PLoS Computational Biology · 2012 · 12 citations · 33 references
Life SciencesGeneticsMicrobiologyComparative GenomicsGenomicsRecombinationComparative Microbial GenomicsGenome SequencingMicrobial Evolutionary GenomicsMicrobial RecombinationRecombination Rate EvolutionBacterial Recombination MechanismsDna Sequence Data AnalysisHomologous Recombination DetectionRecombination BreakpointsRecombination DetectionRecombination In Microbial GenomesRecombination In Microbial PopulationsStreptococcus Species
Advances in high-throughput DNA sequencing technologies have determined an explosion in the number of sequenced bacterial genomes. Comparative sequence analysis frequently reveals evidences of homologous recombination occurring with different mechanisms and rates in different species, but the large-scale use of computational methods to identify recombination events is hampered by their high computational costs. Here, we propose a new method to identify recombination events in large datasets of whole genome sequences. Using a filtering procedure of the gene conservation profiles of a test genome against a panel of strains, this algorithm identifies sets of contiguous genes acquired by homologous recombination. The locations of the recombination breakpoints are determined using a statistical test that is able to account for the differences in the natural rate of evolution between different genes. The algorithm was tested on a dataset of 75 genomes of Staphylococcus aureus and 50 genomes comprising different streptococcal species, and was able to detect intra-species recombination events in S. aureus and in Streptococcus pneumoniae. Furthermore, we found evidences of an inter-species exchange of genetic material between S. pneumoniae and Streptococcus mitis, a closely related commensal species that colonizes the same ecological niche. The method has been implemented in an R package, Reco, which is freely available from supplementary material, and provides a rapid screening tool to investigate recombination on a genome-wide scale from sequence data.
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MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) Software Version 4.0
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