Genomic Sequence-Independent Prediction of RNA Editing Sites Using Single RNA-Seq Data

• Activity Number: 17
• Type: Topic Contributed
• Date/Time: Sunday, August 3, 2014 : 2:00 PM to 3:50 PM
• Sponsor: International Chinese Statistical Association
• Author(s): Xinshu Xiao*+ and Qing Zhang
• Companies: University of California, Los Angeles and University of California, Los Angeles
• Keywords: RNA editing ; RNA-Seq ; SNPs

Abstract

High-throughput RNA sequencing (RNA-Seq) provides single-nucleotide information that makes it a powerful tool for prediction of RNA editome. However, most methods to this end require genome sequence data or comparative analysis across multiple RNA-Seq data sets. Here we introduce a new method that predicts human RNA editome using a single RNA-Seq data set and public databases of single nucleotide polymorphisms (SNPs). This method integrates features of RNA editing sites and genomic SNPs to drive statistical inference of RNA editing. It does not require genome sequence data of the particular sample under study. We show that this method has a low false discovery rate of < 5%, even in simulations where the majority of sample-specific genomic SNPs being unknown to the public databases. The sensitivity of the method is also higher than previous methods. Applied to a large number of RNA-Seq data sets, this approach revealed important insights in understanding the contribution of RNA editing to gene expression diversity.