Events / Bioinformatics Methods for Graph-Based Pangenomics

Bioinformatics Methods for Graph-Based Pangenomics

July 1, 2021
2:00 pm - 3:00 pm

Thursday, July 1, 2021 2-3 PM https://ucsc.zoom.us/j/93631652670
Meeting ID: 93631652670
Passcode: 350271

Description
In most sequencing experiments, sequencing reads are mapped to a reference genome assembly in order to identify the genomic elements that the reads originated from. The mapping process becomes less accurate when the sample’s genome differs from the reference genome. This introduces a pervasive reference bias in which genomics analyses are systematically less accurate for non-reference alleles. In the field of pangenomics, it has been proposed that more general reference structures could mitigate this bias. The fundamental idea is to incorporate population variation into the reference itself. The result is naturally expressible as a sequence graph. In this defense, I will present my research into developing bioinformatics methods for graph-based pangenomic analyses in the variation graph toolkit. I will particularly emphasize work on applying pangenomics methods to transcriptomics to perform haplotype-resolved transcript quantification.

Biography
Jordan Eizenga is a PhD Candidate in Benedict Paten’s lab at UCSC. He completed his undergraduate degree in mathematics and political science in 2011 at the University of Michigan. His research focuses on methods development for computational pangenomics.

Advisor
Benedict Paten

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