exp2GO: Improving Prediction of Functions in the Gene Ontology With Expression Data

The computational methods for the prediction of gene function annotations aim to automatically find associations between a gene and a set of Gene Ontology (GO) terms describing its functions. Since the hand-made curation process of novel annotations and the corresponding wet experiments validations are very time-consuming and costly procedures, there is a need for computational tools that can reliably predict likely annotations and boost the discovery of new gene functions. This work proposes a novel method for predicting annotations based on the inference of GO similarities from expression similarities. The novel method was benchmarked against other methods on several public biological datasets, obtaining the best comparative results. exp2GO effectively improved the prediction of GO annotations in comparison to state-of-the-art methods. Furthermore, the proposal was validated with a full genome case where it was capable of predicting relevant and accurate biological functions. The repository of this project withh full data and code is available at https://github.com/sinc-lab/exp2GO.

Automated summary

This work proposes a novel method for predicting gene function annotations based on the inference of GO similarities from expression similarities. The proposed method outperforms other methods on several public biological datasets and effectively improves the prediction of GO annotations. It is also capable of predicting relevant and accurate biological functions in a full genome case.