title: Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq.
abstract: RNA-seq is currently the technology of choice for global measurement of transcript abundances in cells. Despite its successes, isoform-level quantification remains difficult because short RNA-seq reads are often compatible with multiple alternatively spliced isoforms. Existing methods rely heavily on uniquely mapping reads, which are not available for numerous isoforms that lack regions of unique sequence. To improve quantification accuracy in such difficult cases, we developed a novel computational method, prior-enhanced RSEM (pRSEM), which uses a complementary data type in addition to RNA-seq data. We found that ChIP-seq data of RNA polymerase II and histone modifications were particularly informative in this approach. In qRT-PCR validations, pRSEM was shown to be superior than competing methods in estimating relative isoform abundances within or across conditions. Data-driven simulations suggested that pRSEM has a greatly decreased false-positive rate at the expense of a small increase in false-negative rate. In aggregate, our study demonstrates that pRSEM transforms existing capacity to precisely estimate transcript abundances, especially at the isoform level.
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706, USA;
authors.forename: Peng
authors.initials: P
authors.lastname: Liu
authors.affiliation: Department of Cell and Regenerative Biology, UW-Madison Blood Research Program, Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin 53705, USA;
authors.forename: Rajendran
authors.initials: R
authors.lastname: Sanalkumar
authors.affiliation: Department of Cell and Regenerative Biology, UW-Madison Blood Research Program, Carbone Cancer Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin 53705, USA;
authors.forename: Emery H
authors.initials: EH
authors.lastname: Bresnick
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706, USA; Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA;
authors.forename: Sündüz
authors.initials: S
authors.lastname: Keleş
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706, USA; Department of Computer Sciences, University of Wisconsin, Madison, Wisconsin 53706, USA.
authors.forename: Colin N
authors.initials: CN
authors.lastname: Dewey
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D012313:RNA / Q000235:genetics*; D012319:RNA Polymerase II / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software
chemical_list: D012313:RNA; D012319:RNA Polymerase II