OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders
Open accessAbstract
Online Mendelian Inheritance in Man, OMIM(®), is a comprehensive, authoritative and timely research resource of curated descriptions of human genes and phenotypes and the relationships between them. The new official website for OMIM, OMIM.org (http://omim.org), was launched in January 2011. OMIM is based on the published peer-reviewed biomedical literature and is used by overlapping and diverse communities of clinicians, molecular biologists and genome scientists, as well as by students and teachers of these disciplines. Genes and phenotypes are described in separate entries and are given unique, stable six-digit identifiers (MIM numbers). OMIM entries have a structured free-text format that provides the flexibility necessary to describe the complex and nuanced relationships between genes and genetic phenotypes in an efficient manner. OMIM also has a derivative table of genes and genetic phenotypes, the Morbid Map. OMIM.org has enhanced search capabilities such as genome coordinate searching and thesaurus-enhanced search term options. Phenotypic series have been created to facilitate viewing genetic heterogeneity of phenotypes. Clinical synopsis features are enhanced with UMLS, Human Phenotype Ontology and Elements of Morphology terms and image links. All OMIM data are available for FTP download and through an API. MIMmatch is a novel outreach feature to disseminate updates and encourage collaboration.
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For disease phenotypes in GeneTuring's gene disease association questions, we manually identified genes associated with that phenotype according to DisGeNet that are not associated according to OMIM.
“Specifically, for disease phenotypes in GeneTuring's gene disease association questions, we manually identified genes associated with that phenotype according to DisGeNet [42] that are not associated according to OMIM [1].”
From LAB-Bench: Measuring Capabilities of Language Models for Biology Research (2024) · cites this paper as [1]
XGBoost was utilized to identify novel genes associated with Alzheimer's disease.
“In this case, XGBoost, a form of extreme gradient boosting (67), was utilized to identify novel genes associated with Alzheimer's disease (68).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [67]
The 'Representation of Gene–Phenotype Relationships' section (page unavailable) describes how OMIM records and evaluates gene–phenotype associations, not an XGBoost analysis. Searches for XGBoost and for Alzheimer's disease gene identification found no passage in our copy supporting the attributed claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredClinical translation of rare disease technologies has benefited from information resources including open databases of genes, clinically interpreted variants, and patient records.
“Clinical translation of these technologies has benefited from a series of information resources, including open databases of genes associated with rare disorders (for example, OMIM and ORPHANET)21, clinically interpreted variants (for example, ClinVar and ClinGen)22,23 and patient records (for example, DECIPHER and MyGene2 (https://mygene2.org/MyGene2))17.”
From A brief history of human disease genetics (2020) · cites this paper as [21]
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“OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders.”
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“Specifically, for disease phenotypes in GeneTuring’s gene disease association questions, we manually identified genes associated with that phenotype according to DisGeNet [42] that are not associated according to OMIM [1].”
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- Authors
- Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F., & Hamosh, A.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Amberger, Joanna S.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-11-26
- Volume
- 43
- Issue
- D1
- Pages
- D789-D798
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC4383985
- OpenAlex
- W2162151166
- Cited by
- 2,691
- References
- 15
- Citations by year
- 2026: 316 · 2025: 343 · 2024: 336 · 2023: 318 · 2022: 322 · 2021: 215 · 2020: 177 · 2019: 166 · 2018: 163 · 2017: 146 · 2016: 117 · 2015: 65 · 2014: 5
- FWCI
- 45.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nucleic Acids Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- National Human Genome Research Institute (U41HG006627)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- OMIM, genetic disorders, genotype-phenotype correlation, genetic heterogeneity, UMLS, Human Phenotype Ontology
- MeSH terms
- Genes, Humans, Phenotype, Internet, Genetic Diseases, Inborn, Databases, Genetic
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