DIMACS Workshop on Algorithmics in Human Population-Genomics

April 27 - 29, 2009
DIMACS Center, CoRE Building, Rutgers University, Piscataway, NJ

Jaume Bertranpetit, Universitat Pompeu Fabra, jaume.bertranpetit at upf.edu
Dan Gusfield, University of California, gusfield@cs.ucdavis.edu
Laxmi Parida, IBM T J Watson Research, parida at us.ibm.com
Presented under the auspices of the DIMACS/BioMaPS/MB Center Special Focus on Information Processing in Biology.

This special focus is jointly sponsored by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), the Biological, Mathematical, and Physical Sciences Interfaces Institute for Quantitative Biology (BioMaPS), and the Rutgers Center for Molecular Biophysics and Biophysical Chemistry (MB Center).

Algorithmics played arguably a pivotal role in the successful completion of the Human Genome Project and continues to be of great importance in the post-genomic era. One fertile area is human population genomics: apparently there is more genetic diversity, in terms of SNPs (Single Nucleotide Polymorphisms), inversions, copy number variations and so on, than was believed earlier. As more and more genomic or haplotype/genotype data become available (and soon full genomes will be at hand), it becomes imperative to analyze the wide spectrum of variations from individual-specific to subpopulation-specific signatures. Broadly speaking, while the former is useful to address issues in personalized medicine, pharmacogenomics or forensics, the latter reveals population substructures of interest for association studies and the whole field of genetic epidemiology, and for the analysis of population history through phylogeography or for understanding different adaptations around the world.

In particular, to name a few problems of interest: understanding the subpopulation substructures through the differences in the selective pressures and adaptation of humans at different natural (like climate and insulation) or human-made (metabolism of food or drugs) environments; looking for evidence of recent selection; understanding the relationship of Homo sapiens to Neanderthals; understanding the role of infections and disease in shaping human evolution; phylogeny of specific human loci (for example mtDNA or Y chromosome or specific autosomal regions); understanding different subpopulation response to drugs or disease.

Various such areas are beginning to gain momentum due to efforts in algorithm design for more sophisticated and realistic scenarios. The workshop is intended to bring computer scientists (including mathematicians and statisticians) and population geneticists together to better understand the core issues and make a synergistic effort in solving these (and other)thorny problems of the area.

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Document last modified on August 20, 2008.