A new face and new challenges for Online Mendelian Inheritance in Man (OMIM®)

By Joanna S. Amberger, Carol A. Bocchini, Ada Hamosh. Human Mutation, 2011

335 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cd0b0df43885111ce4d85f42d125794805aeccba

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Citations in the corpus

ALSoD: A user‐friendly online bioinformatics tool for amyotrophic lateral sclerosis genetics (2012, background): “, OMIM [Amberger et al., 2011; Amberger et al., 2009], Genecards [Safran et al.]”
Protein–protein interaction sites are hot spots for disease‐associated nonsynonymous SNPs (2012, background): “3) “Neutral polymorphisms” which are nsSNPs that are not present in OMIM.”
Representation of rare diseases in health information systems: The orphanet approach to serve a wide range of end users (2012, neutral): “Theclosestterminology istheOMIMone[Ambergeretal.,2011],whichpursuesthetask ofcatalogingtheassociationbetweenhumanphenotypesandtheir causativesgenes.”
Ensembl 2014 (2013, background): “We signiﬁcantly expanded our support for human phenotype data in Ensembl beyond the UniProt (21), OMIM (22), EGA, HGMD Public (23) (variation location only), COSMIC somatic mutations (24) and NHGRI GWAS Catalog (25) resources that we have supported in the past.”
Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data (2014, methodology): “DO also includes disease terms extracted directly from Online Mendelian Inheritance in Man (OMIM) (11), the Experimental Factor Ontology (EFO, http://www.”
Common activation mechanism of class A GPCRs (2019, methodology): “Four commonly used databases (UniProt [The UniProt Consortium, 2017], OMIM [Amberger et al., 2011], Ensembl [Zerbino et al.”
ChemProt-3.0: a global chemical biology diseases mapping (2016, positive): “OMIM (26), the human disease network (27) GeneCards (28), KEGG (29), Reactome (30), UniPathway (31) and Gene Ontology (32) databases were also downloaded, curated and included in our system.”
An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge (2014, methodology): “Use of reference datasets (1000 Genomes, dbSNP, Hap-Map, NHLBI Go ESP and OMNI), and annotation databases (OMIM, Uniprot, SeattleSeq, SNPedia, ClinVar, PharmGKB, Human Gene Mutation Database, dbNSFP and in-house annotations) revealed considerable consensus and uniformity across entries.”
The Comparative Toxicogenomics Database's 10th year anniversary: update 2015 (2014, methodology): “In 2006, we produced MEDIC (12), a resource of merged OMIM (13) and MeSH (14) disease terms, allowing biocurators to additionally capture chemical–disease (C– D) and gene–disease (G–D) relationships using a robust and hierarchical controlled vocabulary.”
Ensembl 2013 (2012, methodology): “We have also added mitochondrial variants, information on clinical significance and global minor allele frequencies from dbSNP, as well as phenotype data for >287 000 variants from OMIM (4), the European Genomephenome Archive (EGA) and the NHGRI GWAS catalog (29).”
The Comparative Toxicogenomics Database: update 2013 (2012, methodology): “(iii) To curate disease information, CTD biocurators annotate using MEDIC (9), a merged disease vocabulary of Medical Subject Headings (MeSH) disease terms (27) and the Online Mendelian Inheritance in Man (28).”