Haplotyping as perfect phylogeny: conceptual framework and efficient solutions
Proceedings of the sixth annual international conference on Computational biology
A Dataset Generator for Whole Genome Shotgun Sequencing
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
SNPs Problems, Complexity, and Algorithms
ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
Polynomial and APX-hard cases of the individual haplotyping problem
Theoretical Computer Science - Pattern discovery in the post genome
Technical comment: A clustering algorithm based on two distance functions for MEC model
Computational Biology and Chemistry
Haplotyping Populations by Pure Parsimony: Complexity of Exact and Approximation Algorithms
INFORMS Journal on Computing
Algorithmica - Parameterized and Exact Algorithms
WABI '07 Proceedings of the 7th international workshop on Algorithms in Bioinformatics
Efficient Distributed Genetic Algorithm for Rule Extraction
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
A Survey: Genetic Algorithms and the Fast Evolving World of Parallel Computing
HPCC '08 Proceedings of the 2008 10th IEEE International Conference on High Performance Computing and Communications
Bioinformatics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Haplotype assembly from aligned weighted SNP fragments
Computational Biology and Chemistry
On the complexity of several haplotyping problems
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
A polynomial case of the parsimony haplotyping problem
Operations Research Letters
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Discovering ways to reconstruct reliable Single Individual Haplotypes (SIHs) becomes one of the core issues in the whole-genome research nowadays as previous research showed that haplotypes contain more information than individual Singular Nucleotide Polymorphisms (SNPs). Although with advances in high-throughput sequencing technologies obtaining sequence information is becoming easier in today's laboratories, obtained sequences from current technologies always contain inevitable sequence errors and missing information. The SIH reconstruction problem can be formulated as bi-partitioning the input SNP fragment matrix into paternal and maternal sections to achieve minimum error correction (MEC) time; the problem that is proved to be NP-hard. Several heuristics or greedy algorithms have already been designed and implemented to solve this problem, most of them however (1) do not have the ability to handle data sets with high error rates and/or (2) can only handle binary input matrices. In this study, we introduce a Genetic Algorithm (GA) based method, named GAHap, to reconstruct SIHs with lowest MEC times. GAHap is equipped with a well-designed fitness function to obtain better reconstruction rates. GAHap is also compared with existing methods to show its ability in generating highly reliable solutions.