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An Information Theory-based Unified Analytic Framework for Prioritization of Non-Coding Variants of Uncertain Significance in Heritable Breast and Ovarian Cancer

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  • Fri, 11/11/2016 - 11:00am - 12:00pm

Computer Science Colloquium Series
Dr. Eliseos Mucaki

Date:  Friday, November 11th, 2016
Time: 11:00 am
Location: Chrysler Hall North, G125

Abstract: Sequencing of both healthy and disease singletons yields many novel and low frequency variants of uncertain significance (VUS). Complete gene and genome sequencing by next generation sequencing (NGS) significantly increases the number of VUS detected. While prior studies have emphasized protein coding variants, non-coding sequence variants also significantly contribute to high penetrance disorders, such as hereditary breast and ovarian cancer (HBOC). We present a strategy for analyzing different functional classes of non-coding variants based on information theory (IT) and prioritizing patients with large intragenic deletions. We captured and enriched for coding and non-coding variants in genes known to harbor mutations that increase HBOC risk using custom oligonucleotide baits spanning their complete coding, non-coding, and intergenic regions. Unique and divergent repetitive sequences were sequenced in 389 high-risk, anonymized patients without identified mutations in BRCA1/2. IT-based sequence analysis was used to identify and prioritize pathogenic non-coding variants that occurred within sequence elements predicted to be recognized by proteins or protein complexes involved in mRNA splicing, transcription, and untranslated region (UTR) binding and structure. From two separate studies, 53,683 unique variants were identified. With the unified IT-framework, 332 functionally significant VUS were further prioritized. We also identified 9 stop-gain variants and 7 reading-frame altering exonic insertions/deletions, as well as 2 in-frame deletions. The described strategy for complete gene sequence analysis, followed by a unified framework for interpreting non-coding variants that may affect gene expression, can distill large numbers of variants detected by NGS to a limited set of variants prioritized as potential deleterious changes.

Bio: Eliseos Mucaki has been a researcher at the University of Western Ontario for the last 8 years, working with Dr.’s Peter Rogan and Joan Knoll, with projects focusing on high-throughput DNA sequencing of hereditary breast and ovarian cancer, the large-scale evaluation of coding and non-coding DNA variants and their affect on protein binding, as well as the design and testing of many bioinformatic tools for DNA and RNA sequencing, oligo capture microarray design, the prediction of chemotherapy response based on machine-learning techniques and variant analysis. Regarding variant analysis, he began the development of what would become the Shannon Pipeline (high-throughput splicing variant analysis), which has now been released as a commercial product. Eliseos was born in Windsor, and obtained both his Bachelors and his Masters degree at the University of Windsor under the supervision of Dr. Panayotis Vacratsis in Biochemistry.

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