Dr. Michael Brudno, University of Toronto
Even as the number of sequenced genomes is growing rapidly, the process of assembling sequencing data into a genome remains exceptionally challenging. The difficulty is due to the fact that current technologies are unable to sequence a whole genome in one pass, relying instead on the shotgun method: the genome is broken into many small segments (reads) whose sequence is then determined. The information about the location of these reads in the genome is lost, and the assembler must put them back together using the information about the overlaps between the reads.
A Graphical Modeling Framework to Study Complex Dependence Patterns in High-Dimensional Biological Data
[url=mailto:firstname.lastname@example.org]Dr. Laurent Briollais[/url] , University of Toronto
Graphical models have been one of the most efficient statistical tools used in the last twenty years for the analysis of complex structured high-dimensional data. Graphical models provide a probabilistic framework for making inference and representing the knowledge that we have about these complex structured data. In biological research and more particularly in the emerging -omics disciplines such as genomics, proteomics, metabolomics, transcriptomics, data are often generated from complex high throughput experiments and from complex experimental designs.
[url=http://www.genizon.com/]Genizon Biosciences Inc.[/url]
[url=http://www.tgen.org/]Translational Genomics Research Institute[/url]