Variation Interpretation Predictors: Principles, Types, Performance, and Choice.

Area of Science:

• Genomics and Bioinformatics
• Computational Biology
• Medical Genetics

Background:

• Next-generation sequencing (NGS) rapidly generates vast amounts of genetic variation data.
• Interpreting the functional significance of identified variants is a major bottleneck for clinical applications.
• Experimental validation of variants is often impractical, necessitating computational approaches.

Purpose of the Study:

• To review computational prediction methods for interpreting genetic variants.
• To discuss available variation databases and their utility.
• To provide guidelines for selecting appropriate variant interpretation tools and outline future directions.

Main Methods:

• Categorization of computational predictors into generic tolerance (pathogenicity), gene/protein/disease-specific, and mechanism/effect-specific tools.
• Review of existing variation databases and prediction algorithms.
• Discussion of performance assessment metrics and summary of evaluation studies.

Main Results:

• Computational methods are essential for addressing the variant interpretation bottleneck.
• Diverse prediction tools exist, targeting different aspects of variant significance.
• Performance assessment frameworks are critical for evaluating the reliability of these methods.

Conclusions:

• Computational tools are indispensable for leveraging NGS data in disease diagnosis and precision medicine.
• Understanding the principles and applications of various predictors aids in selecting optimal tools.
• The field is advancing towards more accurate and efficient variant interpretation strategies.