title: Detection of homologous recombination in closely related strains.
abstract: Detection of recombination events in a bacterial genome is both important from the evolutionary point of view, and of practical interest. Indeed, homologous recombination (HR) plays a major role in the exchange of antigenic determinants between strains. There exist statistical methods to detect recently recombined segments in whole-genome sequences that use a high local density of substitutions as a signal of HR events with a source outside considered strains. However, it is difficult to detect the HR events within a set of strains, which represent whole species diversity, due to a low number of substitutions in recombined segments and high level of diversity of strains. Here, we analyzed HR in 20 Escherichia coli (E. coli) strains to define what fraction of segments with a high substitution rate were introduced in a genome by HR. For detection of HR, we used the segmentation, performed by the adaptive weights smoothing (AWS) algorithm. It detects sharp changes in the structure of observed data analyzing only qualitative structural information. We validated the approach on simulated data, applied it to the analysis of E. coli strains, and determined the recombination rates between phylogroups.
authors.affiliation: * A. A. Kharkevich Institute for Information Transmission Problems, RAS, Moscow, Russia.
authors.forename: Anastasia S
authors.initials: AS
authors.lastname: Kalinina
authors.affiliation: * A. A. Kharkevich Institute for Information Transmission Problems, RAS, Moscow, Russia.|† The International Research Training Group 1792, Berlin, Germany.|‡ Moscow Institute of Physics and Technology, Laboratory of Structural Methods of Data Analysis in Predictive Modeling, Moscow, Russia.
authors.forename: Alexandra L
authors.initials: AL
authors.lastname: Suvorikova
authors.affiliation: * A. A. Kharkevich Institute for Information Transmission Problems, RAS, Moscow, Russia.|‡ Moscow Institute of Physics and Technology, Laboratory of Structural Methods of Data Analysis in Predictive Modeling, Moscow, Russia.|§ Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany.
authors.forename: Vladimir G
authors.initials: VG
authors.lastname: Spokoiny
authors.affiliation: * A. A. Kharkevich Institute for Information Transmission Problems, RAS, Moscow, Russia.|¶ Department of Bioengineering and Bioinformatics, M.V. Lomonosov Moscow State University, Moscow, Russia.
authors.forename: Mikhail S
authors.initials: MS
authors.lastname: Gelfand
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004926:Escherichia coli / Q000235:genetics*; D005821:Genetic Techniques; D016680:Genome, Bacterial; D059765:Homologous Recombination
keywords: AWS; Detection of homologous recombination; Escherichia coli; change-point detector