NDLI: ReFHap: a reliable and fast algorithm for single individual haplotyping

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ReFHap: a reliable and fast algorithm for single individual haplotyping

Content Provider	ACM Digital Library
Author	Duitama, Jorge Huebsch, Thomas McEwen, Gayle Suk, Eun-Kyung Hoehe, Margret R.
Abstract	Full human genomic sequences have been published in the latest two years for a growing number of individuals. Most of them are a mixed consensus of the two real haplotypes because it is still very expensive to separate information coming from the two copies of a chromosome. However, latest improvements and new experimental approaches promise to solve these issues and provide enough information to reconstruct the sequences for the two copies of each chromosome through bioinformatics methods such as single individual haplotyping. Full haploid sequences provide a complete understanding of the structure of the human genome, allowing accurate predictions of translation in protein coding regions and increasing power of association studies. In this paper we present a novel problem formulation for single individual haplotyping. We start by assigning a score to each pair of fragments based on their common allele calls and then we use these score to formulate the problem as the cut of fragments that maximize an objective function, similar to the well known max-cut problem. Our algorithm initially finds the best cut based on a heuristic algorithm for max-cut and then builds haplotypes consistent with that cut. We have compared both accuracy and running time of ReFHap with other heuristic methods on both simulated and real data and found that ReFHap performs significantly faster than previous methods without loss of accuracy.
Starting Page	160
Ending Page	169
Page Count	10
File Format	PDF
ISBN	9781450304382
DOI	10.1145/1854776.1854802
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2010-08-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Variants Algorithms Efficiency Fragments Haplotyping Maximum cut Heuristic
Content Type	Text
Resource Type	Article

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