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Hum Mutat. 2019 Sep;40(9):1202-1214. doi: 10.1002/humu.23858. Epub 2019 Aug 17.

VIPdb, a genetic Variant Impact Predictor Database.

Author information

1
Department of Plant and Microbial Biology, University of California, Berkeley, California.
2
Department of Bioengineering, University of California, Berkeley, California.
3
Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California.
4
Division of Data Sciences, University of California, Berkeley, California.

Abstract

Genome sequencing identifies vast number of genetic variants. Predicting these variants' molecular and clinical effects is one of the preeminent challenges in human genetics. Accurate prediction of the impact of genetic variants improves our understanding of how genetic information is conveyed to molecular and cellular functions, and is an essential step towards precision medicine. Over one hundred tools/resources have been developed specifically for this purpose. We summarize these tools as well as their characteristics, in the genetic Variant Impact Predictor Database (VIPdb). This database will help researchers and clinicians explore appropriate tools, and inform the development of improved methods. VIPdb can be browsed and downloaded at https://genomeinterpretation.org/vipdb.

KEYWORDS:

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

PMID:
31283070
DOI:
10.1002/humu.23858

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