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http://www.nia.nih.gov/ResearchInformation/ScientificResources/LongitudinalStudies.htm

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 11, 2015. A searchable database for epidemiologic research on aging changes across the lifespan. In 2003, the National Institute on Aging (NIA) established the Longitudinal Data on Aging (LDA) working group to assist with the development of research initiatives for identifying the physiologic and other types of factors across the lifespan, affecting onset and progression of disease with advancing age, as well as elucidation of protective factors contributing to exceptionally healthy aging. This database was developed based on input from the LDA working group which indicated that establishing a database of existing sources of longitudinal data on aging (e.g., ongoing longitudinal cohorts, longitudinal data sets, biospecimen repositories) would be a valuable resource for facilitating future research on aging changes across the lifespan. The longitudinal studies, data sets and repositories included in this database encompass a wide range of age groups (childhood to old age), studies in minority populations, as well as sources of longitudinal data existing in the United States and abroad. Our primary purpose for establishing this database is to provide a resource for potential applicants for grants to the NIA. No part of this database can be used for commercial purposes.

Proper citation: National Institute on Aging, Database of Longitudinal Studies (RRID:SCR_008259) Copy   

•   Source: SciCrunch Registry

http://cmbi.bjmu.edu.cn/cmbidata/cgf/CGF_Database/cytokine.medic.kumamoto-u.ac.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 26, 2016. A collection of cDNA, gene and protein records of cytokines deposited in public databases provides various information about the cytokine members of vertebrates in other databases including NCBI GenBank, Swiss-Prot, UniGene, TIGR (The Institute for Genomic Research) Gene Indices, Ensembl, Entrez Gene, Mouse Genome Informatics (MGI) and Rat Genome Database (RGD). It also provides orthologous relationship of cytokine members and includes novel members identified in the databases.

Proper citation: Cytokine Family Database (RRID:SCR_008134) Copy   

•   Source: SciCrunch Registry

http://animal.dna.affrc.go.jp/agp/index.html

Database of comparative gene mapping between species to assist the mapping of the genes related to phenotypic traits in livestock. The linkage maps, cytogenetic maps, polymerase chain reaction primers of pig, cattle, mouse and human, and their references have been included in the database, and the correspondence among species have been stipulated in the database. AGP is an animal genome database developed on a Unix workstation and maintained by a relational database management system. It is a joint project of National Institute of Agrobiological Sciences (NIAS) and Institute of the Society for Techno-innovation of Agriculture, Forestry and Fisheries (STAFF-Institute), under cooperation with other related research institutes. AGP also contains the Pig Expression Data Explorer (PEDE), a database of porcine EST collections derived from full-length cDNA libraries and full-length sequences of the cDNA clones picked from the EST collection. The EST sequences have been clustered and assembled, and their similarity to sequences in RefSeq, and UniGene determined. The PEDE database system was constructed to store sequences and similarity data of swine full-length cDNA libraries and to make them available to users. It provides interfaces for keyword and ID searches of BLAST results and enables users to obtain sequence data and names of clones of interest. Putative SNPs in EST assemblies have been classified according to breed specificity and their effect on coding amino acids, and the assemblies are equipped with an SNP search interface. The database contains porcine nucleotide sequences and cDNA clones that are ready for analyses such as expression in mammalian cells, because of their high likelihood of containing full-length CDS. PEDE will be useful for researchers who want to explore genes that may be responsible for traits such as disease susceptibility. The database also offers information regarding major and minor porcine-specific antigens, which might be investigated in regard to the use of pigs as models in various medical research applications.

Proper citation: Animal Genome Database (RRID:SCR_008165) Copy   

•   Source: SciCrunch Registry

http://www.nisc.nih.gov/projects/comp_seq.html

Generates data for use in developing and refining computational tools for comparing genomic sequence from multiple species. The NISC Comparative Sequencing Program's goal is to establish a data resource consisting of sequences for the same set of targeted genomic regions derived from multiple animal species. The broader program includes plans for a diverse set of analytical studies using the generated sequence and the publication of a series of papers describing the results of those analysis in peer-reviewed journals in a timely fashion. Experimentally, this project involves the shotgun sequencing of mapped BAC clones. For each BAC, an assembly is first performed when a sufficient number of sequence reads have been generated to provide full shotgun coverage of the clone. At that time, the assembled sequence is submitted to the HTGS division of GenBank. Subsequent refinements of the sequence, including the generation of higher-accuracy finished sequence, results in the updating of the sequence record in GenBank. By immediately submitting our BAC-derived sequences to GenBank, it makes their data available as a public service to allow colleagues to speed up their research, consistent with the now well-established routine of sequencing centers participating in the Human Genome Project. However, at the same time, it has made considerable investment in acquiring these mapping and sequence data, including sizable efforts of graduate students, postdoctoral fellows, and other trainees. Furthermore, in most cases, large data sets involving multiple BAC sequences from multiple species must first be generated, often taking many months to accumulate, before the planned analysis can be performed and the resulting papers written and submitted for publication.

Proper citation: Comparative Vertebrate Sequencing (RRID:SCR_008213) Copy   

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http://www.nimh.nih.gov/about/director/index.shtml

Blog by the NIMH Director, Thomas R. Insel, M.D. Users may sort posts by topic and/or subsribe to the RSS Feed, http://www.nimh.nih.gov/site-info/feed-directors-blog.atom

Proper citation: NIMH Director's Blog (RRID:SCR_008841) Copy   

•   Source: SciCrunch Registry

https://www.med.upenn.edu/scxc/

Offers in vivo services specializing in immunodeficient and xenograft models (PDX, humanized immune system). Facility has dedicated BSL2 barrier space equipped with optical imaging, for applications ranging from immunotherapy, cancer biology, infectious diseases and regenerative medicine. Offers services centered around repository of live and fully annotated cells from adult patients with hematologic malignancies (AML, ALL, MPN, MDS), and hematopoietic stem/progenitor cells from healthy donors (BM, CB, and FL).

Proper citation: Pennsylvania University Perelman School of Medicine Stem Cell and Xenograft Core Facility (RRID:SCR_010035) Copy   

•   Source: SciCrunch Registry

http://cgap.nci.nih.gov/Chromosomes/Mitelman

The web site includes genomic data for humans and mice, including transcript sequence, gene expression patterns, single-nucleotide polymorphisms, clone resources, and cytogenetic information. Descriptions of the methods and reagents used in deriving the CGAP datasets are also provided. An extensive suite of informatics tools facilitates queries and analysis of the CGAP data by the community. One of the newest features of the CGAP web site is an electronic version of the Mitelman Database of Chromosome Aberrations in Cancer. The data in the Mitelman Database is manually culled from the literature and subsequently organized into three distinct sub-databases, as follows: -The sub-database of cases contains the data that relates chromosomal aberrations to specific tumor characteristics in individual patient cases. It can be searched using either the Cases Quick Searcher or the Cases Full Searcher. -The sub-database of molecular biology and clinical associations contains no data from individual patient cases. Instead, the data is pulled from studies with distinct information about: -Molecular biology associations that relate chromosomal aberrations and tumor histologies to genomic sequence data, typically genes rearranged as a consequence of structural chromosome changes. -Clinical associations that relate chromosomal aberrations and/or gene rearrangements and tumor histologies to clinical variables, such as prognosis, tumor grade, and patient characteristics. It can be searched using the Molecular Biology and Clinical (MBC) Associations Searcher -The reference sub-database contains all the references culled from the literature i.e., the sum of the references from the cases and the molecular biology and clinical associations. It can be searched using the Reference Searcher. CGAP has developed six web search tools to help you analyze the information within the Mitelman Database: -The Cases Quick Searcher allows you to query the individual patient cases using the four major fields: aberration, breakpoint, morphology, and topography. -The Cases Full Searcher permits a more detailed search of the same individual patient cases as above, by including more cytogenetic field choices and adding search fields for patient characteristics and references. -The Molecular Biology Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to gene rearrangements as a consequence of cytogenetic aberrations. -The Clinical Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to clinical associations of cytogenetic aberrations and/or gene rearrangements. -The Recurrent Chromosome Aberrations Searcher provides a way to search for structural and numerical abnormalities that are recurrent, i.e., present in two or more cases with the same morphology and topography. -The Reference Searcher queries only the references themselves, i.e., the references from the individual cases and the molecular biology and clinical associations. Sponsors: This database is sponsored by the University of Lund, Sweden and have support from the Swedish Cancer Society and the Swedish Children''s Cancer Foundation

Proper citation: Mitelman Database of Chromosome Aberrations in Cancer (RRID:SCR_012877) Copy   

•   Source: SciCrunch Registry

http://www.homd.org/index.php

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on April 14,2022. Database of comprehensive information on the approximately 600 prokaryote species that are present in the human oral cavity. The majority of these species are uncultivated and unnamed, recognized primarily by their 16S rRNA sequences. The HOMD presents a provisional naming scheme for the currently unnamed species so that strain, clone, and probe data from any laboratory can be directly linked to a stably named reference entity. The HOMD links sequence data with phenotypic, phylogenetic, clinical, and bibliographic information. Full and partial oral bacterial genome sequences determined as part of this project and the Human Microbiome Project, are being added to the HOMD as they become available. HOMD offers easy to use tools for viewing all publicly available oral bacterial genomes. Data is also downloadable.

Proper citation: HOMD (RRID:SCR_012770) Copy   

•   Source: SciCrunch Registry

http://www.antibodypedia.com/

Open-access database of antibodies against human proteins developed through collaboration between Antibodypedia AB and the Nature Publishing Group. It aims to provide the scientific community and antibody distributors alike with information on the effectiveness of specific antibodies in specific applications--to help scientists select the right antibody for the right application. Antibodypedia's mission is to promote the functional understanding of the human proteome and expedite analysis of potential biomarkers discovered through clinical efforts. To this end, they have developed an open-access, curated, searchable database containing annotated and scored affinity reagents to aid users in selecting antibodies tailored to specific biological and biomedical assays. They envisage Antibodypedia as a virtual repository of validated antibodies against all human, and ultimately most model-organism, proteins. Such a tool will be exploitable to identify affinity reagents to document protein expression patterns in normal and pathological states and to purify proteins alone and in complex for structural and functional analyses. They hope to promote characterization of the roles and interplay of proteins and complexes in human health and disease. They encourage commercial providers to submit information regarding their inventory of antibodies with links to quality control data. Independent users can submit their own application-specific experimental data using standard validation criteria (supportive or non-supportive) developed with the assistance of an international advisory board recruited from academic research institutions. Users can also comment on specific antibodies without submitting validation data.

Proper citation: Antibodypedia (RRID:SCR_012782) Copy   

•   Source: SciCrunch Registry

http://geneticassociationdb.nih.gov/

The Genetic Association Database is an archive of human genetic association studies of complex diseases and disorders. The goal of this database is to allow the user to rapidly identify medically relevant polymorphism from the large volume of polymorphism and mutational data, in the context of standardized nomenclature. The data is from published scientific papers. Study data is recorded in the context of official human gene nomenclature with additional molecular reference numbers and links. It is gene centered. That is, each record is a record of a gene or marker. If a study investigated 6 genes for a particular disorder, there will be 6 records. Anyone may view this database and anyone may submit records. You do not have to be an author on the original study to submit a record. All submitted records will be reviewed before inclusion in the archive. Both genetic and environmental factors contribute to human diseases. Most common diseases are influenced by a large number of genetic and environmental factors, most of which individually have only a modest effect on the disease. Though genetic contributions are relatively well characterized for some monogenetic diseases, there has been no effort at curating the extensive list of environmental etiological factors. From a comprehensive search of the MeSH annotation of MEDLINE articles, they identified 3,342 environmental etiological factors associated with 3,159 diseases. They also identified 1,100 genes associated with 1,034 complex diseases from the NIH Genetic Association Database (GAD), a database of genetic association studies. 863 diseases have both genetic and environmental etiological factors available. Integrating genetic and environmental factors results in the etiome, which they define as the comprehensive compendium of disease etiology.

Proper citation: Genetic Association Database (RRID:SCR_013264) Copy   

•   Source: SciCrunch Registry

http://www.epa.gov/iris/

IRIS is a toxicology data file on the National Library of Medicine''s (NLM) Toxicology Data Network. It contains data in support of human health risk assessment. It is compiled by the U.S. Environmental Protection Agency (EPA) and contains over 500 chemical records. It is a compilation of electronic reports on specific substances found in the environment and their potential to cause human health effects. IRIS was initially developed for EPA staff in response to a growing demand for consistent information on substances for use in risk assessments, decision-making and regulatory activities. The information in IRIS is intended for those without extensive training in toxicology, but with some knowledge of health sciences. The Integrated Risk Information System (IRIS) is an electronic database containing information on human health effects that may result from exposure to various substances in the environment. IRIS is prepared and maintained by the EPAs National Center for Environmental Assessment (NCEA) within the Office of Research and Development (ORD). The heart of the IRIS system is its collection of searchable documents that describe the health effects of individual substances and that contain descriptive and quantitative information in the following categories: -Noncancer effects: Oral reference doses and inhalation reference concentrations (RfDs and RfCs, respectively) for effects known or assumed to be produced through a nonlinear (possibly threshold) mode of action. In most instances, RfDs and RfCs are developed for the noncarcinogenic effects of substances. -Cancer effects: Descriptors that characterize the weight of evidence for human carcinogenicity, oral slope factors, and oral and inhalation unit risks for carcinogenic effects. Where a nonlinear mode of action is established, RfD and RfC values may be used.

Proper citation: Integrated Risk Information System (RRID:SCR_013005) Copy   

•   Source: SciCrunch Registry

http://www.syfpeithi.de/

SYFPEITHI is a database comprising more than 7000 peptide sequences known to bind class I and class II MHC molecules. The entries are compiled from published reports only. It contains a collection of MHC class I and class II ligands and peptide motifs of humans and other species, such as apes, cattle, chicken, and mouse, for example, and is continuously updated. Searches for MHC alleles, MHC motifs, natural ligands, T-cell epitopes, source proteins/organisms and references are possible. Hyperlinks to the EMBL and PubMed databases are included. In addition, ligand predictions are available for a number of MHC allelic products. The database is based on previous publications on T-cell epitopes and MHC ligands. It contains information on: -Peptide sequences -anchor positions -MHC specificity -source proteins, source organisms -publication references Since the number of motifs continuously increases, it was necessary to set up a database which facilitates the search for peptides and allows the prediction of T-cell epitopes. The prediction is based on published motifs (pool sequencing, natural ligands) and takes into consideration the amino acids in the anchor and auxiliary anchor positions, as well as other frequent amino acids. The score is calculated according to the following rules: The amino acids of a certain peptide are given a specific value depending on whether they are anchor, auxiliary anchor or preferred residue. Ideal anchors will be given 10 points, unusual anchors 6-8 points, auxiliary anchors 4-6 and preferred residues 1-4 points. Amino acids that are regarded as having a negative effect on the binding ability are given values between -1 and -3. Sponsors: SYFPEITHI is supported by DFG-Sonderforschungsbereich 685 and theEuropean Union: EU BIOMED CT95-1627, BIOTECH CT95-0263, and EU QLQ-CT-1999-00713.

Proper citation: SYFPEITHI: A Database for MHC Ligands and Peptide Motifs (RRID:SCR_013182) Copy   

•   Source: SciCrunch Registry

http://www.strokecenter.org/radiology/

The Internet Stroke Center at Washington University is pleased to offer this module for viewing CT, MR, and angiogram images of cerebrovascular and neurological diseases. While this project is still being perfected -- and many more cases have yet to be added -- we hope that you will find this collection useful in your education and practice. The images presented here are for educational use only. This information may not be used for diagnosis or treatment. All images are protected property of the Internet Stroke Center at Washington University and may not be reproduced without permission. Permission may be granted to students and professionals to borrow images from this site for educational purposes and/or presentations; we just ask that an email be sent detailing both the desired material and the intended use. Please direct all comments, questions, and requests to the Site Editor of the Internet Stroke Center.

Proper citation: Neurology Image Library from The Internet Stroke Center (RRID:SCR_013633) Copy   

•   Source: SciCrunch Registry

http://web.stanford.edu/group/barres_lab/brain_rnaseq.html

Database containing RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of cerebral cortex. Collection of RNA-Seq transcriptome and splicing data from glia, neurons, and vascular cells of mouse cerebral cortex. RNA-Seq of cell types isolated from mouse and human brain.

Proper citation: Brain RNA-Seq (RRID:SCR_013736) Copy   

•   Source: SciCrunch Registry

https://hpap.pmacs.upenn.edu

Program is performing deep phenotyping of human endocrine pancreas and its interaction with immune system to better understand cellular and molecular events that precede and lead to beta cell loss in Type-1 Diabetes (T1D) and islet dysfunction in Type-2 Diabetes (T2D).

Proper citation: HIRN Human Pancreas Analysis Program (RRID:SCR_016202) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://www.jdrfnpod.org

A collaborative research project that supports nPOD approved diabetes investigators by freely providing rare and difficult-to-obtain tissues from type 1 and type 2 diabetes donors. Interested researchers are encouraged to apply to obtain nPOD tissues, or to request access to analyze cases in the nPOD Online Pathology site. Interested donors can contact nPOD directly for more information.

Proper citation: Network for Pancreatic Organ Donors with Diabetes (RRID:SCR_014641) 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

http://epifactors.autosome.ru/

Manually curated collection of human epigenetic factors, their complexes, corresponding genes and products.

Proper citation: EpiFactors (RRID:SCR_016956) Copy   

•   Source: SciCrunch Registry

http://www.regulomedb.org/

Database that annotates SNPs with known and predicted regulatory elements in intergenic regions of H. sapiens genome. Known and predicted regulatory DNA elements include regions of DNAase hypersensitivity, binding sites of transcription factors, and promoter regions that have been biochemically characterized to regulation transcription. Source of these data include public datasets from GEO, ENCODE project, and published literature.

Proper citation: RegulomeDB (RRID:SCR_017905) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/pubs/MitoCarta/

Collection of genes encoding proteins with strong support of mitochondrial localization. Inventory of genes encoding mitochondrial-localized proteins and their expression across 14 mouse tissues. Database is based on human and mouse RefSeq proteins that are mapped to NCBI Gene loci. MitoCarta 2.0 inventory provides molecular framework for system-level analysis of mammalian mitochondria.

Proper citation: MitoCarta (RRID:SCR_018165) Copy   

•   Source: SciCrunch Registry