ArticleBMC evolutionary biology2010
Assembling networks of microbial genomes using linear programming.
Article in BMC evolutionary biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Interactions in the microbiome: communities of organisms and communities of genes.FEMS microbiology reviews · 2014Review
- Being Aquifex aeolicus: Untangling a hyperthermophile's checkered past.Genome biology and evolution · 2013Article
- Telling the whole story in a 10,000-genome world.Biology direct · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
backgroundMicrobial 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.
resultsWe 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 Aquifex aeolicus 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 Pseudomonas.
conclusionsThe 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.
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Registered trials
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