iReckon: Simultaneous isoform discovery and abundance estimation from RNA-seq data

Abstract

High throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this paper, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenom- ena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multi-mapped reads. iReckon utilizes regularized Expectation-Maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a supe- rior ability to discover novel isoforms with a significantly reduced number of false positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore we have applied iReckon to two cancer transcriptome datasets, a triple negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r^2 = 0.94) with the predicted abundances.