Publications & Reports

An accurate method for identifying recent recombinants from unaligned sequences.

Feng Q, Tiedje KE, Ruybal-Pesántez S, Tonkin-Hill G, Duffy MF, Day KP, Shim H, Chan YB
Melbourne Integrative Genomics/School of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, 3010, Australia.

Abstract

MOTIVATION: Recombination is a fundamental process in molecular evolution, and the identification of recombinant sequences is thus of major interest. However, current methods for detecting recombinants are primarily designed for aligned sequences. Thus they struggle with analyses of highly diverse genes, such as the var genes of the malaria parasite Plasmodium falciparum, which are known to diversify primarily through recombination. RESULTS: We introduce an algorithm to detect recent recombinant sequences from a dataset without a full multiple alignment. Our algorithm can handle thousands of gene-length sequences without the need for a reference panel. We demonstrate the accuracy of our algorithm through extensive numerical simulations; in particular, it maintains its effectiveness in the presence of insertions and deletions. We apply our algorithm to a dataset of 17,335 DBLalpha types in var genes from Ghana, observing that sequences belonging to the same ups group or domain subclass recombine amongst themselves more frequently, and that non-recombinant DBLalpha types are more conserved than recombinant ones. AVAILABILITY: Source code is freely available at https://github.com/qianfeng2/detREC_program. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Link to publisher’s web site

Publication

  • Journal: Bioinformatics
  • Published: 13/01/2022
  • Volume: 38
  • Issue: 7
  • Pagination: 1823-1829