Extraction and Grounding of Protein Mutations via Semantic Integration of Text and Sequence Information

Rich information on mutations and their impacts is scattered across scientific texts and literature. Reuse of mutation impact annotations requires grounding mutations to the correct positions on sequences extracted from protein databases as a critical step. This paper presents a generic method for grounding textual mentions of mutation entities to protein sequences, that is based on an OWL-DL ontology driven workflow that integrates text and sequence information in a semantically consistent way. Mutation mentions mined from texts are iteratively mapped onto candidate proteins, and an ontology mining algorithm facilitates their correct grounding to a protein sequence. Using a gold standard corpus of full text articles and corresponding protein sequences we show the proposed method is promising compared to existing approaches.