title: 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 article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped 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 superior 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 data sets, 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.
authors.affiliation: Department of Computer Science, University of Toronto, Ontario M5S 2E4, Canada.
authors.forename: Aziz M
authors.initials: AM
authors.lastname: Mezlini
authors.forename: Eric J M
authors.initials: EJ
authors.lastname: Smith
authors.forename: Marc
authors.initials: M
authors.lastname: Fiume
authors.forename: Orion
authors.initials: O
authors.lastname: Buske
authors.forename: Gleb L
authors.initials: GL
authors.lastname: Savich
authors.forename: Sohrab
authors.initials: S
authors.lastname: Shah
authors.forename: Sam
authors.initials: S
authors.lastname: Aparicio
authors.forename: Derek Y
authors.initials: DY
authors.lastname: Chiang
authors.forename: Anna
authors.initials: A
authors.lastname: Goldenberg
authors.forename: Michael
authors.initials: M
authors.lastname: Brudno
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005260:Female; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D061986:MCF-7 Cells; D012322:RNA Precursors / Q000235:genetics / Q000378:metabolism; D012326:RNA Splicing; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D017423:Sequence Analysis, RNA / Q000379:methods*; D059467:Transcriptome; D016159:Tumor Suppressor Protein p53 / Q000235:genetics / Q000378:metabolism
chemical_list: D012322:RNA Precursors; D012333:RNA, Messenger; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53