Profiling of structural variants and complex rearrangements in cancer genome using long-read sequencing

About this Class

Many recent studies highlighted the improved capability of long-read sequencing to detect structural variation in the human genome. For example, these technologies was also recently utilized to produce the first complete assembly of the human genome by the Telomere-to-Telomere consortium. Further, Human Pangenome Reference Consortium has recently released 47 nearly-complete haplotype-resolved human genomes from diverse backgrounds. A few recent studies have utilized long-read sequencing to discover complex genomic changes such as chromothripsis or ecDNA formation in cancer patients. However the broad application of the technology is facing additional hurdles, such as patient sample availability, high-molecular weight DNA extraction, tumor heterogeneity and purity among others. Compared to short-read sequencing, there are little-to-no existing computational approaches to analyze cancer long-read data either. In this discussion, I will summarize the recent successes of long-read sequencing. Then, I will give my perspective on the promises and challenges of the application of long reads to cancer genomes and our plans to overcome them. Bio: Before joining the Cancer Data Science Laboratory in January 2022, Dr. Mikhail Kolmogorov 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 the 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.