Traditional approaches to integrate protein data generally involved keyword searches, which immediately excludes unannotated or poorly annotated data. An alternative protein annotation approach is to rely on sequence identity, or structural similarity, or functional identification. Some proteins have high degree of sequence identity, or structural similarity, or similarity in functions that are unique to members of that family alone. Consequently, this approach can’t be generalized to integrate the protein data. Clearly, these traditional approaches have limitations in capturing and integrating data for Protein Annotation. For these reasons, we have adopted an alternative method that does not rely on keywords or similarity metrics, but instead uses ontology. In this chapter we discuss conceptual framework of Protein Ontology that has a hierarchical classification of concepts represented as classes, from general to specific; a list of attributes related to each concept, for each class; a set of relations between classes to link concepts in ontology in more complicated ways then implied by the hierarchy, to promote reuse of concepts in the ontology; and a set of algebraic operators for querying protein ontology instances.
Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database
