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Structural Variation and Gene Fusion Discovery using Next-Generation Sequencing

Jin Zhang, Genome Institute, Washington University

  • Computer Science Seminar
  • Colloquium
When Tue, Mar 03, 2015
from 04:00 PM to 04:50 PM
Where Ritter Hall 316
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Whole genome sequencing (WGS) has become the primary technology for discovering structural variations (SV), a type of genetic variation that can be associated with complex diseases.  Currently, great efforts have been taken to develop computational methods that discover SVs with exact break points, which are very important for the functional analysis of the SVs. SVs in cancer genomes often lead to the juxtaposition of two genes, which may create gene fusions. Fusions could serve as exquisitely specific diagnostic markers, prognostic indicators and therapeutic targets. Unbiased view offered by deep whole transcriptome sequencing (RNA-Seq) of cancer is enabling the discovery and prioritization of expressed gene fusions for functional analysis.

In this talk, I will briefly introduce the challenges of the fast-evolving research area of split-reads mapping in discovery of SVs and gene fusions using next-generation sequencing (NGS). The talk will focus on a series of advanced algorithms I developed that have dramatically improved the efficiency, sensitivity and accuracy of split-read mapping, enabling the discovery of clinical significant gene fusions across myriad human tumor types.

Specifically, I will cover a set of algorithms elegantly solve the problem of split-read mapping of whole genome sequencing (WGS) data for the identification of chromosomal deletions and insertions (SVseq1 and 2). Finally, I will extend this discussion to include my latest work on a state-of-the-art algorithm capable of combining both WGS and RNA-sequencing data to discover gene fusion events in human carcinogenesis (INTEGRATE), whose broad clinical utility has already led to the identification of novel ESR1 gene fusions in estrogen-resistant breast cancer.

Reception to precede at 3:30 p.m.

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