A Practical Exact Algorithm for the Individual Haplotyping Problem MEC/GI

Authors:
Minzhu Xie;Jianxin Wang;Jianer Chen
Affiliations:
School of Information Science and Engineering, Central South University, Changsha, P.R. China 410083 and College of Physics and Information Science, Hunan Normal University, Changsha, P.R. China 4 ...;School of Information Science and Engineering, Central South University, Changsha, P.R. China 410083;School of Information Science and Engineering, Central South University, Changsha, P.R. China 410083 and Department of Computer Science, Texas A&M University, USA TX 77843
Venue:
COCOON '08 Proceedings of the 14th annual international conference on Computing and Combinatorics
Year:
2008

Citing 3
Cited 0

A Dataset Generator for Whole Genome Shotgun Sequencing

Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
SNPs Problems, Complexity, and Algorithms

ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
Haplotype reconstruction from SNP fragments by minimum error correction

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Given the genotype and the aligned single nucleotide polymorphism (SNP) fragments of an individual, Minimum Error Correction with Genotype Information(MEC/GI) is an important computational model to infer a pair of haplotypes compatible with the genotype by correcting minimum number of SNPs in the given SNP fragments. For the problem, there has been no practical exact algorithm. In DNA sequencing experiments, due to technical limits, the maximum length of a fragment sequenced directly is about 1kb. In consequence, the maximum number kof SNP sites that a fragment covers is usually small (usually smaller than 10). Based on the observation above, the current paper introduces a new parameterized dynamic programming algorithm of running time O(mk2k+ mlogm+ mk), where mis the number of fragments. The algorithm solves the MEC/GI problem efficiently even if the number of fragments and SNPs are large, and is practical in real biological applications.