Semantic annotation modelling for protein functions prediction

Abstract

Functional protein annotation is a key phase in the analysis of de-novo sequenced genomes. Often the automatic annotation tools are insensitive to removing wrong annotations associated with contradictions and non-compliance in biological terms. In this study, we introduce a semantic model for representation of functional annotations based on a resource description framework standard (RDF). We have integrated several databases with information for protein sequences and ontologies describing the functional relationships of the protein molecules. By using Web Ontology Language (OWL) axioms, RDF storage engines are able to take decisions which candidate annotations should be marked as biologically unviable and do not withstand the reality checks associated with coexistence, subcellular location and species affiliation [1]. This approach reduces the number of false positives and time spent in machine annotation’s curation process. The presented semantic data model is designed to combine the semantic representation of annotations with examples designed for machine learning. Current work is part of a large scale project of functional annotation of plant genomes.