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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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

Peng Liu¹, Rajendran Sanalkumar², Emery H Bresnick²

¹Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706, USA;

Genome Research

|July 14, 2016

Summary

Quantifying RNA at the isoform level is challenging. A new method, prior-enhanced RSEM (pRSEM), improves accuracy by integrating RNA-seq with complementary data like ChIP-seq, enhancing transcript abundance estimation.

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Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

RNA sequencing (RNA-seq) is standard for measuring transcript levels.
Isoform quantification is difficult due to short reads mapping to multiple splice variants.
Current methods struggle with isoforms lacking unique sequence regions.

Purpose of the Study:

To develop a computational method for improved isoform-level transcript quantification.
To leverage complementary data types to enhance RNA-seq analysis.
To address limitations of existing methods in quantifying alternatively spliced isoforms.

Main Methods:

Developed prior-enhanced RSEM (pRSEM), a novel computational approach.
Integrated RNA-seq data with complementary data, specifically ChIP-seq for RNA polymerase II and histone modifications.
Validated pRSEM using quantitative reverse transcription PCR (qRT-PCR) and data-driven simulations.

Main Results:

pRSEM demonstrated superior performance compared to existing methods in estimating relative isoform abundances.
Simulations indicated a significantly reduced false-positive rate for pRSEM.
ChIP-seq data proved highly informative when combined with RNA-seq in the pRSEM framework.

Conclusions:

pRSEM enhances the precision of transcript abundance estimation, particularly at the isoform level.
The integration of complementary data types offers a powerful strategy for improving RNA-seq analysis.
This novel method overcomes key challenges in quantifying alternatively spliced isoforms.