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

Michael Sierk; Shashikala Ratnayake; Manoj M Wagle; Ben Chen; Brian Park; Jiyao Wang; Philippe Youkharibache; Daoud Meerzaman

doi:10.1186/s12859-023-05370-5

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

BMC Bioinformatics. 2023 Jun 9;24(1):244. doi: 10.1186/s12859-023-05370-5.

Authors

Michael Sierk¹, Shashikala Ratnayake², Manoj M Wagle^{3

4

5}, Ben Chen⁶, Brian Park⁶, Jiyao Wang⁷, Philippe Youkharibache⁸, Daoud Meerzaman²

Affiliations

¹ Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA. michael.sierk@nih.gov.
² Computational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA.
³ Faculty of Pharmacy, University of Grenoble Alpes, Grenoble, France.
⁴ Department of Bioinformatics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576104, India.
⁵ School of Mathematics and Statistics, Faculty of Science, and Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia.
⁶ Digital Services and Solutions Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA.
⁷ National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, 20894, USA.
⁸ Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA.

Abstract

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 .

Keywords: Genomic variation; Mutation prioritization; Phenotype impact; Protein structure; Single nucleotide polymorphisms; Structural bioinformatics.

MeSH terms

Computational Biology* / methods
Genomics / methods
Mutation
Mutation, Missense*
Software