# Genome rearrangements in evolution

## Dr. David Sankoff

Canada Research Chair in Mathematical Genomics

Professor, Department of Mathematics and Statistics, University of Ottawa

**November 28, 2007**

admission is free

13:30 – 15:00

Mackenzie Building 4359

Carleton University

Light refreshment will be served

## Abstract

During evolution, the order of elements on chromosomes, and their partition
among the chromosomes, is scrambled by the accumulation of rearrangement
mutation events. The main kinds of genetic event are the inversion of a
chromosomal segment of arbitrary length and the reciprocal translocation of
segments of arbitrary length (prefix and/ or sufffix exchange) between two
chromosomes. Given two related genomes, it is important to try to infer the
evolutionary events that have intervened since their last common ancestor. We
discuss the combinatorial optimization approach to this problem and its many
generalizations, all based on the "breakpoint graph". There is also need for
ways to statistically validate the results.We explore idea that the null
hypothesis for genome comparison is provided by two genomes, where the order of
elements in one is an appropriately randomized permutation of the order in the
other. I.e., are the characteristics of the evolutionary history of two related
genomes as inferred from an algorithmic analysis different from the chance
patterns obtained from two unrelated genomes? We illustrate these consideration
using data from yeast, cereals, mammals and organelle genomes.

## Biography

David Sankoff received his PhD in mathematics from McGill University under
the direction of Donald Dawson, and has been a member of the Centre de
recherches mathematiques in Montreal for many years. He currently holds the
Canada Research Chair in Mathematical Genomics in the Mathematics and Statistics
Department at the University of Ottawa, and is cross-appointed to the Biology
Department and the School of Information Technology and Engineering. His
research interest is comparative genomics, particularly probability models,
statistics and algorithms for genome rearrangements.

*Last modified 07-11-19*