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Repository of biospecimen and phenotype data collected from Crohn's disease and ulcerative colitis cases and controls recruited at six sites throughout North America that are available to the scientific community. Phenotyping is performed using a standardized protocol, and lymphoblastoid cell lines are established for each subject. Phenotype data for each subject are collected by the Consortium's Data Coordinating Center (DCC), and phenotype data for all subjects with DNA samples are available. The resulting DNA samples have already been utilized by the Consortium to complete various association studies, including genome-wide association studies using dense genotyping arrays. Researchers can obtain DNA samples and phenotype, genotype, and pedigree data through the Data Repository. GWAS data must be requested through dbGAP. The IBDGC is involved with independent genetic research studies and actively works with members of the IBD and genetic communities on collaborative projects. They are also members of the International IBD Genetics Consortium. Phenotype Tools: The Consortium Phenotype Committee, led by Dr. Hillary Steinhart designed and validated paper forms to collect extensive phenotype data on Crohn's Disease and ulcerative colitis. Consortium phenotype tools are available for use by non-Consortium members.
Proper citation: NIDDK Inflammatory Bowel Disease Genetics Consortium (RRID:SCR_001461) Copy
Group of 10 academic laboratories provide pancreatic islets of cGMP-quality to eligible investigators for use in FDA approved, IRB-approved transplantation protocols in which isolated human islets are transplanted into qualified patients afflicted with type 1 diabetes mellitus; optimize the harvest, purification, function, storage, and shipment of islets while developing tests that characterize the quality and predict the effectiveness of islets transplanted into patients with diabetes mellitus; and provide pancreatic islets for basic science studies. The centers are electronically linked through an Administrative and Bioinformatics Coordinating Center (ABCC). The ABCC manages a system with objectively defined criteria that establishes the order of priority for islet distribution. It also provides database and other informatics to track the utilization of pancreata and all distributed clinical grade islets for transplant and basic research, and supports the Islet Cell Resource Centers Consortium so that the research community has a single entry point to the program. Qualified researchers from domestic institutions may request islets by submitting a written application to the director of the ABCC. The ICRs will distribute Islets as appropriate for either clinical or basic science protocol use to eligible investigators who have received a favorable review and subsequent approval by the ICR Steering Committee (SC). The Administrative and Bioinformatics Coordinating Center (ABCC) manages the distribution according to a priority list. The ABCC will give preference to investigators who have peer-reviewed, NIH-funded research support.
Proper citation: Islet Cell Resource Centers (RRID:SCR_002806) Copy
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 on August 1, 2015. Consortium that aims to facilitate interdisciplinary collaborations to advance the understanding of pancreatic islet development and function, with the goal of developing innovative therapies to correct the loss of beta cell mass in diabetes, including cell reprogramming, regeneration and replacement. They are responsible for collaboratively generating the necessary reagents, mouse strains, antibodies, assays, protocols, technologies and validation assays that are beyond the scope of any single research effort. The scientific goals for the BCBC are to: * Use cues from pancreatic development to directly differentiate pancreatic beta cells and islets from stem / progenitor cells for use in cell-replacement therapies for diabetes, * Determine how to stimulate beta cell regeneration in the adult pancreas as a basis for improving beta cell mass in diabetic patients, * Determine how to reprogram progenitor / adult cells into pancreatic beta-cells both in-vitro and in-vivo as a mean for developing cell-replacement therapies for diabetes, and * Investigate the progression of human type-1 diabetes using patient-derived cells and tissues transplanted in humanized mouse models. Many of the BCBC investigator-initiated projects involve reagent-generating activities that will benefit the larger scientific community. The combination of programs and activities should accelerate the pace of major new discoveries and progress within the field of beta cell biology.
Proper citation: Beta Cell Biology Consortium (RRID:SCR_005136) Copy
https://github.com/FunctionalUrology/MLme
Software toolkit for Machine Learning Driven Data Analysis. Simplifies machine learning for data exploration, visualization and analysis.
Proper citation: Machine Learning Made Easy (RRID:SCR_024439) Copy
Consortium serving the diabetic complications community that sponsors annual meetings in complications-relevant scientific areas, solicits and funds pilot projects in high impact areas of complications research, and provides resources and data including animal models, protocols and methods, validation criteria, reagents and resources, histology, publications and bioinformatics for researchers conducting diabetic complications research.
Proper citation: Diabetic Complications Consortium (RRID:SCR_001415) Copy
http://chgr.mc.vanderbilt.edu/page/gist
Software package to test if a marker can account in part for the linkage signal in its region. There are two versions of the software: Windows and Linux/Unix.
Proper citation: Genotype-IBD Sharing Test (RRID:SCR_006257) Copy
http://digestive.niddk.nih.gov/statistics/statistics.aspx
A collection of statistics about specific digestive diseases, including prevalence, mortality, care delivery and cost.
Proper citation: Digestive Diseases Statistics for the United States (RRID:SCR_006703) Copy
https://maayanlab.cloud/sigcom-lincs
Web server that serves over million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. Data and metadata search engine for gene expression signatures.
Proper citation: SigCom LINCS (RRID:SCR_022275) Copy
https://github.com/zdk123/SpiecEasi
Software R package for microbiome network analysis. Used for inference of microbial ecological networks from amplicon sequencing datasets. Combines data transformations developed for compositional data analysis with graphical model inference framework that assumes underlying ecological association network is sparse.
Proper citation: SpiecEasi (RRID:SCR_022712) Copy
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
Center whose goals include fostering collaboration among basic and clinical investigators, facilitating the use of new technologies in the study of treatment of digestive diseases, and providing education and training for improved treatment and diagnosis.
Proper citation: University of Chicago Digestive Diseases Research Core Center (RRID:SCR_015601) Copy
http://www.bsc.gwu.edu/dpp/index.htmlvdoc
Multicenter clinical research study aimed at discovering whether modest weight loss through dietary changes and increased physical activity or treatment with the oral diabetes drug metformin (Glucophage) could prevent or delay the onset of type 2 diabetes in study participants. At the beginning of the DPP, all 3,234 study participants were overweight and had blood glucose levels higher than normal but not high enough for a diagnosis of diabetesa condition called prediabetes. In addition, 45 percent of the participants were from minority groups-African American, Alaska Native, American Indian, Asian American, Hispanic/Latino, or Pacific Islander-at increased risk of developing diabetes. The DPP found that participants who lost a modest amount of weight through dietary changes and increased physical activity sharply reduced their chances of developing diabetes. Taking metformin also reduced risk, although less dramatically. In the DPP, participants from 27 clinical centers around the United States were randomly divided into different treatment groups. The first group, called the lifestyle intervention group, received intensive training in diet, physical activity, and behavior modification. By eating less fat and fewer calories and exercising for a total of 150 minutes a week, they aimed to lose 7 percent of their body weight and maintain that loss. The second group took 850 mg of metformin twice a day. The third group received placebo pills instead of metformin. The metformin and placebo groups also received information about diet and exercise but no intensive motivational counseling. A fourth group was treated with the drug troglitazone (Rezulin), but this part of the study was discontinued after researchers discovered that troglitazone can cause serious liver damage. The participants in this group were followed but not included as one of the intervention groups. In the years since the DPP was completed, further analyses of DPP data continue to yield important insights into the value of lifestyle changes in helping people prevent type 2 diabetes and associated conditions. For example, one analysis confirmed that DPP participants carrying two copies of a gene variant, or mutation, that significantly increased their risk of developing diabetes benefited from lifestyle changes as much as or more than those without the gene variant. Another analysis found that weight loss was the main predictor of reduced risk for developing diabetes in DPP lifestyle intervention group participants. The authors concluded that diabetes risk reduction efforts should focus on weight loss, which is helped by increased exercise.
Proper citation: Diabetes Prevention Program (RRID:SCR_001501) Copy
Resource enables integrative exploration of genetic and epigenetic basis of development of Type 2 Diabetes, together with other associated functional, molecular and clinical data, centered in biology and role of pancreatic beta cells.The gene expression regulatory variation landscape of human pancreatic islets.
Proper citation: TIGER Data Portal (RRID:SCR_023626) Copy
Portal for research on urinary stones in adults and children in order to learn more about who forms kidney stones, treatments and prevention. Network comprises of experts including adult and pediatric urologists, adult and pediatric nephrologists, pediatricians, emergency department physicians, clinical trialists, nutritionists, behavioral scientists, and radiologists. Duke Clinical Research Institute is Scientific Data Research Center and with clinical sites including University of Pennsylvania Children Hospital of Philadelfia, University of Texas Southwestern Medical Center, University of Washington, Washington University in St. Louis, work together in planning, executing, and analyzing results from USDRN studies.
Proper citation: Urinary Stone Disease Research Network (RRID:SCR_019059) Copy
https://github.com/vlink/marge
Software package that integrates genome wide genetic variation with epigenetic data to identify collaborative transcription factor pairs. Optimized to work with chromatin accessibility assays such as ATAC-seq or DNase I hypersensitivity, as well as transcription factor binding data collected by ChIP-seq. Used to identify combinations of cell type specific transcription factors while simultaneously interpreting functional effects of non-coding genetic variation.
Proper citation: Motif Mutation Analysis for Regulatory Genomic Elements (RRID:SCR_021902) Copy
Functional gene pipeline and repository. Functional gene repository provides collections of genes in interactive platform, while functional gene pipeline offers suite of tools for functional gene amplicon processing and analysis. Together they enable key steps in functional gene based microbial community analysis, from target selection and primer analysis to amplicon processing and ecological discovery.
Proper citation: FunGene (RRID:SCR_018749) Copy
http://diabeticfootconsortium.org/
Group of academic institutions committed to studying diabetic foot conditions, such as foot ulcers and wound healing, to develop predictive biomarkers which can be later used to create better treatment plans and improve health and quality of life for people living with diabetes.
Proper citation: Diabetic Foot Consortium (RRID:SCR_018914) Copy
Software tool as data and metadata repository of Extracellular RNA Communication Consortium. Atlas includes small RNA sequencing and qPCR derived exRNA profiles from human and mouse biofluids. All RNAseq datasets are processed using version 4 of exceRpt small RNAseq pipeline. Atlas accepts submissions for RNAseq or qPCR data.
Proper citation: exRNA Atlas (RRID:SCR_017221) Copy
http://grantome.com/grant/NIH/U01-DK099919-04S1
Consortium to design and conduct pilot and feasibility studies of novel therapies to reduce morbidity and mortality for patients treated with maintenance hemodialysis. Data Coordinating Center (DCC) for consortium provides scientific expertise and operational support for pilot studies that will be conducted at HDPSC Participating Clinical Centers. Data Coordinating Center for Hemodialysis Pilot Studies Consortium.
Proper citation: Hemodialysis Pilot Studies Consortium (RRID:SCR_017468) Copy
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
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