BALL-SNP: combining genetic and structural information to identify candidate non-synonymous single nucleotide...

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-throughput genetic testing, particularly Next-Generation Sequencing (NGS), is increasingly used in clinical settings.
• Accurate interpretation of genetic variants, individually or in combination, is critical for diagnosis and therapy selection.
• Analyzing NGS data and understanding variant pathogenicity remain significant challenges in clinical genetics.

Purpose of the Study:

• To develop a software tool, BALL-SNP, that facilitates the interpretation of genetic variants.
• To aid in the selection of candidate non-synonymous polymorphisms (nsSNPs) for further clinical investigation.
• To integrate genetic variant data with protein structure visualization and pathogenicity prediction.

Main Methods:

• BALL-SNP processes genetic variants from VCF or tabular files.
• It automatically visualizes protein 3D structures (from PDB) and highlights mutated residues.
• The tool performs hierarchical clustering of nsSNPs within 3D structures and integrates external databases and prediction tools.

Main Results:

• BALL-SNP integrates data from resources like ClinVar, HUMSAVAR, and in silico prediction tools (e.g., I-Mutant2.0).
• It provides predictions of binding pockets and presents variant information in an interpretable table.
• Application to cardiomyopathies revealed mutation accumulations in JUP, VCL, and SMYD2 genes.

Conclusions:

• Software solutions are essential for analyzing high-throughput genomics data to support clinical decisions.
• BALL-SNP offers a freely available, interactive graphical representation of genetic variants within protein structures.
• The tool aids in discovering mutation proximity to functional sites and potential collective effects of mutations.