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A practical parameterised algorithm for the individual haplotyping problem MLF†

Published online by Cambridge University Press:  27 October 2010

MINZHU XIE ,
JIANXIN WANG  and
JIANER CHEN
MINZHU XIE
Affiliation:
School of Information Science and Engineering, Central South University, Changsha 410083, P.R.China and College of Physics and Information Science, Hunan Normal University, Changsha 410081, P.R.China Email: xieminzhu@hotmail.com
JIANXIN WANG
Affiliation:
School of Information Science and Engineering, Central South University, Changsha 410083, P.R.China Email: jxwang@mail.csu.edu.cn
JIANER CHEN
Affiliation:
School of Information Science and Engineering, Central South University, Changsha 410083, P.R.China and Department of Computer Science, Texas A&M University, College Station, TX 77843, U.S.A. Email: chen@cs.tamu.edu
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Abstract

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Haplotypes are more useful in complex disease gene mapping than single-nucleotide polymorphisms (SNPs). However, haplotypes are difficult to obtain directly using biological experiments, which has prompted research into efficient computational methods for determining haplotypes. The individual haplotyping problem called Minimum Letter Flip (MLF) is a computational problem that, given a set of aligned DNA sequence fragment data of an individual, induces the corresponding haplotypes by flipping minimum SNPs. There has been no practical exact algorithm for solving the problem. Due to technical limits in DNA sequencing experiments, the maximum length of a fragment sequenced directly is about 1kb. In consequence, with a genome-average SNP density of 1.84 SNPs per 1 kb of DNA sequence, the maximum number k1 of SNP sites that a fragment covers is usually small. Moreover, in order to save time and money, the maximum number k2 of fragments that cover an SNP site is usually no more than 19. Building on these fragment data properties, the current paper introduces a new parameterised algorithm with running time O(nk22k2 + mlogm + mk1), where m is the number of fragments and n is the number of SNP sites. In practical biological applications, the algorithm solves the MLF problem efficiently even if m and n are large.

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