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https://www.grnpedia.org/trrust/

TRUSST is reference database of human transcriptional regulatory interactions.TRRUST v2 is manually curated expanded reference database of human and mouse transcriptional regulatory interactions.

Proper citation: Transcriptional Regulatory Relationships Unrevealed by Sentence based Text mining database (RRID:SCR_022554) Copy   

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https://gitlab.com/rosen-lab/white-adipose-atlas

Single cell atlas of human and mouse white adipose tissue.

Proper citation: White Adipose Atlas (RRID:SCR_023625) Copy   

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http://ww2.sanbi.ac.za/Dbases.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. The STACKdb is knowledgebase generated by processing EST and mRNA sequences obtained from GenBank through a pipeline consisting of masking, clustering, alignment and variation analysis steps. The STACK project aims to generate a comprehensive representation of the sequence of each of the expressed genes in the human genome by extensive processing of gene fragments to make accurate alignments, highlight diversity and provide a carefully joined set of consensus sequences for each gene. The STACK project is comprised of the STACKdb human gene index, a database of virtual human transcripts, as well as stackPACK, the tools used to create the database. STACKdb is organized into 15 tissue-based categories and one disease category. STACK is a tool for detection and visualization of expressed transcript variation in the context of developmental and pathological states. The data system organizes and reconstructs human transcripts from available public data in the context of expression state. The expression state of a transcript can include developmental state, pathological association, site of expression and isoform of expressed transcript. STACK consensus transcripts are reconstructed from clusters that capture and reflect the growing evidence of transcript diversity. The comprehensive capture of transcript variants is achieved by the use of a novel clustering approach that is tolerant of sub-sequence diversity and does not rely on pairwise alignment. This is in contrast with other gene indexing projects. STACK is generated at least four times a year and represents the exhaustive processing of all publicly available human EST data extracted from GenBank. This processed information can be explored through 15 tissue-specific categories, a disease-related category and a whole-body index

Proper citation: Sequence Tag Alignment and Consensus Knowledgebase Database (RRID:SCR_002156) Copy   

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http://www.ibiblio.org/dnam/mainpage.html

This site provides access to mutation databases and software including the human hprt database, Human p53 database, Transgenic lacZ database, and Transgenic lacI database. Other avaialble programs include Mutational spectra comparison and relational database data entry. The most recent hprt database contains information on over 2,300 mutations found in vivo and in vitro in the human hprt gene and runs under Windows. The version for evaluation on this homepage has fewer mutations and is a DOS program. The database contains information on the mutagen, dose, spontaneous and induced mutant fraction, base position, amino acid position, amino acid change, local DNA sequence, cell type, citation, and other items. In addition, information regarding the cause and effect of mutations affecting splicing is given. Routines have been developed for the analysis of single base substitutions. The p53 database contains information on nearly 5,867 mutations found in the human p53 gene. The database itself has been updated in April of 1997. The database contains information on the cancer type, loss of heterozygosity, base position, amino acid position, amino acid change, local DNA sequence,citation, and other items. Routines have been developed for the analysis of single base substitutions. The Transgenic lacZ database contains information on 405 mutations found in vivo in the transgenic lacZ gene. It has last been updated in January of 1998. It provides information on the mutagen, dose, organ, mutant fraction, base position, amino acid position, amino acid change, local DNA sequence, citation, and other items. The Transgenic lacI database contains information on over 1700 mutations found in vivo in the transgenic lacI gene and on nearly 8000 mutations in the lacI gene in native E. coli. The database was updated in January 1998. The database contains information on the mutagen, dose, organ, mutant fraction, base position, amino acid position, amino acid change, local DNA sequence, citation, and other items. Routines have been developed for the analysis of single base substitutions for each of the databases. The software runs only on IBM-compatible PCs.

Proper citation: Neal's DNA Mutation Site (RRID:SCR_002947) Copy   

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http://www.patika.org/

The human pathway database which contains different biological entities and reactions and software tools for analysis. PATIKA Database integrates data from several sources, including Entrez Gene, UniProt, PubChem, GO, IntAct, HPRD, and Reactome. Users can query and access this data using the PATIKAweb query interface. Users can also save their results in XML or export to common picture formats. The BioPAX and SBML exporters can be used as part of this Web service.

Proper citation: Pathway Analysis Tool for Integration and Knowledge Acquisition (RRID:SCR_002100) Copy   

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http://www.loni.ucla.edu/~thompson/thompson.html

The UCLA laboratory of neuroimaging is working in several areas to enhance knowledge of anatomy, including brain mapping in large human populations, HIV, Schizophrenia, methamphetamine, tumor growth and 4d brain mapping, genetics and detection of abnormalities.

Proper citation: University of California at Los Angeles, School of Medicine: Neuro Imaging Lab of Thompson (RRID:SCR_001924) Copy   

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http://humanconnectome.org/connectome/connectomeDB.html

Data management platform that houses all data generated by the Human Connectome Project - image data, clinical evaluations, behavioral data and more. ConnectomeDB stores raw image data, as well as results of analysis and processing pipelines. Using the ConnectomeDB infrastructure, research centers will be also able to manage Connectome-like projects, including data upload and entry, quality control, processing pipelines, and data distribution. ConnectomeDB is designed to be a data-mining tool, that allows users to generate and test hypotheses based on groups of subjects. Using the ConnectomeDB interface, users can easily search, browse and filter large amounts of subject data, and download necessary files for many kinds of analysis. ConnectomeDB is designed to work seamlessly with Connectome Workbench, an interactive, multidimensional visualization platform designed specifically for handling connectivity data. De-identified data within ConnectomeDB is publicly accessible. Access to additional data may be available to qualified research investigators. ConnectomeDB is being hosted on a BlueArc storage platform housed at Washington University through the year 2020. This data platform is based on XNAT, an open-source image informatics software toolkit developed by the NRG at Washington University. ConnectomeDB itself is fully open source.

Proper citation: ConnectomeDB (RRID:SCR_004830) Copy   

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http://dels.nas.edu/ilar

The mission of ILAR is to evaluate and disseminate information on issues related to the scientific, technological, and ethical use of animals and related biological resources in research, testing, and education. Using the principles of refinement, reduction, and replacement (3Rs) as a foundation, ILAR promotes high-quality science through the humane care and use of animals and the implementation of alternatives. Through the reports of expert committees, the ILAR Journal, web-based resources, and other means of communication, ILAR functions as a component of the National Academies to provide independent, objective advice to the federal government, the international biomedical research community, and the public. ILAR supports the responsible use of animals in research, testing, and education as a key component to advancing the health and quality of life of humans and animals. It promotes high-quality science and humane care and use of research animals based upon the principles of refinement, replacement, and reduction (the 3Rs) and high ethical standards. It fosters best practices that enhance human and animal welfare by organizing and disseminating information and by facilitating dialogue among interested parties. It has developed a unique Search Engine to search for animal models and strains. This search engine surveys all the websites of vendors and repositories of laboratory animals and biological material on our Links page. The ILAR develops guidelines on laboratory animal care and use and conducts conferences, symposia, and workshops on important laboratory animal problems. ILAR publishes the ILAR Journal on a quarterly basis, as well as conference proceedings and special reports prepared by committees of experts. A list of ILAR publications on issues related to laboratory animal research is available on the Web site. As part of the Animal Models and Genetic Stocks Information Exchange Program, ILAR staff members answer direct telephone and mail inquiries and maintain a Web page containing a database on animal models and genetic stock. The Web site also offers a comprehensive search engine that enables users to find information on the existence and location of special animal models, correct nomenclature to identify animals, and related topics such as diseases of animals and relevant publications. Sponsors: ILAR receives funding from the following sponsors: -Abbott Laboratories -Abbott Fund -American College of Laboratory Animal Medicine (ACLAM) -American Society of Laboratory Animal Practitioners (ASLAP) -Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) -Bristol-Myers Squibb Co. -Charles River -Charles River Laboratories Foundation -Covance -Federation of American Societies for Experimental Biology (FASEB) -GlaxoSmithKline -Merck & Co., Inc. -National Science Foundation (NSF) -Pfizer -Scientists Center for Animal Welfare (SCAW) -U.S. Department of Agriculture (USDA) -U.S. Department of the Army -U.S. Department of Health and Human Services (DHHS) :*National Institutes of Health (NIH) :*Office of Research Integrity (ORI) -U.S. Department of the Navy -U.S. Department of Veterans Affairs -Wellcome Trust -Wyeth Pharmaceuticals

Proper citation: Institute for Laboratory Animal Research (RRID:SCR_006872) Copy   

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http://www.nimh.nih.gov/news/media/audio/index.shtml

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Audio and video available from the National Institute of Mental Health (NIMH).

Proper citation: NIMH Multimedia (RRID:SCR_005467) Copy   

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http://people.biochem.umass.edu/fournierlab/3dmodmap/

Database of maps showing the sites of modified rRNA nucleotides. Access to the rRNA sequences, secondary structures both with modification sites indicated, 3D modification maps and the supporting tables of equivalent nucleotides for rRNA from model organisms including yeast, arabidopsis, e. coli and human is provided. This database complements the Yeast snoRNA Database at UMass-Amherst and relies on linking to some content from that database, as well as to others by colleagues in related fields. Therefore, please be very cognizant as to the source when citing information obtained herein. Locations of modified rRNA nucleotides within the 3D structure of the ribosome.

Proper citation: 3D Ribosomal Modification Maps Database (RRID:SCR_003097) Copy   

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http://compbio.uthsc.edu/miRSNP/

Database of naturally occurring DNA variations in microRNA (miRNA) seed regions and miRNA target sites. MicroRNAs pair to the transcripts of protein-coding genes and cause translational repression or mRNA destabilization. SNPs and INDELs in miRNAs and their target sites may affect miRNA-mRNA interaction, and hence affect miRNA-mediated gene repression. The PolymiRTS database was created by scanning 3'UTRs of mRNAs in human and mouse for SNPs and INDELs in miRNA target sites. Then, the potential downstream effects of these polymorphisms on gene expression and higher-order phenotypes are identified. Specifically, genes containing PolymiRTSs, cis-acting expression QTLs, and physiological QTLs in mouse and the results of genome-wide association studies (GWAS) of human traits and diseases are linked in the database. The PolymiRTS database also includes polymorphisms in target sites that have been supported by a variety of experimental methods and polymorphisms in miRNA seed regions.

Proper citation: PolymiRTS (RRID:SCR_003389) Copy   

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http://ratmap.gen.gu.se/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone., documented September 2, 2016. Database for defining official rat gene symbols. It includes rat gene symbols from three major sources: the Rat Genome Database (RGD), Ensembl, and NCBI-Gene. All rat symbols are compared with official symbols from orthologous human genes as specified by the Human Gene Nomenclature Committee (HGNC). Based on the outcome of the comparisons, a rat gene symbol may be selected. Rat symbols that do not match a human ortholog undergo a strict procedure of comparisons between the different rat gene sources as well as with the Mouse Genome Database (MGD). For each rat gene this procedure results in an unambiguous gene designation. The designation is presented as a status level that accompanies every rat gene symbol suggested in the database. The status level describes both how a rat symbol was selected, and its validity. Rat Gene Symbol Tracker approves rat gene symbols by an automatic procedure. The rat genes are presented with links to RGD, Ensembl, NCBI Gene, MGI and HGNC. RGST ensures that each acclaimed rat gene symbol is unique and follows the guidelines given by the RGNC. To each symbol a status level associated, describing the gene naming process.

Proper citation: Rat Gene Symbol Tracker (RRID:SCR_003261) Copy   

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http://bioinfo.mbi.ucla.edu/ASAP/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on 8/12/13. Database to access and mine alternative splicing information coming from genomics and proteomics based on genome-wide analyses of alternative splicing in human (30 793 alternative splice relationships found) from detailed alignment of expressed sequences onto the genomic sequence. ASAP provides precise gene exon-intron structure, alternative splicing, tissue specificity of alternative splice forms, and protein isoform sequences resulting from alternative splicing. They developed an automated method for discovering human tissue-specific regulation of alternative splicing through a genome-wide analysis of expressed sequence tags (ESTs), which involves classifying human EST libraries according to tissue categories and Bayesian statistical analysis. They use the UniGene clusters of human Expressed Sequence Tags (ESTs) to identify splices. The UniGene EST's are clustered so that a single cluster roughly corresponds to a gene (or at least a part of a gene). A single EST represents a portion of a processed (already spliced) mRNA. A given cluster contains many ESTs, each representing an outcome of a series of splicing events. The ESTs in UniGene contain the different mRNA isoforms transcribed from an alternatively spliced gene. They are not predicting alternative splicing, but locating it based on EST analysis. The discovered splices are further analyzed to determine alternative splicing events. They have identified 6201 alternative splice relationships in human genes, through a genome-wide analysis of expressed sequence tags (ESTs). Starting with 2.1 million human mRNA and EST sequences, they mapped expressed sequences onto the draft human genome sequence and only accepted splices that obeyed the standard splice site consensus. After constructing a tissue list of 46 human tissues with 2 million human ESTs, they generated a database of novel human alternative splices that is four times larger than our previous report, and used Bayesian statistics to compare the relative abundance of every pair of alternative splices in these tissues. Using several statistical criteria for tissue specificity, they have identified 667 tissue-specific alternative splicing relationships and analyzed their distribution in human tissues. They have validated our results by comparison with independent studies. This genome-wide analysis of tissue specificity of alternative splicing will provide a useful resource to study the tissue-specific functions of transcripts and the association of tissue-specific variants with human diseases.

Proper citation: ASAP: the Alternative Splicing Annotation Project (RRID:SCR_003415) Copy   

•   Source: SciCrunch Registry

http://www.mitomap.org/

Database of polymorphisms and mutations of the human mitochondrial DNA. It reports published and unpublished data on human mitochondrial DNA variation. All data is curated by hand. If you would like to submit published articles to be included in mitomap, please send them the citation and a pdf.

Proper citation: MITOMAP - A human mitochondrial genome database (RRID:SCR_002996) Copy   

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http://www.bionet.umn.edu/tpf/home.html

Procure and distribute human tissue and other biological samples in support of basic, translational, and clinical cancer research at the University of Minnesota. The TPF is a centralized resource with standardized patient consent, sample collection, processing, storage, quality control, distribution, and electronic record maintenance. Since the 1996 inception of the TPF, over 61,000 tissue samples including well-preserved samples of malignant and benign tumors, organ-matched normal tissue, and other types of diseased tissues, have been collected from surgical specimens obtained at the University of Minnesota Medical Center-Fairview (UMMC-F) University Campus. Surgical pathologists are intellectually engaged in TPF functions, providing researchers with specimen-oriented medical consultation to facilitate research productivity. Prior to surgery, TPF personnel identify and consent patients for procurement of tissue, blood, urine, saliva, and ascites fluid. Within the integrated working environment of the surgical pathology laboratory, freshly obtained tissues not needed for diagnosis are selected and provided by pathologists to TPF personnel. Tissue samples are then assigned an independent code and processed. TPF staff can also work with researchers to individualize the procurement of tissues to fit specific research needs.

Proper citation: University of Minnesota Tissue Procurement Facility (RRID:SCR_004270) Copy   

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http://www.nimhans.kar.nic.in/neuropathology/neuropath2.htm#brainbank

A National Facility to promote research in Neurobiology using human nervous tissues. The brain tissues collected with informed consent of close relatives within 4-24 hours following death are frozen for Biochemical, Immuno-histochemical and Molecular Biological studies. A large number of formalin fixed brain tissues from various Neurological, Neurosurgical and Psychiatric disorders are also available for study.

Proper citation: Bangalore Brain Bank (RRID:SCR_004227) Copy   

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http://www.hgsc.bcm.tmc.edu/content/hapmap-3-and-encode-3

Draft release 3 for genome-wide SNP genotyping and targeted sequencing in DNA samples from a variety of human populations (sometimes referred to as the HapMap 3 samples). This release contains the following data: * SNP genotype data generated from 1184 samples, collected using two platforms: the Illumina Human1M (by the Wellcome Trust Sanger Institute) and the Affymetrix SNP 6.0 (by the Broad Institute). Data from the two platforms have been merged for this release. * PCR-based resequencing data (by Baylor College of Medicine Human Genome Sequencing Center) across ten 100-kb regions (collectively referred to as ENCODE 3) in 712 samples. Since this is a draft release, please check this site regularly for updates and new releases. The HapMap 3 sample collection comprises 1,301 samples (including the original 270 samples used in Phase I and II of the International HapMap Project) from 11 populations, listed below alphabetically by their 3-letter labels. Five of the ten ENCODE 3 regions overlap with the HapMap-ENCODE regions; the other five are regions selected at random from the ENCODE target regions (excluding the 10 HapMap-ENCODE regions). All ENCODE 3 regions are 100-kb in size, and are centered within each respective ENCODE region. The HapMap 3 and ENCORE 3 data are downloadable from the ftp site.

Proper citation: HapMap 3 and ENCODE 3 (RRID:SCR_004563) Copy   

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http://www.tbi-impact.org/?p=impact%2Fcalc&btn_calc=GO+TO+CALCULATOR

A calculator that calculates the prediction models for 6 month outcome after Traumatic Brain Injury. Based on extensive prognostic analysis the IMPACT investigators have developed prognostic models for predicting 6 month outcome in adult patients with moderate to severe head injury (Glasgow Coma Scale <=12) on admission. By entering the characteristics into the calculator, the models will provide an estimate of the expected outcome at 6 months. We present three models of increasing complexity (Core, Core + CT, Core + CT + Lab). These models were developed and validated in collaboration with the CRASH trial collaborators on large numbers of individual patient data (the IMPACT database). The models discriminate well, and are particularly suited for purposes of classification and characterization of large cohorts of patients. Extreme caution is required when applying the estimated prognosis to individual patients. The sequential prediction models may be used as an aid to estimate 6 month outcome in patients with severe or moderate traumatic brain injury (TBI). However, the prediction rule can only complement, never replace, clinical judgment and can therefore be used only as a decision-support system.

Proper citation: IMPACT Prognostic Calculator (RRID:SCR_004730) Copy   

•   Source: SciCrunch Registry

http://www.phenologs.org/

Database for identifying orthologous phenotypes (phenologs). Mapping between genotype and phenotype is often non-obvious, complicating prediction of genes underlying specific phenotypes. This problem can be addressed through comparative analyses of phenotypes. We define phenologs based upon overlapping sets of orthologous genes associated with each phenotype. Comparisons of >189,000 human, mouse, yeast, and worm gene-phenotype associations reveal many significant phenologs, including novel non-obvious human disease models. For example, phenologs suggest a yeast model for mammalian angiogenesis defects and an invertebrate model for vertebrate neural tube birth defects. Phenologs thus create a rich framework for comparing mutational phenotypes, identify adaptive reuse of gene systems, and suggest new disease genes. To search for phenologs, go to the basic search page and enter a list of genes in the box provided, using Entrez gene identifiers for mouse/human genes, locus ids for yeast (e.g., YHR200W), or sequence names for worm (e.g., B0205.3). It is expected that this list of genes will all be associated with a particular system, trait, mutational phenotype, or disease. The search will return all identified model organism/human mutational phenotypes that show any overlap with the input set of the genes, ranked according to their hypergeometric probability scores. Clicking on a particular phenolog will result in a list of genes associated with the phenotype, from which potential new candidate genes can identified. Currently known phenotypes in the database are available from the link labeled ''Find phenotypes'', where the associated gene can be submitted as queries, or alternately, can be searched directly from the link provided.

Proper citation: Phenologs (RRID:SCR_005529) Copy   

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http://blog.ketyov.com/

Bradley Voytek''''s blog is where he tries out new ideas. He will often be wrong, but that''''s the point. He is a Neuroscientist studying human cognition, neuroplasticity, and brain computer interfacing. Into really geeky stuff. World zombie neuroscience expert. Also runs brainSCANr.com with his wife, Jessica.

Proper citation: Oscillatory Thoughts (RRID:SCR_005481) Copy   

•   Source: SciCrunch Registry