CrowdGO: machine learning and semantic similarity guided consensus Gene Ontology annotation

Abstract Characterising gene function for the ever-increasing number and diversity of species with annotated genomes relies almost entirely on computational prediction methods. These software are also numerous and diverse, each with different strengths and weaknesses as revealed through community benchmarking efforts. Meta-predictors that assess consensus and conflict from individual algorithms should deliver enhanced functional annotations. We present CrowdGO, a consensus-based Gene Ontology (GO) term meta-predictor that employs machine learning models with GO term semantic similarities and information contents. Applying CrowdGO to results from a deep learning-based, a sequence similarity-based, and two protein domain-based methods, delivers consensus annotations with improved precision and recall. Furthermore, using standard evaluation measures CrowdGO performance matches that of the community’s best performing methods. CrowdGO therefore offers a model-informed approach to leverage strengths of individual predictors and produce comprehensive and accurate gene functional annotations. CrowdGO is implemented in Python3, and is freely available from https://gitlab.com/mreijnders/CrowdGO, along with a Snakemake workflow and pre-trained models. Contactmaarten.reijnders@unil.ch (MJMFR), robert.waterhouse@unil.ch (RMW).