AvP: A software package for automatic phylogenetic detection of candidate horizontal gene transfers

PLoS Comput Biol. 2022 Nov 9;18(11):e1010686. doi: 10.1371/journal.pcbi.1010686. eCollection 2022 Nov.

Abstract

Horizontal gene transfer (HGT) is the transfer of genes between species outside the transmission from parent to offspring. Due to their impact on the genome and biology of various species, HGTs have gained broader attention, but high-throughput methods to robustly identify them are lacking. One rapid method to identify HGT candidates is to calculate the difference in similarity between the most similar gene in closely related species and the most similar gene in distantly related species. Although metrics on similarity associated with taxonomic information can rapidly detect putative HGTs, these methods are hampered by false positives that are difficult to track. Furthermore, they do not inform on the evolutionary trajectory and events such as duplications. Hence, phylogenetic analysis is necessary to confirm HGT candidates and provide a more comprehensive view of their origin and evolutionary history. However, phylogenetic reconstruction requires several time-consuming manual steps to retrieve the homologous sequences, produce a multiple alignment, construct the phylogeny and analyze the topology to assess whether it supports the HGT hypothesis. Here, we present AvP which automatically performs all these steps and detects candidate HGTs within a phylogenetic framework.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biological Evolution*
  • Evolution, Molecular
  • Gene Transfer, Horizontal* / genetics
  • Genome
  • Phylogeny
  • Software

Grants and funding

This work has been supported by the French government, through the UCA-JEDI “Investments in the Future” project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-01. GDK has received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills+ fellowship (under grant number 609398).