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Nat Commun. 2019 Mar 22;10(1):1338. doi: 10.1038/s41467-019-09292-w.

Allele-specific binding of RNA-binding proteins reveals functional genetic variants in the RNA.

Author information

1
Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA, 90095, USA.
2
Department of Bioengineering, UCLA, Los Angeles, CA, 90095, USA.
3
Molecular, Cellular and Integrative Physiology Interdepartmental Program, UCLA, Los Angeles, CA, 90095, USA.
4
Department of Psychiatry and Behavioral Sciences, Department of Genetics, Stanford University School of Medicine, Palo Alto, CA, 94305, USA.
5
Department of Cellular and Molecular Medicine, UCSD, La Jolla, CA, 92093, USA.
6
Institute for Genomic Medicine, UCSD, La Jolla, CA, 92093, USA.
7
Department of Biology, MIT, Cambridge, MA, 02139, USA.
8
Department of Genetics and Genome Sciences, Institute for Systems Genomics, UConn Health, Farmington, CT, 06030, USA.
9
Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117593, Singapore.
10
Molecular Engineering Laboratory, A*STAR, Singapore, 138673, Singapore.
11
Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA, 90095, USA. gxxiao@ucla.edu.
12
Department of Bioengineering, UCLA, Los Angeles, CA, 90095, USA. gxxiao@ucla.edu.
13
Molecular, Cellular and Integrative Physiology Interdepartmental Program, UCLA, Los Angeles, CA, 90095, USA. gxxiao@ucla.edu.
14
Molecular Biology Institute, UCLA, Los Angeles, CA, 90095, USA. gxxiao@ucla.edu.

Abstract

Allele-specific protein-RNA binding is an essential aspect that may reveal functional genetic variants (GVs) mediating post-transcriptional regulation. Recently, genome-wide detection of in vivo binding of RNA-binding proteins is greatly facilitated by the enhanced crosslinking and immunoprecipitation (eCLIP) method. We developed a new computational approach, called BEAPR, to identify allele-specific binding (ASB) events in eCLIP-Seq data. BEAPR takes into account crosslinking-induced sequence propensity and variations between replicated experiments. Using simulated and actual data, we show that BEAPR largely outperforms often-used count analysis methods. Importantly, BEAPR overcomes the inherent overdispersion problem of these methods. Complemented by experimental validations, we demonstrate that the application of BEAPR to ENCODE eCLIP-Seq data of 154 proteins helps to predict functional GVs that alter splicing or mRNA abundance. Moreover, many GVs with ASB patterns have known disease relevance. Overall, BEAPR is an effective method that helps to address the outstanding challenge of functional interpretation of GVs.

PMID:
30902979
PMCID:
PMC6430814
DOI:
10.1038/s41467-019-09292-w
[Indexed for MEDLINE]
Free PMC Article

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