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.

Here, we describe an overview and update on GeneMatcher (http://www.genematcher.org), a freely accessible Web-based tool developed as part of the Baylor-Hopkins Center for Mendelian Genomics. We created GeneMatcher with the goal of identifying additional individuals with rare phenotypes who had variants in the same candidate disease gene. We also wanted to facilitate connections to basic scientists working on orthologous genes in model systems with the goal of connecting their work to human Mendelian phenotypes. Meeting these goals will enhance the identification of novel Mendelian genes. Launched in September, 2013, Gene-Matcher now has 2,178 candidate genes from 486 submitters spread across 38 countries entered in the database (June 1, 2015). GeneMatcher is also part of the Match-maker Exchange (http://matchmakerexchange.org/) with an Application Programing Interface enabling submitters to query other databases of genetic variants and phenotypes without having to create accounts and data entries in multiple systems.

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for “the needle in a haystack” to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease-specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can “match” these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

Identifying the causative variant from among the thousands identified by whole-exome sequencing or whole-genome sequencing is a formidable challenge. To make this process as efficient and flexible as possible, we have developed a Variant Analysis Module coupled to our previously described Web-based phenotype intake tool, PhenoDB (http://researchphenodb.net and http://phenodb.org). When a small number of candidate-causative variants have been identified in a study of a particular patient or family, a second, more difficult challenge becomes proof of causality for any given variant. One approach to this problem is to find other cases with a similar phenotype and mutations in the same candidate gene. Alternatively, it may be possible to develop biological evidence for causality, an approach that is assisted by making connections to basic scientists studying the gene of interest, often in the setting of a model organism. Both of these strategies benefit from an open access, online site where individual clinicians and investigators could post genes of interest. To this end, we developed GeneMatcher (http://genematcher.org), a freely accessible Website that enables connections between clinicians and researchers across the world who share an interest in the same gene(s).

To interpret whole exome/genome sequence data for clinical and research purposes, comprehensive phenotypic information, knowledge of pedigree structure, and results of previous clinical testing are essential. With these requirements in mind and to meet the needs of the Centers for Mendelian Genomics project, we have developed PhenoDB (http://phenodb.net), a secure, Web-based portal for entry, storage, and analysis of phenotypic and other clinical information. The phenotypic features are organized hierarchically according to the major headings and subheadings of the Online Mendelian Inheritance in Man (OMIM®) clinical synopses, with further subdivisions according to structure and function. Every string allows for a free-text entry. All of the approximately 2,900 features use the preferred term from Elements of Morphology and are fully searchable and mapped to the Human Phenotype Ontology and Elements of Morphology. The PhenoDB allows for ascertainment of relevant information from a case in a family or cohort, which is then searchable by family, OMIM number, phenotypic feature, mode of inheritance, genes screened, and so on. The database can also be used to format phenotypic data for submission to dbGaP for appropriately consented individuals. PhenoDB was built using Django, an open source Web development tool, and is freely available through the Johns Hopkins McKusick-Nathans Institute of Genetic Medicine (http://phenodb.net).