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http://kobas.cbi.pku.edu.cn/

Web server to identify statistically enriched pathways, diseases, and GO terms for a set of genes or proteins, using pathway, disease, and GO knowledge from multiple famous databases. It allows for both ID mapping and cross-species sequence similarity mapping. It then performs statistical tests to identify statistically significantly enriched pathways and diseases. KOBAS 2.0 incorporates knowledge across 1327 species from 5 pathway databases (KEGG PATHWAY, PID, BioCyc, Reactome and Panther) and 5 human disease databases (OMIM, KEGG DISEASE, FunDO, GAD and NHGRI GWAS Catalog). A standalone command line version is also available, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: KOBAS (RRID:SCR_006350) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov

A portal to biomedical and genomic information. NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information for the better understanding of molecular processes affecting human health and disease.

Proper citation: NCBI (RRID:SCR_006472) Copy   

•   Source: SciCrunch Registry

https://neuinfo.org/mynif/search.php?list=cover&q=*

Service that partners with the community to expose and simultaneously drill down into individual databases and data sets and return relevant content. This type of content, part of the so called hidden Web, is typically not indexed by existing web search engines. Every record links back to the originating site. In order for NIF to directly query these independently maintained databases and datasets, database providers must register their database or dataset with the NIF Data Federation and specify permissions. Databases are concept mapped for ease of sharing and to allow better understanding of the results. Learn more about registering your resource, http://neuinfo.org/nif_components/disco/interoperation.shtm Search results are displayed under the Data Federation tab and are categorized by data type and nervous system level. In this way, users can easily step through the content of multiple resources, all from the same interface. Each federated resource individually displays their query results with links back to the relevant datasets within the host resource. This allows users to take advantage of additional views on the data and tools that are available through the host database. The NIF site provides tutorials for each resource, indicated by the Professor Icon professor icon showing users how to navigate the results page once directed there through the NIF. Additionally, query results may be exported as an Excel document. Note: NIF is not responsible for the availability or content of these external sites, nor does NIF endorse, warrant or guarantee the products, services or information described or offered at these external sites. Integrated Databases: Theses virtual databases created by NIF and other partners combine related data indexed from multiple databases and combine them into one view for easier browsing. * Integrated Animal View * Integrated Brain Gene Expression View * Integrated Disease View * Integrated Nervous System Connectivity View * Integrated Podcasts View * Integrated Software View * Integrated Video View * Integrated Jobs * Integrated Blogs For a listing of the Federated Databases see, http://neuinfo.org/mynif/databaseList.php or refer to the Resources Listed by NIF Data Federation table below.

Proper citation: NIF Data Federation (RRID:SCR_004834) Copy   

•   Source: SciCrunch Registry

http://monarchinitiative.org/

Repository of information about model organisms, in vitro models, genes, pathways, gene expression, protein and genetic interactions, orthology, disease, phenotypes, publications, and authors, and the the ability to navigate multi-scale spatial and temporal phenotypes across in vivo and in vitro model systems in the context of genetic and genomic data, using semantics and statistics. The discovery system provides basic and clinical science researchers, informaticists, and medical professionals with an integrated interface and set of discovery tools to reveal the genetic basis of disease, facilitate hypothesis generation, and identify novel candidate drug targets. The aim for this system is to promote true translational research, in that clinicians can connect with model systems researchers with expertise in related phenotypes, assays, or models. In addition to providing easy-to-use tools to navigate the model-disease data landscape, it also provides services for other resources, and educational outreach regarding the production of structured data for biomedical discovery. Model systems are the cornerstone of biomedical research to investigate biological processes, test gene-based disease hypotheses, and develop and test disease treatments. The vast knowledge about model systems can be better utilized if semantically aggregated and made queryable based on any number of facets, such as phenotypic similarity, network analysis, gene expression and function, and genomics.

Proper citation: Monarch Initiative (RRID:SCR_001373) Copy   

•   Source: SciCrunch Registry

http://www.human-phenotype-ontology.org/

Provides standardized vocabulary of phenotypic abnormalities encountered in human disease. Structured and controlled vocabulary for phenotypic features encountered in human hereditary and other disease. HPO is being developed in collaboration with members of OBO Foundry (Open Biological and Biomedical Ontologies), and logical definitions for HPO terms are being developed using PATO and a number of other ontologies including FMA, GO, ChEBI, and MPATH.

Proper citation: Human Phenotype Ontology (RRID:SCR_006016) Copy   

•   Source: SciCrunch Registry

http://phenomebrowser.net/

PhenomeNet is a cross-species phenotype similarity network. It contains the experimentally observed phenotypes of multiple species as well as the phenotypes of human diseases. PhenomeNet provides a measure of phenotypic similarity between the phenotypes it contains. The latest release (from 22 June 2012) contains 124,730 complex phenotype nodes taken from the yeast, fish, worm, fly, rat, slime mold and mouse model organism databases as well as human disease phenotypes from OMIM and OrphaNet. The network is a complete graph in which edge weights represent the degree of phenotypic similarity. Phenotypic similarity can be used to identify and prioritize candidate disease genes, find genes participating in the same pathway and orthologous genes between species. To compute phenotypic similarity between two sets of phenotypes, we use a weighted Jaccard index. First, phenotype ontologies are used to infer all the implications of a phenotype observation using several phenotype ontologies. As a second step, the information content of each phenotype is computed and used as a weight in the Jaccard index. Phenotypic similarity is useful in several ways. Phenotypic similarity between a phenotype resulting from a genetic mutation and a disease can be used to suggest candidate genes for a disease. Phenotypic similarity can also identify genes in a same pathway or orthologous genes. PhenomeNet uses the axioms in multiple species-dependent phenotype ontologies to infer equivalent and related phenotypes across species. For this purpose, phenotype ontologies and phenotype annotations are integrated in a single ontology, and automated reasoning is used to infer equivalences. Specifically, for every phenotype, PhenomeNet infers the related mammalian phenotype and uses the Mammalian Phenotype Ontology for computing phenotypic similarity. Tools: * PhenomeBLAST - A tool for cross-species alignments of phenotypes * PhenomeDrug - method for drug-repurposing

Proper citation: phenomeNET (RRID:SCR_006165) Copy   

•   Source: SciCrunch Registry

http://gila.bioengr.uic.edu/snp/toposnp

A topographic database for analyzing non-synonymous SNPs (nsSNPs) that can be mapped onto known 3D structures of proteins. These include disease- associated nsSNPs derived from the Online Mendelian Inheritance in Man (OMIM) database and other nsSNPs derived from dbSNP, a resource at the National Center for Biotechnology Information that catalogs SNPs. TopoSNP further classifies each nsSNP site into three categories based on their geometric location: those located in a surface pocket or an interior void of the protein, those on a convex region or a shallow depressed region, and those that are completely buried in the interior of the protein structure. These unique geometric descriptions provide more detailed mapping of nsSNPs to protein structures. It also includes relative entropy of SNPs calculated from multiple sequence alignment as obtained from the Pfam database (a database of protein families and conserved protein motifs) as well as manually adjusted multiple alignments obtained from ClustalW. These structural and conservational data can be useful for studying whether nsSNPs in coding regions are likely to lead to phenotypic changes. TopoSNP includes an interactive structural visualization web interface, as well as downloadable batch data.

Proper citation: TopoSNP (RRID:SCR_005572) Copy   

•   Source: SciCrunch Registry

http://malacards.org

An integrated database of human maladies and their annotations, modeled on the architecture and richness of the popular GeneCards database of human genes. The database contains 17,705 diseases, consolidated from 28 sources.

Proper citation: MalaCards (RRID:SCR_005817) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-7/search?q=*

Virtual database currently indexing interaction between genes and diseases from Online Mendelian Inheritance in Man (OMIM) and Comparative Toxicogenomics Database (CTD).

Proper citation: Integrated Gene-Disease Interaction (RRID:SCR_006173) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/humanDisease.shtml

Collection of published and potential mouse models of human disease, discovery of candidate genes and investigation of phenotypic similarity between mouse models and human patients. Mouse mutation, and phenotype and disease model data from Mouse Genome Informatics database are integrated with human gene to disease relationships from the National Center for Biotechnology Information and Online Mendelian Inheritance in Man and human disease to phenotype relationships from the Human Phenotype Ontology.

Proper citation: Human Mouse Disease Connection (RRID:SCR_017522) Copy   

•   Source: SciCrunch Registry