The Haplotyping problem: an overview of computational models and solutions
Journal of Computer Science and Technology
Complexity and approximation of the minimum recombinant haplotype configuration problem
Theoretical Computer Science
Bioinformatics
An Efficient Algorithm for Haplotype Inference on Pedigrees with Recombinations and Mutations
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Haplotype-based prediction of gene alleles using pedigrees and SNP genotypes
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Hi-index | 0.00 |
The MINIMUM-RECOMBINANT HAPLOTYPE CONFIGURATION problem (MRHC) has been highly successful in providing a sound combinatorial formulation for the important problem of genotype phasing on pedigrees. Despite several algorithmic advances that have improved the efficiency, its applicability to real data sets has been limited since it does not take into account some important phenomena such as mutations, genotyping errors, and missing data. In this work, we propose the MINIMUM-RECOMBINANT HAPLOTYPE CONFIGURATION WITH BOUNDED ERRORS problem (MRHCE), which extends the original MRHC formulation by incorporating the two most common characteristics of real data: errors and missing genotypes (including untyped individuals). We describe a practical algorithm for MRHCE that is based on a reduction to the well-known Satisfiability problem (SAT) and exploits recent advances in the constraint programming literature. An experimental analysis demonstrates the biological soundness of the phasing model and the effectiveness (on both accuracy and performance) of the algorithm under several scenarios. The analysis on real data and the comparison with state-of-the-art programs reveals that our approach couples better scalability to large and complex pedigrees with the explicit inclusion of genotyping errors into the model.