Abstract
In eukaryotes, detailed surveys of recombination rates have shown variation at multiple genomic scales and the presence of “hotspots” of highly elevated recombination. In bacteria, studies of recombination rate variation are less developed, in part because there are few analysis methods that take into account the clonal context within which bacterial evolution occurs. Here, we focus in particular on identifying “hot regions” of the genome where DNA is transferred frequently between isolates. We present a computationally efficient algorithm based on the recently developed “chromosome painting” algorithm, which characterizes patterns of haplotype sharing across a genome. We compare the average genome wide painting, which principally reflects clonal descent, with the painting for each site which additionally reflects the specific deviations at the site due to recombination. Using simulated data, we show that hot regions have consistently higher deviations from the genome wide average than normal regions. We applied our approach to previously analyzed Escherichia coli genomes and revealed that the new method is highly correlated with the number of recombination events affecting each site inferred by ClonalOrigin, a method that is only applicable to small numbers of genomes. Furthermore, we analyzed recombination hot regions in Campylobacter jejuni by using 200 genomes. We identified three recombination hot regions, which are enriched for genes related to membrane proteins. Our approach and its implementation, which is downloadable from https://github.com/bioprojects/orderedPainting, will help to develop a new phase of population genomic studies of recombination in prokaryotes.
Highlights
Recombination is a fundamental driving force in evolution
We illustrate the use of the ordered painting method to infer recombination hot regions in three different data sets: 1) simulated data of a closed recombining population; 2) whole genomes of 27 E. coli isolates, which were recently analyzed by ClonalOrigin; and 3) genomes of 200 Campylobacter jejuni isolates
The results indicate that the method is effective in distinguishing recombination hot regions and others when is 4 or 5
Summary
Recombination is a fundamental driving force in evolution. Patterns of recombination have been studied most actively in humans, revealing considerable variation of recombination rates across the genome with recombination “hotspots” in which the majority of crossover occurs (McVean et al 2004; Myers et al 2005). We illustrate the use of the ordered painting method to infer recombination hot regions in three different data sets: 1) simulated data of a closed recombining population; 2) whole genomes of 27 E. coli isolates, which were recently analyzed by ClonalOrigin; and 3) genomes of 200 Campylobacter jejuni isolates. We inferred recombination hot regions as blocks containing sites in the top percentile of the distance statistic values.
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