Screening for recombination in large data sets: sensitivity, specificity, and applications to coronaviruses

Abstract

Overthe past 35 years many statistical and bioinformatic tools have been developedto detect recombination, gene conversion, or horizontal gene transfer insequence data. Five separate statistical signals have been commonly used todetect recombinant sequences, and two of these - mosaic signals and phylogenetic incongruence signals - have emerged as thepreferred methods for generating evidence for recombination. I will review thederivation of a non-parametric mosaic statistic called Delta_mn2 that forms thebasis of the 3SEQ recombination detection algorithm. The sensitivity,specificity, and exact p-values reported by 3SEQ give it some advantages as ascreening tool for recombination in large data sets. I will show how to deriveclonal subsegments, or breakpoint-free regions (BFRs), using this approach. AndI will show how we have used this screening approach inidentifyingrecombinants in sarbecoviruses and SARS-CoV-2.

Biography

MaciejBoni‘s academic background is in mathematical epidemiology, mathematicalpopulation genetics, individual-based disease transmission models, fieldepidemiology, phylogenetics, and recombination. His main influenza focus is thedynamics and evolution of influenza virus in the tropics, which he hasaddressed with theoretical, field epidemiological, and informatic methods. Hismain work in malaria focuses on optimal methods of distributing antimalarialdrugs to minimize the risks and slow the spread of drug resistance. From 2008to 2016, Maciej’s research group was based in Ho Chi Minh City, at the OxfordUniversity Clinical Research Unit. In 2016, Maciej joined the BiologyDepartment and the Center for Infectious Disease Dynamics (CIDD) atPennsylvania State University.