Scalable phylogenetic profiling using MinHash uncovers likely eukaryotic sexual reproduction genes

Area of Science:

• Computational biology
• Bioinformatics
• Genomics

Background:

• Phylogenetic profiling predicts genes in the same biological process by analyzing protein family loss/retention across species.
• Traditional methods are computationally intensive, especially for large eukaryotic genomes, limiting their scalability.
• The increasing availability of diverse eukaryotic genomes necessitates faster, more scalable profiling approaches.

Purpose of the Study:

• To introduce HogProf, a fast and scalable phylogenetic profiling method.
• To enable large-scale phylogenetic profiling across all domains of life.
• To reconstruct biological networks and identify novel gene/protein interactions.

Main Methods:

• Leverages hierarchical orthologous groups for constructing large profiles.
• Employs locality-sensitive hashing for efficient retrieval of similar profiles.
• Compares performance against existing phylogeny-based methods like Enhanced Phylogenetic Tree.

Main Results:

• HogProf demonstrates superior performance compared to Enhanced Phylogenetic Tree.
• Successfully reconstructs biological networks, including the kinetochore complex.
• Identifies conserved proteins involved in sexual reproduction (Hap2, Spo11, Gex1).

Conclusions:

• HogProf provides a scalable solution for phylogenetic profiling, overcoming limitations of previous methods.
• Facilitates large-scale prediction of biological pathways in the rapidly growing number of available eukaryotic genomes.
• The tool is poised to significantly advance our understanding of gene function and biological processes across life.