Exploring lateral genetic transfer among microbial genomes using TF-IDF.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Microbes acquire genetic material via lateral genetic transfer (LGT).
• Existing computational methods for LGT detection have limitations in sensitivity, donor-recipient distinction, and scalability.
• A new alignment-free method, TF-IDF, was developed to address these challenges.

Purpose of the Study:

• To evaluate the performance of the TF-IDF method on diverse microbial genomic datasets.
• To investigate the impact of parameters like k-mer size and gap size on LGT inference.
• To explore the mosaic nature of lateral genes, including their sources and evolutionary history.

Main Methods:

• Utilized an alignment-free approach based on Term Frequency-Inverse Document Frequency (TF-IDF).
• Applied the TF-IDF method to three empirical datasets: Escherichia coli/Shigella, enteric bacteria, and a broad range of bacterial/archaeal phyla.
• Analyzed the influence of k-mer size, gap size, and group delineation on LGT region identification.

Main Results:

• TF-IDF demonstrated effectiveness across different datasets and microbial groups.
• Identified an interplay between parameter choices, sequence divergence, and LGT inference.
• Enabled the exploration of lateral gene mosaicism, including multiple origins and transfer events.

Conclusions:

• The TF-IDF method offers a sensitive, scalable, and informative approach for detecting LGT.
• This method provides novel insights into the complexity and evolutionary dynamics of lateral gene acquisition in microbes.
• Gene Ontology enrichment analysis revealed biological processes potentially impacted by LGT.