Solving Genome Puzzles

Abstract:

Genomics sequencing has created opportunities and challenges to gain new insights into biology and biomedical research. Genome sequencing data usually consists of millions or billions of short DNA sequences, called reads, that are randomly drawn from genomes. Genome assembly is to put reads back together into a single genome, like solving a giant jigsaw puzzle. While the problem of genome assembly remains computationally challenging, metagenomic analysis aims to resolve the composition of a mixture of genomes, like solving mixed jigsaw puzzles simultaneously. In this talk, we will present how to design graph models and algorithms to address these emerging challenges.

Bio:

Yu Lin is a Senior Lecturer and runs the Computational Genomics Group at the School of Computing, the Australian National University. Prior to this, he was a postdoctoral fellow at the Department of Computer Science and Engineering, University of California San Diego (UCSD). He received his PhD in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. His research focuses on computational biology and bioinformatics, and he has been working on models and algorithms for genome assembly, metagenomics and comparative genomics. In 2020, he received the Outstanding Contribution by an Early Career Researcher Award from the Australian Bioinformatics and Computational Biology Society.

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Topic: Mathematics and Computational Sciences Seminar Series 
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