Orthology inference in nonmodel organisms using transcriptomes and low-coverage genomes: improving accuracy and...

Area of Science:

• Evolutionary Biology
• Bioinformatics
• Genomics

Background:

• Orthology inference is critical for phylogenomics but challenged by incomplete and error-prone data.
• Existing heuristics often fail with transcriptomes and low-coverage genomes, violating underlying assumptions.

Purpose of the Study:

• To develop and evaluate a new procedure for homology and orthology assignment using phylogenies.
• To improve the accuracy and completeness of ortholog inference in phylogenomic datasets.

Main Methods:

• A procedure using similarity scores to infer homologs, followed by alignment, phylogeny construction, and pruning of spurious branches.
• Exploration of four tree-based orthology inference approaches, including two novel methods, accommodating gene duplications and discordance.
• Application of serial jackknife analyses to evaluate phylogenetic signal conflicts.

Main Results:

• The phylogenomic procedure significantly enhanced the completeness and accuracy of inferred homologs and orthologs.
• More recently diverged datasets and those with higher-coverage genomes yielded more complete ortholog sets.
• The methods demonstrated scalability to over 100 taxa.

Conclusions:

• The developed tree-based phylogenomic approach offers a robust solution for orthology inference with challenging datasets.
• This method improves the reliability of evolutionary analyses by providing more accurate ortholog assignments.
• The implemented Python scripts are modular and adaptable for integration into existing bioinformatics pipelines.