Also on Zoom- https://umd.zoom.us/j/91856113217?pwd=U1h2QzhlMFRMbkZzOUJ0R0E4ZFFjZz09
Long-read sequencing technologies have substantially improved our ability to study large and complex genomes. However, de novo assembly of complex genomic and metagenomic datasets remains difficult. In this talk, I will give an algorithmic overview of the genome assembly problem. I will also highlight our Flye assembler that uses repeat graphs to generate accurate and complete assemblies. Finally, I will also present our new metagenomic assembler metaFlye, which addresses important long-read metagenomic assembly challenges, such as uneven bacterial composition and intra-species heterogeneity. Using metaFlye, we were able to recover complete or nearly-complete bacterial genomes from complex environmental samples, such as human gut or cow rumen. We also showed that long-read assembly of human microbiomes enables the discovery of full-length biosynthetic gene clusters that encode biomedically important natural products.
Before joining the Cancer Data Science Laboratory in January 2022, Mikhail was a postdoctoral fellow at the University of California (UC) Santa Cruz, supervised by Dr. Benedict Paten. Prior to that, he was a postdoctoral fellow at UC San Diego, co-supervised by Dr. Rob Knight and Dr. Pavel Pevzner. Mikhail completed his Ph.D. in September 2019 in Computer Science from UC San Diego, under the mentorship of Dr. Pavel Pevzner. He received his M.Sc. in bioinformatics from St. Petersburg University of the Russian Academy of Sciences