Inferring unknown biological function by integration of GO annotations and gene expression data

TL;DR

This paper introduces a novel computational method that infers biological functions of unknown genes by clustering them with well-annotated genes based on gene expression data and GO annotations, aiding biologists in gene function discovery.

Contribution

The paper presents a new clustering-based approach that integrates GO annotations and gene expression data to infer functions of uncharacterized genes, advancing computational gene annotation methods.

Findings

Effective clustering of unknown and known genes based on expression data.

Potential to guide biological experiments in gene function discovery.

Web interface available for practical application.

Abstract

Characterizing genes with semantic information is an important process regarding the description of gene products. In spite that complete genomes of many organisms have been already sequenced, the biological functions of all of their genes are still unknown. Since experimentally studying the functions of those genes, one by one, would be unfeasible, new computational methods for gene functions inference are needed. We present here a novel computational approach for inferring biological function for a set of genes with previously unknown function, given a set of genes with well-known information. This approach is based on the premise that genes with similar behaviour should be grouped together. This is known as the guilt-by-association principle. Thus, it is possible to take advantage of clustering techniques to obtain groups of unknown genes that are co-clustered with genes that have well-known semantic information (GO annotations). Meaningful knowledge to infer unknown semantic information can therefore be provided by these well-known genes. We provide a method to explore the potential function of new genes according to those currently annotated. The results obtained indicate that the proposed approach could be a useful and effective tool when used by biologists to guide the inference of biological functions for recently discovered genes. Our work sets an important landmark in the field of identifying unknown gene functions through clustering, using an external source of biological input. A simple web interface to this proposal can be found at http://fich.unl.edu.ar/sinc/webdemo/gamma-am/.