Literature DB >> 17048400

Integer programming approaches to haplotype inference by pure parsimony.

Daniel G Brown1, Ian M Harrower.   

Abstract

In 2003, Gusfield introduced the Haplotype Inference by Pure Parsimony (HIPP) problem and presented an integer program (IP) that quickly solved many simulated instances of the problem. Although it solved well on small instances, Gusfield's IP can be of exponential size in the worst case. Several authors have presented polynomial-sized IPs for the problem. In this paper, we further the work on IP approaches to HIPP. We extend the existing polynomial-sized IPs by introducing several classes of valid cuts for the IP. We also present a new polynomial-sized IP formulation that is a hybrid between two existing IP formulations and inherits many of the strengths of both. Many problems that are too complex for the exponential-sized formulations can still be solved in our new formulation in a reasonable amount of time. We provide a detailed empirical comparison of these IP formulations on both simulated and real genotype sequences. Our formulation can also be extended in a variety of ways to allow errors in the input or model the structure of the population under consideration.

Mesh:

Year:  2006        PMID: 17048400     DOI: 10.1109/TCBB.2006.24

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  7 in total

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Journal:  Bioinformatics       Date:  2008-11-04       Impact factor: 6.937

2.  Estimating Tree-Structured Covariance Matrices via Mixed-Integer Programming.

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3.  Haplotype inference from unphased SNP data in heterozygous polyploids based on SAT.

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Journal:  Algorithms Mol Biol       Date:  2010-01-04       Impact factor: 1.405

5.  Identifying mutation regions for closely related individuals without a known pedigree.

Authors:  Wenjuan Cui; Lusheng Wang
Journal:  BMC Bioinformatics       Date:  2012-06-25       Impact factor: 3.169

6.  A class representative model for Pure Parsimony Haplotyping under uncertain data.

Authors:  Daniele Catanzaro; Martine Labbé; Luciano Porretta
Journal:  PLoS One       Date:  2011-03-25       Impact factor: 3.240

7.  An ILP solution for the gene duplication problem.

Authors:  Wen-Chieh Chang; Gordon J Burleigh; David F Fernández-Baca; Oliver Eulenstein
Journal:  BMC Bioinformatics       Date:  2011-02-15       Impact factor: 3.169

  7 in total

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