Understanding how genes function is a major challenge in biology, especially when a gene lacks prior research. Traditional methods of gene expression analysis focus on entire gene sets, identifying which biological pathways are enriched. However, this approach isn’t ideal when researchers want to study a single gene in detail.

To solve this problem, researchers from Goethe University Frankfurt developed GeneCOCOA, a computational tool that helps scientists explore the function of a single gene-of-interest (GOI) by analyzing its co-expression patterns with known biological pathways. This method uses RNA sequencing (RNA-Seq) data to compare how strongly the gene is expressed alongside curated gene sets. By applying statistical analysis, GeneCOCOA ranks the most relevant functional pathways related to the gene.

GeneCOCOA workflow for identification of functional gene sets co-expressed with a gene-of-interest

(A) Strategies and related methods for statistically associating genes to putative functions, summarized into gene-centric (GeneWalk, DAVID), prior knowledge (GO, Reactome, MSigDB) and co-expression (WGCNA, CemiTool) approaches. GeneCOCOA incorporates elements of each of these approaches into a single workflow. (B) Schematic representation of the GeneCOCOA workflow, which takes as input user-provided functional gene sets, a gene-of-interest (GOI) and gene expression data to report statistically ranked gene sets associated with the provided GOI. This is achieved by comparing root-mean-square error (RMSE) values from bootstrapped linear regression models predicting the expression of the GOI using either genes arising from a single gene set, or randomly sampled genes from the expression data. Gene set errors and random errors are statistically compared, and the resulting p values are adjusted, resulting in an output list of functional gene sets ranked statistically by the strength of their association with the provided gene-of-interest.

One of the key advantages of GeneCOCOA is its ability to uncover experiment-specific gene functions. This means scientists can identify not only what a gene might do in general but also how its role might change under different conditions. The tool also includes a differential mode, allowing researchers to compare how a gene’s function varies between experimental setups.

GeneCOCOA has already demonstrated strong performance in identifying known gene-disease associations, outperforming similar methods. With this tool, scientists can gain new insights into gene regulation and function, particularly for genes that haven’t been well studied.

Availability – GeneCOCOA is formulated as an R package for ease-of-use, available at https://github.com/si-ze/geneCOCOA.