Efficient Haplotype Inference with Pseudo-boolean Optimization

Abstract. Haplotype inference from genotype data is a key computational problem in bioinformatics, since retrieving directly haplotype information from DNA samples is not feasible using existing technology. One of the methods for solving this problem uses the pure parsimony criterion, an approach known as Haplotype Inference by Pure Parsimony (HIPP). Initial work in this area was based on a number of different Integer Linear Programming (ILP) models and branch and bound algorithms. Recent work has shown that the utilization of a Boolean Satisfiability (SAT) formulation and state of the art SAT solvers represents the most efficient approach for solving the HIPP problem. Motivated by the promising results obtained using SAT techniques, this paper investigates the utilization of modern Pseudo-Boolean Optimization (PBO) algorithms for solving the HIPP problem. The paper starts by applying PBO to existing ILP models. The results are promising, and motivate the development of a new PBO model...

• Type: Conference
• Year: 2007
• Where: AB
• Authors: Ana Graça, João Marques-Silva, Inês Lynce, Arlindo L. Oliveira
• Keywords: AB 2007, Computational Biology, Haplotype Inference, HIPP Problem, Pure Parsimony