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Computational inference of mRNA stability from histone modification and transcriptome profiles.
Chengyang Wang1, Rui Tian, Qian Zhao
1Department of Bioinformatics, School of Life Science and Technology, Tongji University, 1239 Siping Road, Shanghai 20092, China.
Nucleic Acids Research
|April 13, 2012
Summary
This study introduces a novel computational model using histone modifications to infer messenger RNA (mRNA) stability. The model effectively distinguishes between stable and unstable mRNAs, offering a new strategy for experimental research.
Area of Science:
- Molecular Biology
- Epigenetics
- Genomics
Background:
- Histone modifications are crucial regulators of eukaryotic gene expression.
- Existing methods for modeling expression levels have limitations.
- Systematic inference of mRNA stability is essential for understanding gene regulation.
Purpose of the Study:
- To develop a computational model for inferring mRNA stability.
- To utilize histone modifications (H3K4me3, H3K27me3, H3K36me3) alongside transcriptome profiles for this inference.
- To provide an alternative strategy for experimental measurements of mRNA stability.
Main Methods:
- A regression model was developed to compare transcriptome profiles with ChIP-seq data for specific histone modifications.
- The model was applied to multiple human and mouse cell lines.
- Regression residuals from RNA-seq were correlated with experimentally measured mRNA half-lives.
Main Results:
- Inferred unstable mRNAs exhibit longer 3' Untranslated Regions (UTRs) and more microRNA binding sites within 3'UTRs compared to stable mRNAs.
- Regression residuals from RNA-seq, unlike GRO-seq, showed high correlation with experimentally determined mRNA half-lives.
- Functional enrichment analysis revealed cell-type specificity in unstable mRNAs under functional constraint.
Conclusions:
- Histone modifications can systematically differentiate non-expressed from unstable mRNAs, and stable from highly expressed mRNAs.
- This computational model represents a novel approach to mRNA stability inference by integrating transcriptome and epigenome data.
- The study offers a valuable alternative strategy for guiding experimental investigations into mRNA stability.
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