Improving the accuracy, functionality, scalability and usability of orthology inference for biological research

Year of award: 2023


  • Prof Steven Kelly

    University of Oxford, United Kingdom

Project summary

Inferring the phylogenetic relationships between biological sequences (orthology inference) is fundamental to biological and biomedical research. It provides the framework for transferring biological knowledge between species and enables the use of model organisms for studying health and disease. However, orthology inference methods are not perfect. The best methods are only ~80% accurate on benchmark tests and also fail to make inferences for thousands of genes in a typical analysis. In addition to these limitations, orthology inference methods are poorly scalable and are unable to analyse the current (or future) quantities of genome data. Finally, the methods themselves are poorly accessible to researchers lacking expertise in command line environments.

This project aims to address each of these limitations and challenges by improving the accuracy, enhancing the functionality, increasing the scalability and extending the usability of OrthoFinder. By delivering these improvements, this project will have an impact on the accuracy and capability of thousands of studies that are underpinned by OrthoFinder. It will also improve the accuracy and utility of repositories of biological sequence data that rely on the method. Finally, it will improve access to high-level comparative genomics tools within the biological and biomedical sciences.