title: IsoSCM: improved and alternative 3' UTR annotation using multiple change-point inference.
abstract: Major applications of RNA-seq data include studies of how the transcriptome is modulated at the levels of gene expression and RNA processing, and how these events are related to cellular identity, environmental condition, and/or disease status. While many excellent tools have been developed to analyze RNA-seq data, these generally have limited efficacy for annotating 3' UTRs. Existing assembly strategies often fragment long 3' UTRs, and importantly, none of the algorithms in popular use can apportion data into tandem 3' UTR isoforms, which are frequently generated by alternative cleavage and polyadenylation (APA). Consequently, it is often not possible to identify patterns of differential APA using existing assembly tools. To address these limitations, we present a new method for transcript assembly, Isoform Structural Change Model (IsoSCM) that incorporates change-point analysis to improve the 3' UTR annotation process. Through evaluation on simulated and genuine data sets, we demonstrate that IsoSCM annotates 3' termini with higher sensitivity and specificity than can be achieved with existing methods. We highlight the utility of IsoSCM by demonstrating its ability to recover known patterns of tissue-regulated APA. IsoSCM will facilitate future efforts for 3' UTR annotation and genome-wide studies of the breadth, regulation, and roles of APA leveraging RNA-seq data. The IsoSCM software and source code are available from our website https://github.com/shenkers/isoscm.
authors.affiliation: Department of Developmental Biology, Sloan-Kettering Institute, New York, New York 10065, USA Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, New York 10065, USA.
authors.forename: Sol
authors.initials: S
authors.lastname: Shenker
authors.affiliation: Department of Developmental Biology, Sloan-Kettering Institute, New York, New York 10065, USA.
authors.forename: Pedro
authors.initials: P
authors.lastname: Miura
authors.affiliation: Department of Developmental Biology, Sloan-Kettering Institute, New York, New York 10065, USA Tri-Institutional Program in Computational Biology and Medicine, Weill Cornell Medical College, New York, New York 10065, USA.
authors.forename: Piero
authors.initials: P
authors.lastname: Sanfilippo
authors.affiliation: Department of Developmental Biology, Sloan-Kettering Institute, New York, New York 10065, USA laie@mskcc.org.
authors.forename: Eric C
authors.initials: EC
authors.lastname: Lai
mesh_terms: D020413:3' Untranslated Regions; D000818:Animals; D019295:Computational Biology; D003198:Computer Simulation; D020869:Gene Expression Profiling; D006801:Humans; D058977:Molecular Sequence Annotation; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA; D012984:Software
chemical_list: D020413:3' Untranslated Regions
keywords: 3′ UTR; APA; IsoSCM; RNA-seq