Detecting lateral genetic material transfer

TL;DR

This paper introduces novel DNA sequence-based features and a neural classifier to detect lateral gene transfer events, including between closely related bacteria, without relying on protein coding characteristics.

Contribution

It proposes new DNA traits based on thermodynamic and physico-chemical properties for detecting lateral gene transfer, expanding detection capabilities beyond traditional methods.

Findings

Effective detection of lateral gene transfer between closely related bacteria

DNA traits based on primary sequence can distinguish different organisms' genomes

Neural classifier achieves high resolution in identifying gene transfer events

Abstract

The bioinformatical methods to detect lateral gene transfer events are mainly based on functional coding DNA characteristics. In this paper, we propose the use of DNA traits not depending on protein coding requirements. We introduce several semilocal variables that depend on DNA primary sequence and that reflect thermodynamic as well as physico-chemical magnitudes that are able to tell apart the genome of different organisms. After combining these variables in a neural classificator, we obtain results whose power of resolution go as far as to detect the exchange of genomic material between bacteria that are phylogenetically close.