The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies
Open accessAbstract
) constitutes a comprehensive collection of published germline mutations in nuclear genes that underlie, or are closely associated with human inherited disease. At the time of writing (March 2017), the database contained in excess of 203,000 different gene lesions identified in over 8000 genes manually curated from over 2600 journals. With new mutation entries currently accumulating at a rate exceeding 17,000 per annum, HGMD represents de facto the central unified gene/disease-oriented repository of heritable mutations causing human genetic disease used worldwide by researchers, clinicians, diagnostic laboratories and genetic counsellors, and is an essential tool for the annotation of next-generation sequencing data. The public version of HGMD ( http://www.hgmd.org ) is freely available to registered users from academic institutions and non-profit organisations whilst the subscription version (HGMD Professional) is available to academic, clinical and commercial users under license via QIAGEN Inc.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Self-citationAnalysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587
Self-citationClinVar: public archive of interpretations of clinically relevant variants
Landrum, M. J., Lee, J. M., Benson, M., Brown, G., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Hoover, J., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1222 · Cited by 2,758
Evolutionary and biomedical insights from the rhesus macaque genome.
Gibbs, R. A., Rogers, J., Katze, M. G., Bumgarner, R., Weinstock, G. M., Mardis, E. R., Remington, K. A., Strausberg, R. L., Venter, J. C., Wilson, R. K., et al. (2007) · Science · doi:10.1126/science.1139247 · Cited by 1,403
Self-citationCarrier testing for severe childhood recessive diseases by next-generation sequencing
Bell, C. J., Dinwiddie, D. L., Miller, N. A., Hateley, S. L., Ganusova, E. E., Mudge, J., Langley, R. J., Zhang, L., Lee, C. C., Schilkey, F. D., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3001756 · Cited by 706 · cited 1×
“In a clinical setting, HGMD is widely utilised by many groups in ongoing NGS diagnostic (Bell et al. 2011; John-ston et al. 2012; Calvo et al. 2012; Makrythanasis et al. 2014; Karageorgos et al. 2015; Wilfert et al. 2016; Walsh et al. 2017) and human genome sequencing (Tong et al. 2010; Kim et al.…”
Mountain gorilla genomes reveal the impact of long-term population decline and inbreeding
Xue, Y., Prado-Martinez, J., Sudmant, P. H., Narasimhan, V., Ayub, Q., Szpak, M., Frandsen, P., Chen, Y., Yngvadottir, B., Cooper, D. N., et al. (2015) · Science · doi:10.1126/science.aaa3952 · Cited by 473
Self-citationA highly annotated whole-genome sequence of a Korean individual
Kim, J.-I., Ju, Y. S., Park, H., Kim, S., Lee, S., Yi, J.-H., Mudge, J., Miller, N. A., Hong, D., Bell, C. J., et al. (2009) · Nature · doi:10.1038/nature08211 · Cited by 330
Effect of predicted protein-truncating genetic variants on the human transcriptome
Rivas, M. A., Pirinen, M., Conrad, D. F., Lek, M., Tsang, E. K., Karczewski, K. J., Maller, J. B., Kukurba, K. R., DeLuca, D. S., Fromer, M., et al. (2015) · Science · doi:10.1126/science.1261877 · Cited by 313
Self-citationGuanine holes are prominent targets for mutation in cancer and inherited disease
Bacolla, A., Temiz, N. A., Yi, M., Ivanic, J., Cer, R. Z., Donohue, D. E., Ball, E. V., Mudunuri, U. S., Wang, G., Jain, A., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003816 · Cited by 40
Self-citation
Cited by
Inferring the molecular and phenotypic impact of amino acid variants with MutPred2
Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K. A., Lin, G. N., Nam, H.-J., Mort, M., Cooper, D. N., Sebat, J., Iakoucheva, L. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19669-x · Cited by 923
Self-citationThe Polygenic and Monogenic Basis of Blood Traits and Diseases
Vuckovic, D., Bao, E. L., Akbari, P., Lareau, C. A., Mousas, A., Jiang, T., Chen, M.-H., Raffield, L. M., Tardaguila, M., Huffman, J. E., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.008 · Cited by 671
Targeted Disruption of HLA Genes via CRISPR-Cas9 Generates iPSCs with Enhanced Immune Compatibility
Xu, H., Wang, B., Ono, M., Kagita, A., Fujii, K., Sasakawa, N., Ueda, T., Gee, P., Nishikawa, M., Nomura, M., et al. (2019) · Cell stem cell · doi:10.1016/j.stem.2019.02.005 · Cited by 581
Doi, D., Magotani, H., Kikuchi, T., Ikeda, M., Hiramatsu, S., Yoshida, K., Amano, N., Nomura, M., Umekage, M., Morizane, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17165-w · Cited by 350
Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer
Petralia, F., Tignor, N., Reva, B., Koptyra, M., Chowdhury, S., Rykunov, D., Krek, A., Ma, W., Zhu, Y., Ji, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.044 · Cited by 317
Metadata
- Authors
- Stenson, P. D., Mort, M., Ball, E. V., Evans, K., Hayden, M., Heywood, S., Hussain, M., Phillips, A. D., & Cooper, D. N.
- Venue
- Human Genetics
- Published
- 2017-03-27
- Volume
- 136
- Issue
- 6
- Pages
- 665-677
- ISSN
- 0018-7348 (ISSN-L)
- 0340-6717
- 1432-1203
- OpenAlex
- W2604078450
- Cited by
- 1,345
- References
- 9
- FWCI
- 104.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
