RMATS: Robust and Flexible Detection of Differential Alternative Splicing from Replicate RNA-Seq Data

• Activity Number: 499
• Type: Contributed
• Date/Time: Wednesday, August 6, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Shihao Shen*+ and Juw Won Park and Zhi-xiang Lu and Lin Lan and Michael Henry and Ying Nian Wu and Qing Zhou and Yi Xing
• Companies: and University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles and University of Iowa and University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles
• Keywords: RNA sequencing ; alternative splicing ; exon ; replicate

Abstract

Ultra-deep RNA sequencing has become a powerful approach for genome-wide analysis of pre-mRNA alternative splicing. We previously developed MATS, a statistical method for detecting differential alternative splicing between two RNA-Seq samples. Here we describe a new statistical model, rMATS (replicate MATS), designed for detection of differential alternative splicing from replicate RNA-Seq data. rMATS uses a hierarchical model to simultaneously account for sampling uncertainty in individual replicates and variability among replicates. In addition to unpaired replicates, rMATS also includes a model specifically designed for paired replicates between sample groups. The performance of rMATS is evaluated by simulated and real RNA-Seq data, yielding a high validation rate (94%) in an RNA-Seq dataset of prostate cancer cell lines. Our data also provide guiding principles for designing RNA-Seq studies of alternative splicing. Importantly, we demonstrate that it is essential to incorporate biological replicates in the study design, especially with a high degree of sample variability or presence of outliers. The rMATS software is freely available at http://rnaseq-mats.sourceforge.net/.