Detecting Horizontal Gene Transfer between Closely Related Taxa.

Area of Science:

• Genetics
• Evolutionary Biology
• Bioinformatics

Background:

• Horizontal gene transfer (HGT) drives genetic innovation and genome evolution.
• Detecting HGT in closely related species is difficult due to weak phylogenetic signals and similar nucleotide composition.
• Identifying HGT is critical in clinical microbiology for tracking virulent and drug-resistant strains.

Purpose of the Study:

• To develop a novel, self-contained technique for detecting HGT between closely related strains.
• To identify horizontally acquired genes by measuring genomic divergence and sequence mutability.

Main Methods:

• Developed the Near HGT technique utilizing a synteny index to assess gene divergence from its native environment.
• Confirmed candidate transferred genes using constant relative mutability (CRM) to score unusual sequence divergence.
• Applied the method to E. coli strains and compared it with existing HGT detection tools.

Main Results:

• Successfully detected several highly probable horizontally acquired genes in E. coli strains.
• The Near HGT method, when combined with other approaches, provides a more comprehensive view of newly acquired genes.
• Demonstrated the ability to identify HGT events even with weak phylogenetic signals.

Conclusions:

• The Near HGT algorithm effectively identifies horizontally acquired genes between closely related strains.
• This novel approach enhances the detection of HGT, particularly in challenging cases.
• The method contributes to a better understanding of microbial evolution and the emergence of new strains.