GROOLS: reactive graph reasoning for genome annotation through biological processes

BMC Bioinformatics

Adrien Josso¹  Jonathan Mercier¹  Claudine Médigue¹  David Vallenet¹ 

[1] LABGeM, Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Université d’Evry, Université Paris-Saclay;

关键词: Genome annotation;    Curation;    Metabolic pathways;    Knowledge representation;    Paraconsistent logic;    Expert system;   

DOI  :  10.1186/s12859-018-2126-1

来源: DOAJ

【 摘 要 】

Abstract Background High quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation. Results We developed an expert system using paraconsistent logic, named GROOLS (Genomic Rule Object-Oriented Logic System), that evaluates the completeness and the consistency of predicted functions through biological processes like metabolic pathways. Using a generic and hierarchical representation of knowledge, biological processes are modeled in a graph from which observations (i.e. predictions and expectations) are propagated by rules. At the end of the reasoning, conclusions are assigned to biological process components and highlight uncertainties and inconsistencies. Results on 14 microbial organisms are presented. Conclusions GROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It assists biocurators in the functional annotation of proteins by focusing on missing or contradictory observations.

【 授权许可】

Unknown