Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq
- 1Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706, USA;
- 2Department 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;
- 3Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA;
- 4Department of Computer Sciences, University of Wisconsin, Madison, Wisconsin 53706, USA
- Corresponding authors: colin.dewey{at}wisc.edu, keles{at}stat.wisc.edu
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.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.199174.115.
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Freely available online through the Genome Research Open Access option.
- Received September 4, 2015.
- Accepted June 13, 2016.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
