Genes Order and Phylogenetic Reconstruction: Application to γ-Proteobacteria

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Abstract. Inferring orthologous and paralogous genes is an important problem in whole genomes comparisons, both for functional or evolutionary studies. In this paper, we introduce a new approach for inferring candidate pairs of orthologous genes between genomes, also called positional homologs, based on the conservation of the genomic context. We consider genomes represented by their gene order -i.e. sequences of signed integers -and common intervals of these sequences as the anchors of the final gene matching. We show that the natural combinatorial problem of computing a maximal cover of the two genomes using the minimum number of common intervals is NP-complete and we give a simple heuristic for this problem. We illustrate the effectiveness of this first approach using common intervals of sequences on two datasets, respectively 8 γ-proteobacterial genomes and the human and mouse whole genomes.

Section: Several Methods Have Been Proposed That Use the Genomic Contmentioning

confidence: 99%

Section: Several Methods Have Been Proposed That Use the Genomic Contmentioning

confidence: 99%

Section: Several Methods Have Been Proposed That Use the Genomic Contmentioning

confidence: 99%

Section: Several Methods Have Been Proposed That Use the Genomic Contmentioning

confidence: 99%

Section: The New Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Inferring Positional Homologs with Common Intervals of Sequences

Blin¹,

Château

Chauve

et al. 2006

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How Pseudo-boolean Programming Can Help Genome Rearrangement Distance Computation

Angibaud

Fertin

Rusu

2006

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Abstract. Computing genomic distances between whole genomes is a fundamental problem in comparative genomics. Recent researches have resulted in different genomic distance definitions: number of breakpoints, number of common intervals, number of conserved intervals, Maximum Adjacency Disruption number (MAD), etc. Unfortunately, it turns out that, in presence of duplications, most problems are NP-hard, and hence several heuristics have been recently proposed. However, while it is relatively easy to compare heuristics between them, until now very little is known about the absolute accuracy of these heuristics. Therefore, there is a great need for algorithmic approaches that compute exact solutions for these genomic distances. In this paper, we present a novel generic pseudo-boolean approach for computing the exact genomic distance between two whole genomes in presence of duplications, and put strong emphasis on common intervals under the maximum matching model. Of particular importance, we show very strong evidence that the simple LCS heuristic provides very good results on a well-known public benchmark dataset of γ-Proteobacteria.

A Pseudo-boolean Programming Approach for Computing the Breakpoint Distance Between Two Genomes with Duplicate Genes

Angibaud

Fertin

Rusu

et al.

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Comparing genomes of different species has become a crucial problem in comparative genomics. Recent research have resulted in different genomic distance definitions: number of breakpoints, number of common intervals, number of conserved intervals, Maximum Adjacency Disruption number (MAD), etc. Classical methods (usually based on permutations of gene order) for computing genomic distances between whole genomes are however seriously compromised for genomes where several copies of the same gene may be scattered across the genome. Most approaches to overcoming this difficulty are based on the exemplar method (keep exactly one copy in each genome of each duplicated gene) and the maximum matching method (keep as many copies as possible in each genome of each duplicated gene). Unfortunately, it turns out that, in presence of duplications, most problems are NP-hard, and hence several heuristics have been recently proposed. Extending research initiated in [2], we propose in this paper a novel generic pseudo-boolean approach for computing the exact breakpoint distance between two genomes in presence of duplications for both the exemplar and maximum matching methods. We illustrate the application of this methodology on a well-known public benchmark dataset of γ-Proteobacteria.