in many applications including transcriptome assembly and the expression level estimation of genes or isoforms. Much effort has been made in the literature to calibrate the expression level estimation from biased RNA-Seq data, but the effect of biases on transcriptome assembly remains largely unexplored. RESULTS: Here, we propose a statistical framework for both transcriptome assembly and isoform expression level estimation from biased RNA-Seq data. Using a quasi-multinomial distribution model, our method is able to capture various types of RNA-Seq biases, including positional, sequencing and mappability biases. Our experimental results on simulated and real RNA-Seq datasets exhibit interesting effects of RNA-Seq biases on both transcriptome assembly and isoform expression level estimation. The advantage of our method is clearly shown in the experimental analysis by its high sensitivity and precision in transcriptome assembly and the high concordance of its estimated expression levels with quantitative reverse transcription-polymerase chain reaction data. AVAILABILITY: CEM is freely available at http://www.cs.ucr.edu/~liw/cem.html. CONTACT: liw@cs.ucr.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Pauline A Fujita; Brooke Rhead; Ann S Zweig; Angie S Hinrichs; Donna Karolchik; Melissa S Cline; Mary Goldman; Galt P Barber; Hiram Clawson; Antonio Coelho; Mark Diekhans; Timothy R Dreszer; Belinda M Giardine; Rachel A Harte; Jennifer Hillman-Jackson; Fan Hsu; Vanessa Kirkup; Robert M Kuhn; Katrina Learned; Chin H Li; Laurence R Meyer; Andy Pohl; Brian J Raney; Kate R Rosenbloom; Kayla E Smith; David Haussler; W James Kent Journal: Nucleic Acids Res Date: 2010-10-18 Impact factor: 16.971
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