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https://www.signalingpathways.org/ominer/query.jsf

THIS RESOURCE IS NO LONGER IN SERVICE.Documented on February 25, 2022.Software tool as knowledge environment resource that accrues, develops, and communicates information that advances understanding of structure, function, and role in disease of nuclear receptors (NRs) and coregulators. It specifically seeks to elucidate roles played by NRs and coregulators in metabolism and development of metabolic disorders. Includes large validated data sets, access to reagents, new findings, library of annotated prior publications in field, and journal covering reviews and techniques.As of March 20, 2020, NURSA is succeeded by the Signaling Pathways Project (SPP).

Proper citation: Nuclear Receptor Signaling Atlas (RRID:SCR_003287) Copy   

•   Source: SciCrunch Registry

https://github.com/SciCrunch/Antibody-Watch

Text mining antibody specificity from literature. Helps researchers identify potential problems with antibody specificity. By mining the scientific literature and linking findings to Research Resource Identifiers (RRIDs), it provides alerts on antibodies that may yield unreliable results, supporting reproducibility in biomedical research.

Proper citation: Antibody Watch (RRID:SCR_027424) Copy   

•   Source: SciCrunch Registry

https://pancreatlas.org/

Collection of human pancreas data and images. Platform to share data from human pancreas samples. Houses reference datasets from human pancreas samples, achieved through generosity of organ donors and their families.

Proper citation: Pancreatlas (RRID:SCR_018567) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

https://github.com/BodenmillerGroup/imctools

Software Python package that implements preprocessing pipeline for imaging mass cytometry data. Can convert IMC raw files to tiff files that are used as inputs into CellProfiller, Ilastik, Fiji etc.

Proper citation: imctools (RRID:SCR_017132) Copy   

•   Source: SciCrunch Registry

https://huttenhower.sph.harvard.edu/picrust/

Software for predicting functional abundances based only on marker gene sequences.Used for prediction of metagenome functions. Contains updated and larger database of gene families and reference genomes, provides interoperability with any operational taxonomic unit (OTU)-picking or denoising algorithm, and enables phenotype predictions. Allows addition of custom reference databases.

Proper citation: PICRUSt2 (RRID:SCR_022647) Copy   

•   Source: SciCrunch Registry

https://picrust.github.io/picrust/

Software package to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes. Used to predict which gene families are present and then combines gene families to estimate the composite metagenome.

Proper citation: PICRUSt (RRID:SCR_016855) Copy   

•   Source: SciCrunch Registry

http://genespeed.ccf.org/home/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. Database and customized tools to study the PFAM protein domain content of the transcriptome for all expressed genes of Homo sapiens, Mus musculus, Drosophila melanogaster, and Caenorhabditis elegans tethered to both a genomics array repository database and a range of external information resources. GeneSpeed has merged information from several existing data sets including the Gene Ontology Consortium, InterPro, Pfam, Unigene, as well as micro-array datasets. GeneSpeed is a database of PFAM domain homology contained within Unigene. Because Unigene is a non-redundant dbEST database, this provides a wide encompassing overview of the domain content of the expressed transcriptome. We have structured the GeneSpeed Database to include a rich toolset allowing the investigator to study all domain homology, no matter how remote. As a result, homology cutoff score decisions are determined by the scientist, not by a computer algorithm. This quality is one of the novel defining features of the GeneSpeed database giving the user complete control of database content. In addition to a domain content toolset, GeneSpeed provides an assortment of links to external databases, a unique and manually curated Transcription Factor Classification list, as well as links to our newly evolving GeneSpeed BetaCell Database. GeneSpeed BetaCell is a micro-array depository combined with custom array analysis tools created with an emphasis around the meta analysis of developmental time series micro-array datasets and their significance in pancreatic beta cells.

Proper citation: GeneSpeed- A Database of Unigene Domain Organization (RRID:SCR_002779) Copy   

•   Source: SciCrunch Registry

http://www.cbil.upenn.edu/RAD

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on March 24, 2014. A resource for gene expression studies, storing highly curated MIAME-compliant studies (i.e. experiments) employing a variety of technologies such as filter arrays, 2-channel microarrays, Affymetrix chips, SAGE, MPSS and RT-PCR. Data were available for querying and downloading based on the MGED ontology, publications or genes. Both public and private studies (the latter viewable only by users having appropriate logins and permissions) were available from this website. Specific details on protocols, biomaterials, study designs, etc., are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format. RAD is part of a more general Genomics Unified Schema (http://gusdb.org), which includes a richly annotated gene index (http://allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. NOTE: Due to changes in technology and funding, the RAD website is no longer available. RAD as a schema is still very much active and incorporated in the GUS (Genomics Unified Schema) database system used by CBIL (EuPathDB, Beta Cell Genomics) and others. The schema for RAD can be viewed along with the other GUS namespaces through our Schema Browser.

Proper citation: RNA Abundance Database (RRID:SCR_002771) Copy   

•   Source: SciCrunch Registry

http://www2.niddk.nih.gov/Research/Resources/ObesityResources.htm

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 23, 2017. This website contains resources for obesity researchers including: Obesity Databases, Registries and Information; Obesity Multicenter Clinical Research; Obesity Basic Research Networks; Obesity Reagents; Obesity Services; Obesity Standardization Programs; Obesity Tissues, Cells, Animals; Obesity Useful Tools.

Proper citation: NIDDK- National Institute of Diabetes and Digestive and Kidney Diseases Obesity Resources (RRID:SCR_003074) Copy   

•   Source: SciCrunch Registry

http://www.mybiosoftware.com/population-genetics/332

A tool for SNP Search and downloading with local management. It also offers flanking sequence downloading and automatic SNP filtering. It requires Windows and .NET Framework.

Proper citation: SNPHunter (RRID:SCR_002968) Copy   

•   Source: SciCrunch Registry

http://toppgene.cchmc.org/

ToppGene Suite is a one-stop portal for gene list enrichment analysis and candidate gene prioritization based on functional annotations and protein interactions network. ToppGene Suite is a one-stop portal for (i) gene list functional enrichment, (ii) candidate gene prioritization using either functional annotations or network analysis and (iii) identification and prioritization of novel disease candidate genes in the interactome. Functional annotation-based disease candidate gene prioritization uses a fuzzy-based similarity measure to compute the similarity between any two genes based on semantic annotations. The similarity scores from individual features are combined into an overall score using statistical meta-analysis.

Proper citation: ToppGene Suite (RRID:SCR_005726) Copy   

•   Source: SciCrunch Registry

http://www.cure.med.ucla.edu/

Center whose interests and activities encompass several facets of gastrointestinal regulatory physiology and cell biology. It provides an infrastructure to support basic, translational and clinical research and to facilitate interdisciplinary research and training activities in digestive diseases.

Proper citation: CURE - Digestive Diseases Research Center (RRID:SCR_004238) Copy   

•   Source: SciCrunch Registry

http://dkcoin.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented October 13, 2014. The resource has moved to the NIDDKInformation Network (dkNET) project. Contact them at info_at_dknet.org with any questions. Database of large pools of data relevant to the mission of NIDDKwith the goal of developing a community-based network for integration across disciplines to include the larger DKuniverse of diseases, investigators, and potential users. The focus is on greater use of this data with the objective of adding value by breaking down barriers between sites to facilitate linking of different datasets. To date (2013/06/10), a total of 1,195 resources have been associated with one or more genes. Of 11,580 total genes associated with resources, the ten most represented are associated with 359 distinct resources. The main method by which they currently interconnect resources between the providers is via EntrezGene identifiers. A total of 780 unique genes provide the connectivity between 3,159 resource pairs across consortia. To further increase interconnectivity, the groups have been further annotating their data with additional gene identifiers, publications, and ontology terms from selected Open Biological and Biomedical Ontologies (OBO).

Proper citation: dkCOIN (RRID:SCR_004438) Copy   

•   Source: SciCrunch Registry

http://www.autoimmunitycenters.org/

Nine centers that conduct clinical trials and basic research on new immune-based therapies for autoimmune diseases. This program enhances interactions between scientists and clinicians in order to accelerate the translation of research findings into medical applications. By promoting better coordination and communication, and enabling limited resources to be pooled, ACEs is one of NIAID''''s primary vehicles for both expanding our knowledge and improving our ability to effectively prevent and treat autoimmune diseases. This coordinated approach incorporates key recommendations of the NIH Autoimmune Diseases Research Plan and will ensure progress in identifying new and highly effective therapies for autoimmune diseases. ACEs is advancing the search for effective treatments through: * Diverse Autoimmunity Expertise Medical researchers at ACEs include rheumatologists, neurologists, gastroenterologists, and endocrinologists who are among the elite in their respective fields. * Strong Mechanistic Foundation ACEs augment each clinical trial with extensive basic studies designed to enhance understanding of the mechanisms responsible for tolerance initiation, maintenance, or loss, including the role of cytokines, regulatory T cells, and accessory cells, to name a few. * Streamlined Patient Recruitment The cooperative nature of ACEs helps scientists recruit patients from distinct geographical areas. The rigorous clinical and basic science approach of ACEs helps maintain a high level of treatment and analysis, enabling informative comparisons between patient groups.

Proper citation: Autoimmunity Centers of Excellence (RRID:SCR_006510) Copy   

•   Source: SciCrunch Registry

https://repository.niddk.nih.gov/home/

NIDDK Central Repositories are two separate contract funded components that work together to store data and samples from significant, NIDDK funded studies. First component is Biorepository that gathers, stores, and distributes biological samples from studies. Biorepository works with investigators in new and ongoing studies as realtime storage facility for archival samples.Second component is Data Repository that gathers, stores and distributes incremental or finished datasets from NIDDK funded studies Data Repository helps active data coordinating centers prepare databases and incremental datasets for archiving and for carrying out restricted queries of stored databases. Data Repository serves as Data Coordinating Center and website manager for NIDDK Central Repositories website.

Proper citation: NIDDK Central Repository (RRID:SCR_006542) Copy   

•   Source: SciCrunch Registry

http://www.nkdep.nih.gov/lab-evaluation/gfr-calculators.shtml

Glomerular Filtration Rate (GFR) calculators to estimate kidney function for adults (MDRD GFR Calculator) and children (Schwartz GFR Calculator). In adults, the recommended equation for estimating glomerular filtration rate (GFR) from serum creatinine is the Modification of Diet in Renal Disease (MDRD) Study equation. The IDMS-traceable version of the MDRD Study equation is used. Currently the best equation for estimating glomerular filtration rate (GFR) from serum creatinine in children is the Bedside Schwartz equation for use with creatinine methods with calibration traceable to IDMS. Using the original Schwartz equation with a creatinine value from a method with calibration traceable to IDMS will overestimate GFR.

Proper citation: Glomerular Filtration Rate Calculators (RRID:SCR_006443) Copy   

•   Source: SciCrunch Registry

http://www.nkdep.nih.gov/lab-evaluation/gfr/creatinine-standardization.shtml

Standard specification to reduce inter-laboratory variation in creatinine assay calibration and therefore enable more accurate estimates of glomerular filtration rate (eGFR). Created by NKDEP''''s Laboratory Working Group in collaboration with the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) and the European Communities Confederation of Clinical Chemistry (now called the European Federation of Clinical Chemistry and Laboratory Medicine), the effort is part of a larger NKDEP initiative to help health care providers better identify and treat chronic kidney disease in order to prevent or delay kidney failure and improve patient outcomes. Recommendations are intended for the USA and other countries or regions that have largely completed standardization of creatinine calibration to be traceable to an isotope dilution mass spectrometry (IDMS) reference measurement procedure. The program''''s focus is to facilitate the sharing of information to assist in vitro diagnostic manufacturers, clinical laboratories, and others in the laboratory community with calibrating their serum creatinine measurement procedures to be traceable to isotope dilution mass spectrometry (IDMS). The program also supports manufacturers'''' efforts to encourage their customers in the laboratory to coordinate use of standardized creatinine methods with implementation of a revised GFR estimating equation appropriate for use with standardized creatinine methods. Communication resources and other information for various segments of the laboratory community are available in the Creatinine Standardization Recommendations section of the website. Also available is a protocol for calibrating creatinine measurements using whole blood devices. The National Institute for Standards and Technology (NIST) released a standard reference material (SRM 967 Creatinine in Frozen Human Serum) for use in establishing calibrations for routine creatinine measurement procedures. SRM 967 was validated to be commutable with native serum samples for many routine creatinine procedures and is useful to establish or verify traceability to an IDMS reference measurement procedure. Establishing calibrations for serum creatinine methods using SRM 967 not only provides a mechanism for ensuring more accurate measurement of serum creatinine, but also enables more accurate estimates of GFR. For clinical laboratories interested in independently checking the calibration supplied by their creatinine reagent suppliers/manufacturers, periodic measurement of NIST SRM 967 should be considered for inclusion in the lab''''s internal quality assurance program. To learn more about SRM 967, including how to purchase it, visit the NIST website, https://www-s.nist.gov/srmors/quickSearch.cfm

Proper citation: Creatinine Standardization Program (RRID:SCR_006441) Copy   

•   Source: SciCrunch Registry

http://www.usrds.org/

Annual report, standard analysis files and an online query system from the national data registry on the end-stage renal disease (ESRD) population in the U.S., including treatments and outcomes. The Annual Data Report is divided into two parts. The Atlas section displays data using graphs and charts. Specific chapters address trends in ESRD patient populations, quality of ESRD care, kidney transplantation outcomes, costs of ESRD care, Healthy People 2010 objectives, chronic kidney disease, pediatric ESRD, and cardiovascular disease special studies. The Reference Tables are devoted entirely to the ESRD population. The RenDER (Renal Data Extraction and Referencing) online data query system allows users to build data tables and maps for the ESRD population. National, state, and county level data are available. USRDS staff collaborates with members of Centers for Medicare & Medicaid Services (CMS), the United Network for Organ Sharing (UNOS), and the ESRD networks, sharing datasets and actively working to improve the accuracy of ESRD patient information.

Proper citation: United States Renal Data System (RRID:SCR_006699) Copy   

•   Source: SciCrunch Registry

http://rdp.cme.msu.edu

A database which provides ribosome related data services to the scientific community, including online data analysis, rRNA derived phylogenetic trees, and aligned and annotated rRNA sequences. It specifically contains information on quality-controlled, aligned and annotated bacterial and archaean 16S rRNA sequences, fungal 28S rRNA sequences, and a suite of analysis tools for the scientific community. Most of the RDP tools are now available as open source packages for users to incorporate in their local workflow.

Proper citation: Ribosomal Database Project (RRID:SCR_006633) Copy   

•   Source: SciCrunch Registry