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Activity Number:
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328
- Statistical Methods for Multi-Omics Data Integration
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #312683
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Title:
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Integrative Analysis of RNA-Seq and CLIP-Seq Data Sets with SURF
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Author(s):
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Fan Chen* and Sunduz Keles
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Companies:
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University of Wisconsin and University of Wisconsin, Madison
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Keywords:
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RNA-binding protein;
CLIP-seq;
RNA-seq;
Integrative modeling;
Alternative splicing;
Alternative polyadenylation
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Abstract:
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Post-transcriptional regulation by RNA binding proteins (RBPs) is a major contributor to protein diversity in mammalian genomes. Advances in ultraviolet cross-linking immunoprecipitation followed by high-throughput sequencing (CLIP-seq) resulted in collections of RBP binding datasets coupled with transcriptome profiling by RNA-seq. However, methods that can integrate analysis of both types of data are lacking. We leveraged a large collection of CLIP-seq and RNA-seq data from the ENCODE consortium and developed SURF, Statistical Utility for RBP Functions. SURF accurately detects differential alternative transcriptional regulation (ATR) events and associates them to local protein-RNA interactions. Large-scale application of SURF recovered known roles of a handful of RBPs and generated novel hypotheses for the others under a well-calibrated false discovery rate. Downstream analysis of SURF-identified protein-RNA interaction regions exhibited significant enrichment of somatic mutations in both the TCGA and ICGC datasets. A comparison of SURF-identified transcript targets of RBPs across GTEx and TCGA compendia highlighted specific ATR roles for RBPs in adult acute myeloid leukemia.
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Authors who are presenting talks have a * after their name.
