JUMP seminar Talk

Speaker: Max Alekseyev, George Washington Univ.
Title: Genome rearrangements: when intuition fails
(* The JUMP seminar is part of the Joint Undergraduate Mathematics & Physics (JUMP) Scholarship program. See more info at
http://math.columbian.gwu.edu/jump-scholarship)

Abstract:

Genome rearrangements are genomic "earthquakes" that change the chromosomal architectures. The minimum number of rearrangements between two genomes (called "genomic distance") represents a rather accurate measure for the evolutionary distance between them and is often used as such in comparative genomics studies.

In this talk I shall describe two rather unexpected phenomena in genome rearrangements analysis.
First, the weighted genomic distance designed to bound the proportion of transpositions (that are complex rearrangements rarely happening in reality) in rearrangement scenarios between two genomes does not actually achieve this goal.
Second, while the median score of three genomes can be approximated by the sum of their pairwise genomic distances (up to a constant factor), these two measures of evolutionary distance of genomes are no so much correlated as one's intuition may suggest.

Bio:

Dr. Max Alekseyev is an associate professor at the Department of Mathematics & Computational Biology Institute, George Washington University. He received a Ph.D. in computer science from the University of California, San Diego, and in 2009-2013 was an assistant professor of computer science at University of South Carolina. In 2011, Dr. Alekseyev served as a scientific director for the Algorithmic Biology Laboratory at St. Petersburg Academic University, Russia, where he led development of genome assembler SPAdes. He received an NSF CAREER award in 2013. Dr. Alekseyev's research interests range from discrete mathematics (particularly, combinatorics and graph theory) to bioinformatics (particularly, comparative genomics and phylogenomics). His research is focused on the development and application of new methods of discrete mathematics to solve old and recently emerged open biological problems.