Computational Methods for Detecting Mapping-Hidden Variants in NGS Data and Applications on Complex Diseases

T.C.

GEBZE YÜKSEK TEKNOLOJİ ENSTİTÜSÜ

Gebze Institute of Technology

Department of Molecular Biology and Genetics

 

MBG SEMINAR

Title: Computational Methods for Detecting Mapping-Hidden Variants in NGS Data and Applications on Complex Diseases.

Emre Karakoç, Ph.D.

Research Associate at the Department of Genome Sciences

The University of Washington, Seattle USA

 

Detecting genomic variants is becoming essential for understanding the etiology of complex diseases. Although the single nucleotide polymorphisms (SNPs) and large copy number variation (CNV) have received considerable attention, the small insertions and deletions (INDELs) remain largely under-discovered and the methods that are discovering INDELs are lagging behind. In this talk I am going to present a computational methods to detect structural variation and INDELs ranging in size from 1 base pair to 1 Mbp within exome sequence data sets as well as whole genome sequencing data sets. Our method is based on split-read approach and it can identify the size, content and location of variants with high specificity and sensitivity. Our algorithm discovers genomic variation including copy number polymorphic processed pseudogenes missed by other methods.

We applied our method for detecting de novo disruptive variants from 209 families with Autism Spectrum Disorder. Based on the human protein-protein interactions networks we also developed methods to rank genes for their topological similarity to the previously identified autism candidate genes. We found that 39% (49 of 126) of the most severe or disruptive de novo mutations map to a highly interconnected ß-catenin/chromatin remodelling protein network ranked significantly for autism candidate genes.

 

DATE: April 3, 2013, Wednesday at 14:30

PLACE: MBG Department, Seminar Room

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