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.
Researchers at University of California, Riverside propose a statistical framework for both transcriptome assembly and isoform expression level estimation from biased RNA-Seq data. Using a quasi-multinomial distribution model, this method is able to capture various types of RNA-Seq biases, including positional, sequencing and mappability biases. Their 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 this 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 qRT-PCR data.
Availability: CEM is freely available at http://www.cs.ucr.edu/~liw/cem.html
- Li W, Jiang T. (2012) Transcriptome Assembly and Isoform Expression Level Estimation from Biased RNA-Seq Reads. Bioinformatics [Epub ahead of print]. [abstract]