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

BackgroundCharacterising 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. ResultsTo exploit the benefits of meta-approaches, we developed CrowdGO, an open-source consensus-based Gene Ontology (GO) term meta-predictor that employs machine learning models with GO term semantic similarities and information contents. By re-evaluating each gene-term annotation, a consensus dataset is produced with high-scoring confident annotations and low-scoring rejected annotations. 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 communitys best performing individual methods. ConclusionCrowdGO offers a model-informed approach to leverage strengths of individual predictors and produce comprehensive and accurate gene functional annotations. Availability and ImplementationCrowdGO is implemented in Python3, and is freely available from https://gitlab.com/mreijnders/CrowdGO, with a Snakemake workflow and pre-trained models.