Improving sequence variant descriptions in mutation databases and literature using the Mutalyzer sequence variation...

Area of Science:

• Genomics
• Bioinformatics
• Medical Genetics

Background:

• Accurate sequence variant descriptions are crucial for reliable clinical diagnosis.
• Errors in variant nomenclature can lead to significant diagnostic uncertainties.
• Existing locus-specific mutation databases (LSDBs) show variable adherence to nomenclature standards.

Purpose of the Study:

• To develop an automated tool, Mutation Analyzer (Mutalyzer), for checking and correcting sequence variant nomenclature.
• To ensure variant descriptions comply with Human Genome Variation Society (HGVS) guidelines.
• To assess the quality of variant descriptions in public mutation databases.

Main Methods:

• Mutalyzer utilizes reference sequences from any organism for automated analysis.
• It supports various variation types including substitutions, deletions, insertions, and splice-site changes.
• Input includes GenBank accession numbers or files, gene symbols, and variant data for batch processing.

Main Results:

• Mutalyzer generates descriptions at DNA, transcript, and protein levels.
• Analysis of over 11,000 variants revealed error rates of 13% (PAH), 75% (BRCA2), and 62% (HbVar) in existing databases.
• Low recognition rates in some databases were attributed to poor reference sequence annotation or noncompliance with guidelines.

Conclusions:

• Mutalyzer effectively curates newly discovered and existing sequence variation data when provided with annotated genomic reference sequences.
• The tool is essential for improving the quality and consistency of variant descriptions in LSDBs.
• Mutalyzer will be integrated with the Leiden Open source Variation Database (LOVD) and is part of a larger sequence variant effect prediction package.