Detecting lateral genetic material transfer
This work addresses a bioinformatics challenge for researchers studying bacterial evolution, but it is incremental as it builds on existing detection methods.
The paper tackled the problem of detecting lateral gene transfer events by using DNA traits independent of protein coding, achieving the ability to detect genomic material exchange between phylogenetically close bacteria.
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.