EdgeHOG: a method for fine-grained ancestral gene order inference at large scale.

Area of Science:

• Evolutionary biology
• Genomics
• Bioinformatics

Background:

• Ancestral genomes are crucial for understanding life's diversification.
• Existing methods for inferring ancestral gene order lack scalability.
• Limited depth of gene neighborhood evolution tracing hinders evolutionary studies.

Purpose of the Study:

• Introduce edgeHOG, a scalable tool for accurate ancestral gene order inference.
• Enable tracing gene neighborhood evolution further back in time.
• Analyze gene order evolution across all domains of life.

Main Methods:

• Developed edgeHOG with linear time complexity for scalability.
• Validated edgeHOG on diverse benchmarks.
• Applied edgeHOG to the OMA orthology database (2,845 genomes).

Main Results:

• Reconstructed gene order for 1,133 ancestral genomes.
• Inferred ancestral gene order for the last eukaryotic common ancestor (~1.8 billion years ago).
• Observed significant functional association among neighboring genes and dated gene adjacencies.

Conclusions:

• EdgeHOG provides accurate and scalable ancestral gene order inference.
• The tool facilitates the study of gene neighborhood evolution over deep evolutionary timescales.
• EdgeHOG aids in detecting conserved gene clusters and chromosomal rearrangements.