Article

Dynamic programming algorithms for haplotype block partitioning: applications to human chromosome 21 haplotype data

Authors:

Kui Zhang,

Fengzhu Sun,

Michael S. Waterman,

Ting ChenAuthors Info & Claims

RECOMB '03: Proceedings of the seventh annual international conference on Research in computational molecular biology

Pages 332 - 340

https://doi.org/10.1145/640075.640119

Published: 10 April 2003 Publication History

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Abstract

Recent studies have shown that the human genome has a haplotype block structure such that it can be divided into discrete blocks of limited haplotype diversity. Patil et al. [6] and Zhang et al. [12] developed algorithms to partition haplotypes into blocks with minimum number of tag SNPs for the entire chromosome. However, it is not clear how to partition haplotypes into blocks with restricted number of SNPs when only limited resources are available. In this paper, we first formulated this problem as finding a block partition with a fixed number of tag SNPs that can cover the maximal percentage of a genome. Then we solved it by two dynamic programming algorithms, which are fairly flexible to take into account the knowledge of functional polymorphism. We applied our algorithms to the published SNP data of human chromosome 21 combining with the functional information of these SNPs and demonstrated the effectiveness of them. Statistical investigation of the relationship between the starting points of a block partition and the coding and non-coding regions illuminated that the SNPs at these starting points are not significantly enriched in coding regions. We also developed an efficient algorithm to find all possible long local maximal haplotypes across a subset of samples. After applying this algorithm to the human chromosome 21 haplotype data, we found that samples with long local haplotypes are not necessarily globally similar.

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Dynamic programming algorithms for haplotype block partitioning: applications to human chromosome 21 haplotype data

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Published In

RECOMB '03: Proceedings of the seventh annual international conference on Research in computational molecular biology

April 2003

352 pages

ISBN:1581136358

DOI:10.1145/640075

Editors:
Martin Vingron
Max-Planck-Institute for Molecular Genetics, Germany
,
Sorin Istrail
Celera Genomics/Applied Biosystems
,
Pavel Pevzner
University of California at San Diego, CA
,
Michael Waterman
University of Southern California, CA
,
Program Chair:
Webb Miller
The Pennsylvania State University

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 10 April 2003

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RECOMB03: The Seventh Annual International Conference on Research in Computational Molecular Biology

April 10 - 14, 2003

Berlin, Germany

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Overall Acceptance Rate 148 of 538 submissions, 28%

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Cited By

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Zhang YWan QCheng XLu GWang SHe S(2022)A Tagging SNP Set Method Based on Network Community Partition of Linkage Disequilibrium and Node CentralityCurrent Bioinformatics10.2174/157489361766622032415581317:9(825-834)Online publication date: Nov-2022
https://doi.org/10.2174/1574893617666220324155813
Tarek AElsayed HRashad MHassan Mel kafrawy P(2020)Dynamic Programming Applications: A Suvrvey2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)10.1109/NILES50944.2020.9257968(380-385)Online publication date: 24-Oct-2020
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Zarandi MGamasaee R(2017)Overview of Big Data in HealthcareHandbook of Research on Data Science for Effective Healthcare Practice and Administration10.4018/978-1-5225-2515-8.ch016(360-384)Online publication date: 2017
https://doi.org/10.4018/978-1-5225-2515-8.ch016
Mohammed EFar BNaugler C(2014)Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trendsBioData Mining10.1186/1756-0381-7-227:1Online publication date: 29-Oct-2014
https://doi.org/10.1186/1756-0381-7-22
Chanda PZhang ARamanathan M(2008)Machine Learning Applications in SNP–Disease Association StudyMachine Learning in Bioinformatics10.1002/9780470397428.ch18(389-412)Online publication date: 21-Apr-2008
https://doi.org/10.1002/9780470397428.ch18
Phuong TLin ZAltman R(2005)Choosing SNPs Using Feature SelectionProceedings of the 2005 IEEE Computational Systems Bioinformatics Conference10.1109/CSB.2005.22(301-309)Online publication date: 8-Aug-2005
https://dl.acm.org/doi/10.1109/CSB.2005.22
Schwartz R(2004)Algorithms for Association Study Design Using a Generalized Model of Haplotype ConservationProceedings of the 2004 IEEE Computational Systems Bioinformatics Conference10.5555/1018417.1019245(90-97)Online publication date: 16-Aug-2004
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Kimmel GShamir RBourne PGusfield D(2004)Maximum likelihood resolution of multi-block genotypesProceedings of the eighth annual international conference on Research in computational molecular biology10.1145/974614.974616(2-9)Online publication date: 27-Mar-2004
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Halldórsson BBafna VLippert RSchwartz RDe La Vega FClark AIstrail S(2004)Optimal Haplotype Block-Free Selection of Tagging SNPs for Genome-Wide Association StudiesGenome Research10.1101/gr.257000414:8(1633-1640)Online publication date: 2-Aug-2004
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Zhang KQin ZChen TLiu JWaterman MSun F(2004)HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithmsBioinformatics10.1093/bioinformatics/bth48221:1(131-134)Online publication date: 27-Aug-2004
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