Background: Functional annotation of bacterial genomes is an obligatory and crucially important step of information processing from the genome sequences into cellular mechanisms. However, there is a lack of computational methods to evaluate the quality of functional assignments. Results: We developed a genome-scale model that assigns Bayesian probability to each gene utilizing a known property of functional similarity between neighboring genes in bacteria. Conclusions: Our model clearly distinguished true annotation from random annotation with Bayesian annotation probability >0.95. Our model will provide a useful guide to quantitatively evaluate functional annotation methods and to detect gene sets with reliable annotations.
CITATION STYLE
Jun, S. R., Nookaew, I., Hauser, L., & Gorin, A. (2017). Assessment of genome annotation using gene function similarity within the gene neighborhood. BMC Bioinformatics, 18(1). https://doi.org/10.1186/s12859-017-1761-2
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