LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC

Area of Science:

• Genomics
• Bioinformatics
• Medical Informatics

Background:

• Genomic variant identification is crucial for genetic disease diagnosis and research.
• Existing databases often have incomplete or outdated variant information due to literature volume and curation costs.
• Genetic variants have multiple naming conventions, hindering comprehensive literature searches.

Purpose of the Study:

• To develop a tool for searching and retrieving standardized genomic variant information.
• To address the challenge of incomplete and outdated curated variant data.
• To improve the discoverability of published variant research.

Main Methods:

• Developed LitVar, a system for searching and retrieving standardized variant information.
• Employed advanced text mining techniques for entity and relationship extraction.
• Integrated data for comprehensive variant analysis.

Main Results:

• LitVar provides standardized variant information, overcoming naming inconsistencies.
• Advanced text mining extracts relationships between variants, diseases, and chemicals/drugs.
• The system facilitates access to up-to-date published variant research.

Conclusions:

• LitVar improves the efficiency of finding relevant genomic variant literature.
• The tool aids scientists, healthcare professionals, and curators in their research.
• LitVar supports more comprehensive understanding of genetic variants and their associations.