The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting
Top Cited Papers
Open Access
- 28 June 2020
- journal article
- review article
- Published by Springer Science and Business Media LLC in Human Genetics
- Vol. 139 (10), 1197-1207
- https://doi.org/10.1007/s00439-020-02199-3
Abstract
The Human Gene Mutation Database (HGMD®) constitutes a comprehensive collection of published germline mutations in nuclear genes that are thought to underlie, or are closely associated with human inherited disease. At the time of writing (June 2020), the database contains in excess of 289,000 different gene lesions identified in over 11,100 genes manually curated from 72,987 articles published in over 3100 peer-reviewed journals. There are primarily two main groups of users who utilise HGMD on a regular basis; research scientists and clinical diagnosticians. This review aims to highlight how to make the most out of HGMD data in each setting.This publication has 40 references indexed in Scilit:
- Autism risk in offspring can be assessed through quantification of male sperm mosaicismNature Medicine, 2019
- AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literatureGenetics in Medicine, 2019
- OMIM.org: leveraging knowledge across phenotype–gene relationshipsNucleic Acids Research, 2018
- The phenotype is equally important in promoting variants from benign to pathogenic as well as in demoting variants from pathogenic to benignHeart Rhythm, 2018
- New insights into the generation and role of de novo mutations in health and diseaseGenome Biology, 2016
- A global reference for human genetic variationNature, 2015
- DECIPHER: database for the interpretation of phenotype-linked plausibly pathogenic sequence and copy-number variationNucleic Acids Research, 2013
- Genes, mutations, and human inherited disease at the dawn of the age of personalized genomicsHuman Mutation, 2010
- A method and server for predicting damaging missense mutationsNature Methods, 2010
- Random ForestsMachine Learning, 2001