3DVizSNP: a tool for rapidly visualizing missense mutations identified in high throughput experiments in iCn3D

• Published in: BMC bioinformatics Vol. 24; no. 1; pp. 244 - 9
• Main Authors: Sierk, Michael, Ratnayake, Shashikala, Wagle, Manoj M., Chen, Ben, Park, Brian, Wang, Jiyao, Youkharibache, Philippe, Meerzaman, Daoud
• Format: Journal Article
• Language: English
• Published: London BioMed Central 09.06.2023; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Amino acids; Annotations; Bioinformatics; Biomedical and Life Sciences; Cancer; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Confidence; Experiments; Gene mutations; Genetic research; Genomic variation; Life Sciences; Methods; Microarrays; Missense mutation; Mutation; Mutation prioritization; Nucleotides; Phenotype impact; Protein structure; Proteins; Python (Programming language); Single nucleotide polymorphisms; Single-nucleotide polymorphism; Software; Structural analysis; Structural bioinformatics; Visualization; Visualization (Computers); Web applications; United States; Structural bioinformatics; Mutation prioritization; Single nucleotide polymorphisms; Genomic variation; Protein structure; Phenotype impact
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-023-05370-5

Background High throughput experiments in cancer and other areas of genomic research identify large numbers of sequence variants that need to be evaluated for phenotypic impact. While many tools exist to score the likely impact of single nucleotide polymorphisms (SNPs) based on sequence alone, the three-dimensional structural environment is essential for understanding the biological impact of a nonsynonymous mutation. Results We present a program, 3DVizSNP, that enables the rapid visualization of nonsynonymous missense mutations extracted from a variant caller format file using the web-based iCn3D visualization platform. The program, written in Python, leverages REST APIs and can be run locally without installing any other software or databases, or from a webserver hosted by the National Cancer Institute. It automatically selects the appropriate experimental structure from the Protein Data Bank, if available, or the predicted structure from the AlphaFold database, enabling users to rapidly screen SNPs based on their local structural environment. 3DVizSNP leverages iCn3D annotations and its structural analysis functions to assess changes in structural contacts associated with mutations. Conclusions This tool enables researchers to efficiently make use of 3D structural information to prioritize mutations for further computational and experimental impact assessment. The program is available as a webserver at https://analysistools.cancer.gov/3dvizsnp or as a standalone python program at https://github.com/CBIIT-CGBB/3DVizSNP .