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Related Experiment Video

Updated: Jan 22, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

VIPdb, a genetic Variant Impact Predictor Database.

Zhiqiang Hu¹, Changhua Yu^1,2, Mabel Furutsuki^1,3

¹Department of Plant and Microbial Biology, University of California, Berkeley, California.

|July 9, 2019

Summary

This summary is machine-generated.

Predicting genetic variant effects is crucial for understanding human genetics and advancing precision medicine. A new database, VIPdb, summarizes over 100 tools to aid researchers and clinicians in this task.

Keywords:

SNV phenotype SV impact VIPdb genotype-phenotype relationship variant impact variant impact prediction

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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Area of Science:

Human Genetics
Genomic Medicine
Bioinformatics

Background:

Genome sequencing yields numerous genetic variants.
Predicting the functional and clinical impact of these variants presents a significant challenge.
Understanding variant effects is key to advancing precision medicine.

Purpose of the Study:

To consolidate and characterize existing tools for predicting genetic variant impact.
To create a centralized resource (VIPdb) for researchers and clinicians.
To facilitate the selection of appropriate variant prediction tools and guide future method development.

Main Methods:

Systematic review and summarization of over one hundred genetic variant impact prediction tools.
Database curation of tool characteristics and functionalities.
Development of the Variant Impact Predictor Database (VIPdb).

Main Results:

A comprehensive summary of existing variant impact prediction tools is presented.
The Variant Impact Predictor Database (VIPdb) has been established.
VIPdb provides a browsable and downloadable resource for the scientific community.

Conclusions:

VIPdb serves as a valuable resource for navigating the landscape of genetic variant prediction tools.
The database aids researchers and clinicians in selecting appropriate tools for their needs.
VIPdb can inform the development of more effective variant impact prediction methods.