title: Efficient Inference of Recent and Ancestral Recombination within Bacterial Populations.
abstract: Prokaryotic evolution is affected by horizontal transfer of genetic material through recombination. Inference of an evolutionary tree of bacteria thus relies on accurate identification of the population genetic structure and recombination-derived mosaicism. Rapidly growing databases represent a challenge for computational methods to detect recombinations in bacterial genomes. We introduce a novel algorithm called fastGEAR which identifies lineages in diverse microbial alignments, and recombinations between them and from external origins. The algorithm detects both recent recombinations (affecting a few isolates) and ancestral recombinations between detected lineages (affecting entire lineages), thus providing insight into recombinations affecting deep branches of the phylogenetic tree. In simulations, fastGEAR had comparable power to detect recent recombinations and outstanding power to detect the ancestral ones, compared with state-of-the-art methods, often with a fraction of computational cost. We demonstrate the utility of the method by analyzing a collection of 616 whole-genomes of a recombinogenic pathogen Streptococcus pneumoniae, for which the method provided a high-resolution view of recombination across the genome. We examined in detail the penicillin-binding genes across the Streptococcus genus, demonstrating previously undetected genetic exchanges between different species at these three loci. Hence, fastGEAR can be readily applied to investigate mosaicism in bacterial genes across multiple species. Finally, fastGEAR correctly identified many known recombination hotspots and pointed to potential new ones. Matlab code and Linux/Windows executables are available at https://users.ics.aalto.fi/~pemartti/fastGEAR/ (last accessed February 6, 2017).
authors.affiliation: Department of Infectious Disease Epidemiology, St. Mary's Campus, Imperial College London, London, United Kingdom.
authors.forename: Rafal
authors.initials: R
authors.lastname: Mostowy
authors.affiliation: Department of Infectious Disease Epidemiology, St. Mary's Campus, Imperial College London, London, United Kingdom.
authors.forename: Nicholas J
authors.initials: NJ
authors.lastname: Croucher
authors.affiliation: Department of Epidemiology, Harvard TH Chan School of Public Health, Center for Communicable Disease Dynamics, Boston, MA.
authors.forename: Cheryl P
authors.initials: CP
authors.lastname: Andam
authors.affiliation: Department of Mathematics and Statistics, Helsinki Institute for Information Technology HIIT, University of Helsinki, Helsinki, Finland.|Department of Biostatistics, University of Oslo, Oslo, Norway.
authors.forename: Jukka
authors.initials: J
authors.lastname: Corander
authors.affiliation: Department of Epidemiology, Harvard TH Chan School of Public Health, Center for Communicable Disease Dynamics, Boston, MA.
authors.forename: William P
authors.initials: WP
authors.lastname: Hanage
authors.affiliation: Department of Computer Science, Helsinki Institute for Information Technology HIIT, Aalto University, Espoo, Finland.
authors.forename: Pekka
authors.initials: P
authors.lastname: Marttinen
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D000072441:DNA, Ancient / Q000032:analysis; D019143:Evolution, Molecular; D005798:Genes, Bacterial / Q000235:genetics*; D005828:Genetics, Population / Q000379:methods*; D016680:Genome, Bacterial / Q000235:genetics; D010802:Phylogeny; D011995:Recombination, Genetic / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*; D013296:Streptococcus pneumoniae / Q000235:genetics
chemical_list: D000072441:DNA, Ancient
keywords: Streptococcus pneumoniae; antibiotic resistance; bacterial population genetics; hidden Markov models; population structure; recombination detection