Bioinformatics Research Group

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Our research group designs, develops and assesses computational tools for the exploration of genomic and proteomic data. As computer scientists, we also study algorithmic questions inspired by and related to biological problems. Finally, we collaborate with biologists to study the usefulness in practice of the methods we develop. Featured Research Project Haplotype Inference In diploid organisms, such as humans, the identification of maternal and paternal inheritance is important for the mapping of disease genes. Technological limitations make this problem very difficult. It is currently very expensive to experimentally determine these parental haplotypes, and instead the genotype (the conflation of the two haplotypes) is identified. The process of going from genotypes to haplotypes is called haplotype inference. A surprising fact is that while most problems in this area are very hard in theory, real instances of these problems turn out to be very easy to solve in practice. We have proved theorems explaining this phenomenon for simple models of populations, and are currently moving to more complicated models. By understanding how changes to the population model affect the complexity of the problem, we hope to develop more efficient and effective techniques for haplotype inference. Daniel G. Brown, Ian M. Harrower. A New Integer Programming Formulation for the Pure Parsimony Problem in Haplotype Analysis. In Algorithms in Bioinformatics, 4th International Workshop (WABI), 2004. Details Daniel G. Brown, Ian M. Harrower. Integer Programming Approaches to Haplotype Inference by Pure Parsimony. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2006. Details Daniel G. Brown, Ian M. Harrower. Toward an Algebraic Understanding of Haplotype Inference by Pure Parsimony. In Proceedings of Computational Systems Bioinformatics Conference, 2006. Details View more papers on: haplotypes

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