Assembling networks of microbial genomes using linear programming

<p>Abstract</p> <p>Background</p> <p>Microbial genomes exhibit complex sets of genetic affinities due to lateral genetic transfer. Assessing the relative contributions of parent-to-offspring inheritance and gene sharing is a vital step in understanding the evolutionary...

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Main Authors: Holloway Catherine, Beiko Robert G
Format: Article
Language:English
Published: BMC 2010-11-01
Series:BMC Evolutionary Biology
Online Access:http://www.biomedcentral.com/1471-2148/10/360
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spelling doaj-6ad7c72af8f846ccb226d034cca3a7192021-09-02T04:22:11ZengBMCBMC Evolutionary Biology1471-21482010-11-0110136010.1186/1471-2148-10-360Assembling networks of microbial genomes using linear programmingHolloway CatherineBeiko Robert G<p>Abstract</p> <p>Background</p> <p>Microbial genomes exhibit complex sets of genetic affinities due to lateral genetic transfer. Assessing the relative contributions of parent-to-offspring inheritance and gene sharing is a vital step in understanding the evolutionary origins and modern-day function of an organism, but recovering and showing these relationships is a challenging problem.</p> <p>Results</p> <p>We have developed a new approach that uses linear programming to find between-genome relationships, by treating tables of genetic affinities (here, represented by transformed BLAST e-values) as an optimization problem. Validation trials on simulated data demonstrate the effectiveness of the approach in recovering and representing vertical and lateral relationships among genomes. Application of the technique to a set comprising <it>Aquifex aeolicus </it>and 75 other thermophiles showed an important role for large genomes as 'hubs' in the gene sharing network, and suggested that genes are preferentially shared between organisms with similar optimal growth temperatures. We were also able to discover distinct and common genetic contributors to each sequenced representative of genus <it>Pseudomonas</it>.</p> <p>Conclusions</p> <p>The linear programming approach we have developed can serve as an effective inference tool in its own right, and can be an efficient first step in a more-intensive phylogenomic analysis.</p> http://www.biomedcentral.com/1471-2148/10/360
collection DOAJ
language English
format Article
sources DOAJ
author Holloway Catherine
Beiko Robert G
spellingShingle Holloway Catherine
Beiko Robert G
Assembling networks of microbial genomes using linear programming
BMC Evolutionary Biology
author_facet Holloway Catherine
Beiko Robert G
author_sort Holloway Catherine
title Assembling networks of microbial genomes using linear programming
title_short Assembling networks of microbial genomes using linear programming
title_full Assembling networks of microbial genomes using linear programming
title_fullStr Assembling networks of microbial genomes using linear programming
title_full_unstemmed Assembling networks of microbial genomes using linear programming
title_sort assembling networks of microbial genomes using linear programming
publisher BMC
series BMC Evolutionary Biology
issn 1471-2148
publishDate 2010-11-01
description <p>Abstract</p> <p>Background</p> <p>Microbial genomes exhibit complex sets of genetic affinities due to lateral genetic transfer. Assessing the relative contributions of parent-to-offspring inheritance and gene sharing is a vital step in understanding the evolutionary origins and modern-day function of an organism, but recovering and showing these relationships is a challenging problem.</p> <p>Results</p> <p>We have developed a new approach that uses linear programming to find between-genome relationships, by treating tables of genetic affinities (here, represented by transformed BLAST e-values) as an optimization problem. Validation trials on simulated data demonstrate the effectiveness of the approach in recovering and representing vertical and lateral relationships among genomes. Application of the technique to a set comprising <it>Aquifex aeolicus </it>and 75 other thermophiles showed an important role for large genomes as 'hubs' in the gene sharing network, and suggested that genes are preferentially shared between organisms with similar optimal growth temperatures. We were also able to discover distinct and common genetic contributors to each sequenced representative of genus <it>Pseudomonas</it>.</p> <p>Conclusions</p> <p>The linear programming approach we have developed can serve as an effective inference tool in its own right, and can be an efficient first step in a more-intensive phylogenomic analysis.</p>
url http://www.biomedcentral.com/1471-2148/10/360
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