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IW-Scoring: an Integrative Weighted Scoring framework for annotating and prioritizing genetic variations in the
Jun Wang1, Abu Z Dayem Ullah1, Claude Chelala1,2
1Centre for Molecular Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.
Nucleic Acids Research
|February 2, 2018
Summary
Identifying functional noncoding variations is challenging. Our new Integrative Weighted Scoring (IW-Scoring) model effectively prioritizes these variants, showing promise in cancer research.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Most genomic variations reside in noncoding regions, yet identifying functional ones is difficult.
- Prioritizing noncoding variants with functional significance is crucial for understanding disease mechanisms.
Purpose of the Study:
- To develop and validate a novel Integrative Weighted Scoring (IW-Scoring) model.
- To enhance the annotation and prioritization of functionally relevant noncoding genomic variations.
Main Methods:
- Evaluation of 11 scoring methods.
- Application of unsupervised spectral approach for integration.
- Development of two linear weighted functional scoring schemas for known and novel variations.
Main Results:
- IW-Scoring demonstrated stable, high-quality predictive performance across three datasets.
- Successfully identified functional mutations in the TERT promoter and disease-associated SNPs.
- Located and validated 9 recurrently mutated noncoding regions in follicular lymphoma, including PAX5 regulatory elements.
Conclusions:
- IW-Scoring is a robust and versatile tool for identifying trait- and disease-associated noncoding variants.
- The model shows significant utility in cancer genomics, particularly in identifying novel driver mutations.
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