Reticulate classification of mosaic microbial genomes using NeAT website.

Area of Science:

• Computational Biology
• Evolutionary Biology
• Bioinformatics

Background:

• The classical 'tree of life' model effectively represents vertical descent (mutation) but fails to incorporate lateral gene transfer (LGT).
• LGT is a significant factor in the evolution of many organisms, particularly prokaryotes and mobile genetic elements, making traditional trees misleading.
• Representing complex evolutionary histories requires methods that can accommodate both vertical and horizontal gene flow.

Purpose of the Study:

• To develop and describe a novel computational method for representing evolutionary relationships that includes both vertical descent and lateral gene transfer.
• To provide a classification system for genomes that accounts for shared genetic material through LGT, moving beyond simple tree structures.
• To offer a practical tool for analyzing genomic data incorporating complex evolutionary events.

Main Methods:

• Grouped coded proteins from a set of genomes into families based on sequence similarity.
• Compared all genome pairs, quantifying shared proteins within the same families to derive a weighted graph.
• Applied a two-step graph clustering algorithm to classify genomes, allowing nodes (genomes) to belong to multiple clusters.

Main Results:

• Successfully generated a weighted graph representing genomic similarity, highlighting connections due to both vertical inheritance and LGT.
• The two-step clustering approach enabled a multi-faceted classification of genomes, reflecting their complex evolutionary interdependencies.
• The described procedure is implementable using the Network Analysis Tools (NeAT) website.

Conclusions:

• Network-based representations are more suitable than traditional trees for visualizing evolutionary histories involving significant LGT.
• The developed graph and clustering method provides a robust framework for classifying genomes with complex evolutionary patterns.
• This approach enhances our understanding of genome evolution by integrating diverse genetic exchange mechanisms.