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http://SchistoDB.net

SchistoDB is a genomic database for the parasitic organism Schistosoma mansoni, one of the major causative agents of schistosomiasis worldwide. It currently incorporates sequences and annotation for S. mansoni in a single user-friendly database. Several genomic scale analyses are available as well as ESTs, oligonucleotides, metabolic pathways and drugs. Make your data available: If you''d like to have your updates and/or datasets integrated in SchistoDB, drop us an email.

Proper citation: Schistosoma mansoni Database (RRID:SCR_004341) Copy   

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http://nematode.lab.nig.ac.jp/

Expression pattern map of the 100Mb genome of the nematode Caenorhabditis elegans through EST analysis and systematic whole mount in situ hybridization. NEXTDB is the database to integrate all information from their expression pattern project and to make the data available to the scientific community. Information available in the current version is as follows: * Map: Visual expression of the relationships among the cosmids, predicted genes and the cDNA clones. * Image: In situ hybridization images that are arranged by their developmental stages. * Sequence: Tag sequences of the cDNA clones are available. * Homology: Results of BLASTX search are available. Users of the data presented on our web pages should not publish the information without our permission and appropriate acknowledgment. Methods are available for: * In situ hybridization on whole mount embryos of C.elegans * Protocols for large scale in situ hybridization on C.elegans larvae

Proper citation: NEXTDB (RRID:SCR_004480) Copy   

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http://www.who.int/ictrp/en/

Public database of information about all clinical trials involving humans, this global initiative provides a single point of access to information about ongoing and completed clinical trials. It contains the trial registration data sets made available by data providers around the world meeting criteria for content and quality control. It also aims to: * To improve the comprehensiveness, completeness and accuracy of registered clinical trial data * To communicate and raise awareness of the need to register clinical trials * To ensure the accessibility of registered data * To build capacity for clinical trial registration * To encourage the utilization of registered data * To ensure the sustainability of the ICTRP The mission of the WHO International Clinical Trials Registry Platform is to ensure that a complete view of research is accessible to all those involved in health care decision making. This will improve research transparency and will ultimately strengthen the validity and value of the scientific evidence base. The registration of all interventional trials is a scientific, ethical and moral responsibility. The ICTRP: * Publishes the ICTRP Search Portal * Supports the WHO Registry Network * Supports countries and regions wanting to establish WHO-compliant clinical trial registries or policies on trial registration.

Proper citation: WHO International Clinical Trials Registry Platform (RRID:SCR_004475) Copy   

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http://www.digimorph.org/

A dynamic archive of information on digital morphology and high-resolution X-ray computed tomography of biological specimens serving imagery for more than 750 specimens contributed by almost 150 collaborating researchers from the world''s premiere natural history museums and universities. Browse through the site and see spectacular imagery and animations and details on the morphology of many representatives of the Earth''s biota. Digital Morphology, part of the National Science Foundation Digital Libraries Initiative, develops and serves unique 2D and 3D visualizations of the internal and external structure of living and extinct vertebrates, and a growing number of ''invertebrates.'' The Digital Morphology library contains nearly a terabyte of imagery of natural history specimens that are important to education and central to ongoing cutting-edge research efforts. Digital Morphology visualizations are now in use in classrooms and research labs around the world and can be seen in a growing number of museum exhibition halls. The Digital Morphology site currently presents: * QuickTime animations of complete stacks of serial CT sections * Animated 3D volumetric movies of complete specimens * Stereolithography (STL) files of 3D objects that can be viewed interactively and rapidly prototyped into scalable physical 3D objects that can be handled and studied as if they were the original specimens * Informative introductions to the scanned organisms, often written by world authorities * Pertinent bibliographic information on each specimen * Useful links * A course resource for our ''Digital Methods for Paleontology'' course, in which students learn how to generate all of the types of imagery displayed on the Digital Morphology site

Proper citation: DigiMorph (RRID:SCR_004416) Copy   

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https://www.med.unc.edu/pgc/

Consortium conducting meta-analyses of genome-wide genetic data for psychiatric disease. Focused on autism, attention-deficit hyperactivity disorder, bipolar disorder, major depressive disorder, schizophrenia, anorexia nervosa (AN), Tourette syndrome (TS), and obsessive-compulsive disorder (OCD). Used to investigate common single nucleotide polymorphisms (SNPs) genotyped on commercial arrays, structural variation (copy number variation) and uncommon or rare genetic variation. To participate you are asked to upload data from your study to central computer used by this consortium. Genetic Cluster Computer serves as data warehouse and analytical platform for this study . When data from your study have been incorporated, account will be provided on central server and access to all GWAS genotypes, phenotypes, and meta-analytic results relevant to deposited data and participation aims. NHGRI GWAS Catalog contains updated information about all GWAS in biomedicine, and is usually excellent starting point to find comprehensive list of studies. Files can be obtained by any PGC member for any disease to which they contributed data. These files can also be obtained by application to NIMH Genetics Repository. Individual-level genotype and phenotype data requires application, material transfer agreement, and informed consent consideration. Some datasets are also in controlled-access dbGaP and Wellcome Trust Case-Control Consortium repositories. PGC members can also receive back cleaned and imputed data and results for samples they contributed to PGC analyses.

Proper citation: Psychiatric Genomics Consortium (RRID:SCR_004495) Copy   

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http://www.fishbase.org/home.htm

A global species database and encyclopedia of over 32,800 species and subspecies of fishes that is searchable by common name, genus, species, geography, family, ecosystem, references literature, tools, etc. It links to other, related databases such as the Catalog of Fishes, GenBack, and LarvalBase. It is associated with a partner journal, Acta Ichthyologica et Piscatoria. It is available in English, Greek, Spanish, Portuguese, French, Dutch, Italian, and German. Photo and video submissions are welcome. FishBase 2004 is also available on DVD or CD-ROMs with full information on 28,500 species. It comes together with the FishBase 2000 book and can be ordered for 95 US$ including air-mail.

Proper citation: FishBase (RRID:SCR_004376) Copy   

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https://www.bannerhealth.com/research/locations/sun-health-institute/programs/body-donation

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. An autopsy-based, research-devoted brain bank, biobank and biospecimen bank that derives its human donors from the Arizona Study of Aging and Neurodegenerative Disease (AZSAND), a longitudinal clinicopathological study of the health and diseases of elderly volunteers living in Maricopa county and metropolitan Phoenix, Arizona. Their function is studied during life and their organs and tissue after death. To date, they have concentrated their studies on Alzheimer's disease, Parkinson's disease, heart disease and cancer. They share the banked tissue, biomaterials and biospecimens with qualified researchers worldwide. Registrants with suitable scientific credentials will be allowed access to a database of available tissue linked to relevant clinical information, and will allow tissue requests to be initiated., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Brain and Body Donation Program (RRID:SCR_004822) Copy   

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http://www.smpdb.ca/

An interactive, visual database containing more than 350 small molecule pathways found in humans. More than 2/3 of these pathways (>280) are not found in any other pathway database. SMPDB is designed specifically to support pathway elucidation and pathway discovery in metabolomics, transcriptomics, proteomics and systems biology. It is able to do so, in part, by providing exquisitely detailed, fully searchable, hyperlinked diagrams of human metabolic pathways, metabolic disease pathways, metabolite signaling pathways and drug-action pathways. All SMPDB pathways include information on the relevant organs, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to detailed descriptions contained in the HMDB or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All SMPDB pathways are accompanied with detailed descriptions and references, providing an overview of the pathway, condition or processes depicted in each diagram. The database is easily browsed and supports full text, sequence and chemical structure searching. Users may query SMPDB with lists of metabolite names, drug names, genes / protein names, SwissProt IDs, GenBank IDs, Affymetrix IDs or Agilent microarray IDs. These queries will produce lists of matching pathways and highlight the matching molecules on each of the pathway diagrams. Gene, metabolite and protein concentration data can also be visualized through SMPDB''s mapping interface. All of SMPDB''s images, image maps, descriptions and tables are downloadable.

Proper citation: Small Molecule Pathway Database (RRID:SCR_004844) Copy   

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http://optn.transplant.hrsa.gov/

The only national patient waiting list and an online database system, called UNet, that links all of the professionals involved in the donation and transplantation system for the collection, storage, analysis, and publication of all OPTN data pertaining to the patient waiting list, organ matching, and transplants. The system contains data regarding every organ donation and transplant event occurring in the U.S. since October 1, 1987. UNet is a fail-safe, 24/7, secure Internet-based transplant information database created to enable the nation''''s organ transplant institutions to: * register patients for transplants * match donated organs to waiting patients * manage the time-sensitive, life-critical data of all patients, before and after their transplants Data reports are available by type: National Data, Regional Data, State Data, Center Data, Build Advanced Report, and Annual Report Data. UNet is being used right now by all of the nation''''s organ transplant programs, organ procurement organizations, and histocompatibility (tissue typing) laboratories working cooperatively to efficiently share a limited number of donated organs among thousands of patients.

Proper citation: Organ Procurement and Transplantation Network (RRID:SCR_004883) Copy   

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http://www.hmpdacc.org/

Common repository for diverse human microbiome datsets and minimum reporting standards for Common Fund Human Microbiome Project.

Proper citation: HMP Data Analysis and Coordination Center (RRID:SCR_004919) Copy   

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https://genome.unc.edu/

Database for microarray data storage, retrieval, analysis, and visualization.

Proper citation: UNC Microarray Database (RRID:SCR_010979) Copy   

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http://inparanoid.sbc.su.se/cgi-bin/index.cgi

Collection of pairwise comparisons between 100 whole genomes generated by a fully automatic method for finding orthologs and in-paralogs between TWO species. Ortholog clusters in the InParanoid are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for in-paralogs. The original data sets can be downloaded.

Proper citation: InParanoid: Eukaryotic Ortholog Groups (RRID:SCR_006801) Copy   

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http://www.peptideatlas.org

Multi-organism, publicly accessible compendium of peptides identified in a large set of tandem mass spectrometry proteomics experiments. Mass spectrometer output files are collected for human, mouse, yeast, and several other organisms, and searched using the latest search engines and protein sequences. All results of sequence and spectral library searching are subsequently processed through the Trans Proteomic Pipeline to derive a probability of correct identification for all results in a uniform manner to insure a high quality database, along with false discovery rates at the whole atlas level. The raw data, search results, and full builds can be downloaded for other uses. All results of sequence searching are processed through PeptideProphet to derive a probability of correct identification for all results in a uniform manner ensuring a high quality database. All peptides are mapped to Ensembl and can be viewed as custom tracks on the Ensembl genome browser. The long term goal of the project is full annotation of eukaryotic genomes through a thorough validation of expressed proteins. The PeptideAtlas provides a method and a framework to accommodate proteome information coming from high-throughput proteomics technologies. The online database administers experimental data in the public domain. You are encouraged to contribute to the database.

Proper citation: PeptideAtlas (RRID:SCR_006783) Copy   

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http://www.ensemblgenomes.org/

Database portal offering integrated access to genome-scale data from non-vertebrate species of scientific interest, developed using the Ensembl genome annotation and visualization platform. Ensembl Genomes consists of five sub-portals (for bacteria, protists, fungi, plants and invertebrate metazoa) designed to complement the availability of vertebrate genomes in Ensembl. Many of the databases supporting the portal have been built in close collaboration with the scientific community - essential for maintaining the accuracy and usefulness of the resource. A common set of user interfaces (which include a graphical genome browser, FTP, BLAST search, a query optimized data warehouse, programmatic access, and a Perl API) is provided for all domains. Data types incorporated include annotation of (protein and non-protein coding) genes, cross references to external resources, and high throughput experimental data (e.g. data from large scale studies of gene expression and polymorphism visualized in their genomic context). Additionally, extensive comparative analysis has been performed, both within defined clades and across the wider taxonomy, and sequence alignments and gene trees resulting from this can be accessed through the site.

Proper citation: Ensembl Genomes (RRID:SCR_006773) Copy   

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https://cansar.icr.ac.uk/

canSAR is an integrated database that brings together biological, chemical, pharmacological (and eventually clinical) data. Its goal is to integrate this data and make it accessible to cancer research scientists from multiple disciplines, in order to help with hypothesis generation in cancer research and support translational research. This cancer research and drug discovery resource was developed to utilize the growing publicly available biological annotation, chemical screening, RNA interference screening, expression, amplification and 3D structural data. Scientists can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets. canSAR has, from the outset, been completely use-case driven which has dramatically influenced the design of the back-end and the functionality provided through the interfaces. The Web interface provides flexible, multipoint entry into canSAR. This allows easy access to the multidisciplinary data within, including target and compound synopses, bioactivity views and expert tools for chemogenomic, expression and protein interaction network data.

Proper citation: canSAR (RRID:SCR_006794) Copy   

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http://prodom.prabi.fr/

Comprehensive set of protein domain families automatically generated from UniProt Knowledge Database. Automated clustering of homologous domains generated from global comparison of all available protein sequences., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ProDom (RRID:SCR_006969) Copy   

•   Source: SciCrunch Registry

http://ardb.cbcb.umd.edu

The goals of Antibiotic Resistance Genes Database (ARGB) are to provide a centralized compendium of information on antibiotic resistance, to facilitate the consistent annotation of resistance information in newly sequenced organisms, and also to facilitate the identification and characterization of new genes. ARGB contains six types of database groups: - Resistance Type: This database contains information, such as resistance profile, mechanism, requirement, epidemiology for each type. - Resistance Gene: This database contains information, such as resistance profile, resistance type, requirement, protein and DNA sequence for each gene.This database only includes NON-REDUNDANT, NON-VECTOR, COMPLETE genes. - Antibiotic: This database contains information, such as producer, action mechanism, resistance type, for each gene. - Resistance Gene(NonRD): This database contains the same information as Resistance Gene. It does NOT include NON-REDUNDANT, NON-VECTOR genes, but includes INCOMPLETE genes. - Resistance Gene(ALL): This database contains the same information as Resistance Gene. It includes all REDUNDANT, VECTOR AND INCOMPLETE genes. - Resistance Species: This database contains resistance profile and corresponding resistance genes for each species. Furthermore, ARDB also contians three types BLAST database: - Resistance Genes Complete: Contains only NON-REDUNDANT, NON-VECTOR, COMPLETE genes sequences. - Resistance Genes Non-redundant: Contains NON-REDUNDANT, NON-VECTOR, COMPLETE, INCOMPLETE genes sequences. - Resistance Genes All: Contains all REDUNDANT, VECTOR, COMPLETE, INCOMPLETE genes sequences. Lastly, ARDB provides four types of Analytical tools: - Normal BLAST: This function allows an user to input a DNA or protein sequence, and find similar DNA (Nucleotide BLAST) or protein (Protein BLAST) sequences using blastn, blastp, blastx, tblastn, tblastx - RPS BLAST: A web RPSBLAST (RPS BLAST) interface is provided to align a query sequence against the Position Specific Scoring Matrix (PSSM) for each type. Normally, this will give the same annotation information as using regular BLAST mentioned above. - Multiple Sequences BLAST (Genome Annotation): This function allows an user to annotate multiple (less than 5000) query sequences in FASTA format. - Mutation Resistance Identification: This function allows an user to identify mutations that will cause potential antibiotic resistance, for 12 genes (16S rRNA, 23S rRNA, gyrA, gyrB, parC, parE, rpoB, katG, pncA, embB, folP, dfr). ������ :Sponsors: ARDB is funded by Uniformed Services University of the Health Sciences, administered by the Henry Jackson Foundation. :

Proper citation: Antibiotic Resistance Genes Database (RRID:SCR_007040) Copy   

•   Source: SciCrunch Registry

http://seer.cancer.gov/

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

Proper citation: Surveillance Epidemiology and End Results (RRID:SCR_006902) Copy   

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http://gtrnadb.ucsc.edu

This genomic tRNA database contains tRNA gene predictions made by the program tRNAscan-SE (Lowe & Eddy, Nucl Acids Res 25: 955-964, 1997) on complete or nearly complete genomes. Unless otherwise noted, all annotation is automated, and has not been inspected for agreement with published literature. Transfer RNAs (tRNAs) represent the single largest, best-understood class of non-protein coding RNA genes found in all living organisms. By far, the major source of new tRNAs is computational identification of genes within newly sequenced genomes. To organize the rapidly growing collection and enable systematic analyses, we created the Genomic tRNA Database (GtRNAdb). The web resource provides overview statistics of tRNA genes within each analyzed genome, including information by isotype and genetic locus, easily downloadable primary sequences, graphical secondary structures and multiple sequence alignments. Direct links for each gene to UCSC eukaryotic and microbial genome browsers provide graphical display of tRNA genes in the context of all other local genetic information. The database can be searched by primary sequence similarity, tRNA characteristics or phylogenetic group. Inevitably with automated sequence analysis, we find exceptions to general identification rules, isoacceptor type predictions (esp. due to variable post-transcriptional anticodon modification), and questionable tRNA identifications (due to pseudogenes, SINES, or other tRNA-derived elements). We attempt to document all cases we come across, and welcome feedback on new or unrecognized discrepancies.

Proper citation: GtRNAdb - Genomic tRNA Database (RRID:SCR_006939) Copy   

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

Genetic factors contribute significantly to ASD. AutismKB is an evidence-based knowledgebase of Autism spectrum disorder (ASD) genetics. The current version contains 2193 genes (99 syndromic autism related genes and 2135 non-syndromic autism related genes), 4617 Copy Number Variations (CNVs) and 158 linkage regions associated with ASD by one or more of the following six experimental methods: # Genome-Wide Association Studies (GWAS); # Genome-wide CNV studies; # Linkage analysis; # Low-scale genetic association studies; # Expression profiling; # Other low-scale gene studies. Based on a scoring and ranking system, 99 syndromic autism related genes and 383 non-syndromic autism related genes (434 genes in total) were designated as having high confidence. Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with a prevalence of 1.0-2.6%. The three core symptoms of ASD are: # impairments in reciprocal social interaction; # communication impairments; # presence of restricted, repetitive and stereotyped patterns of behavior, interests and activities.

Proper citation: AutismKB (RRID:SCR_006937) Copy   

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