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Mathbio Seminar

April 8, 2020 - 10:30am

Zoom link:

Speaker: Melissa Spence

Title: Genomic Signal Processing for Structural Variant Detection in Related Individuals

Abstract: This talk serves as a rough draft/ practice talk for my thesis defense later this month. In this work we develop a general optimization framework to more accurately recover structural variants (SVs) in low-coverage sequencing data fromgenomes of related individuals. In previous work the framework incorporated biological constraints that reect relatedness between individuals and enforced sparsity to model the rarity of SVs. This framework operated under the assumption that the genomes were haploid, meaning that each individual had one copy of the genetic material. There are two main contributions of this thesis:First we propose an approach that allows the child signal to possess variants that are not present in either parent (i.e., novel SVs) under the assumption of haploid signals. Second, we propose an approach to reconstruct the signals of two parents and a child under the assumption of diploid genomes. We tested the effectiveness of these approaches on both simulated data and data from the1000 Genomes Project.