Constructing a database for the relations between CNV and human genetic diseases via systematic text mining.

Area of Science:

• Genomics
• Bioinformatics
• Medical Genetics

Background:

• Copy number variations (CNVs) are clinically significant in genetic testing.
• Existing databases aid CNV interpretation, but lack detailed literature context for rare variants.
• Clinical geneticists require original literature context for comprehensive CNV analysis.

Purpose of the Study:

• To construct a comprehensive database of CNV-disease relationships using systematic text mining.
• To develop a user-friendly front-end for analyzing CNV/disease associations.
• To provide accessible literature context for rare CNVs.

Main Methods:

• Systematic text mining approach to identify and extract CNV-disease correlations from literature.
• Annotation of CNV and disease mentions within extracted sentences.
• Development of a database (CNVdigest) to store indexed sentences.

Main Results:

• CNVdigest contains 440,485 sentences detailing CNV-disease relationships.
• The database encompasses 1582 unique CNVs and 2425 distinct diseases.
• Sentences are indexed with annotated CNV and disease mentions for efficient retrieval.

Conclusions:

• A novel text mining method successfully constructed a CNV-disease relationship database.
• A user-friendly web interface facilitates the analysis of CNV/disease associations.
• The CNVdigest system is publicly available for clinical and research use.