Evidence map›Paper›PMID 37296383›Full record

ArticleBMC bioinformatics2023

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

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact, top 75% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 4 countries.

Michael SierkComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA. michael.sierk@nih.gov.
Shashikala RatnayakeComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA.
Manoj M WagleFaculty of Pharmacy, University of Grenoble Alpes, Grenoble, France.
Ben ChenDigital Services and Solutions Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA.
Brian ParkDigital Services and Solutions Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA.
Jiyao WangNational Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, 20894, USA.
Philippe YoukharibacheCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA.
Daoud MeerzamanComputational Genomics and Bioinformatics Branch, Center for Biomedical Informatics and Information Technology, National Cancer Institute, NIH, Rockville, MD, 20852, USA.
National Cancer Institute · USChildren's Medical Research Institute · AUNational Center for Biotechnology Information · US

Funding

National Biomedical Information ServicesZIHLM200888 · NLM · NATIONAL LIBRARY OF MEDICINE · PI PALTOO, DINA · 2009 to 2025
$5200.7M
Structural data science methods and software to study immunotherapeutic proteinsZIHBC012005 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI YOUKHARIBACHE, PHILIPPE · 2020 to 2024
$753k
6 · The paper itself

Abstract

backgroundHigh 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.

resultsWe 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.

conclusionsThis 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 .

Indexed as

Computational BiologyMutation, MissenseGenomicsMutationSoftwareGenomic variationMutation prioritizationPhenotype impactProtein structureSingle nucleotide polymorphismsStructural bioinformatics

Identifiers

PMID37296383
PMCPMC10251577
OpenAlexW4380081794

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.