Improving Orthology Inference with OMA Algorithm
Author Information
Author(s): Alexander CJ Roth, Gaston H Gonnet, Christophe Dessimoz
Primary Institution: ETH Zurich, and Swiss Institute of Bioinformatics
Hypothesis
The OMA algorithm aims to improve orthology inference by using evolutionary distances and accounting for various factors affecting gene relationships.
Conclusion
The OMA algorithm provides several novel improvements for orthology inference and a unique dataset of large-scale orthology assignments.
Supporting Evidence
- The OMA project has analyzed 657 genomes, making it one of the largest orthology inference projects.
- The algorithm improves upon traditional methods by using evolutionary distances instead of alignment scores.
- It accounts for uncertainty in distance estimation and allows for many-to-many orthologous relationships.
Takeaway
The OMA project helps scientists find similar genes across different species by looking at their evolutionary history, making it easier to understand how they are related.
Methodology
The OMA algorithm processes complete genomes to identify orthologous genes through pairwise alignments, evolutionary distance calculations, and clustering into orthologous groups.
Limitations
The algorithm may struggle with cases of differential gene loss and lateral gene transfer, which can complicate orthology predictions.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website