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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.

Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq