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https://medschool.cuanschutz.edu/diabetes-research-center

Center to facilitate diabetes research at University of Colorado by integrating interdisciplinary basic, translational, and clinical diabetes research base; providing infrastructure and resources that are indispensable for continued discovery and progress towards diabetes research and developing improved prediction and disease prevention;providing P&F and enrichment programs to support DRC investigators and their trainees, and recruit new and young investigators into diabetes research.

Proper citation: University of Colorado Diabetes Research Center (RRID:SCR_022897) Copy   

•   Source: SciCrunch Registry

https://ncdiabetesresearch.org/

Interactive regional diabetes research community across four premiere research institutions in North Carolina, who currently garner over $70 million annually for support of their diabetes research: Duke University (Duke), The University of North Carolina at Chapel Hill (UNC), Wake Forest School of Medicine (WF), and North Carolina A&T State University (NC A&T State). NCDRC supports Research Cores that represent unique strengths at each institution.

Proper citation: North Carolina Diabetes Research Center (RRID:SCR_022896) Copy   

•   Source: SciCrunch Registry

https://nih-lurn.org/

A research consortium with the long term goal of developing and testing measurement tools to describe symptoms of lower urinary tract dysfunction (LUTD) in women and men. The group plans to study targeted populations of patients with LUTD in order to expand our understanding of the causes of symptoms and common ways that symptoms change over time. The researchers will also collect biosamples from patients for current and future study of LUTD.

Proper citation: Symptoms of Lower Urinary Tract Dysfunction Research Network (LURN) (RRID:SCR_014378) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6301786/

Device to control spatial and temporal variations in oxygen tensions to better replicate in vivo biology. Consists of three parallel connected tissue chambers and oxygen scavenger channel placed adjacent to these tissue chambers. Provides consistent control of spatial and temporal oxygen gradients in tissue microenvironment and can be used to investigate important oxygen dependent biological processes present in cancer, ischemic heart disease, and wound healing.

Proper citation: Microfluidic device to attain high spatial and temporal control of oxygen (RRID:SCR_017131) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.citisletstudy.org/

Network of clinical centers and a data coordinating center established to conduct studies of islet transplantation in patients with type 1 diabetes.

Proper citation: Clinical Islet Transplantation Consortium (CITC) (RRID:SCR_014385) Copy   

•   Source: SciCrunch Registry

http://www.utsouthwestern.edu/labs/acute-liver/

Clinical research network for gathering prospective data and bio-samples on acute liver failure in adults since 1998. Clinical histories and laboratory and outcome data are available. Sample types include serum, plasma, urine, DNA, and liver tissue.

Proper citation: Acute Liver Failure Study Group (RRID:SCR_001463) Copy   

•   Source: SciCrunch Registry

http://www.uchicagoddrcc.org/research-cores/tissue-engineering-and-cell-models-core

Core that provides services such as a repository for intestinal cell lines, Tissue Engineering Models, experimental materials, and supplies for digestive disease research.

Proper citation: University of Chicago Digestive Diseases Research Core Center Tissue Engineering and Cell Models Core (RRID:SCR_015604) Copy   

•   Source: SciCrunch Registry

https://github.com/EpistasisLab/ReBATE

Open source software Python package to compare relief based feature selection algorithms used in data mining. Used for feature selection in any bioinformatics problem with potentially predictive features and target outcome variable, to detect feature interactions without examination of all feature combinations, to detect features involved in heterogeneous patterns of association such as genetic heterogeneity .

Proper citation: ReBATE (RRID:SCR_017139) Copy   

•   Source: SciCrunch Registry

https://github.com/zeyang-shen/maggie

Software Python package for identifying motifs mediating transcription factor binding and function. Links mutations of motif to changes of epigenomic feature without assuming linear relationship.

Proper citation: Motif Alteration Genome wide to Globally Investigate Elements (RRID:SCR_021903) Copy   

•   Source: SciCrunch Registry

https://spin.niddk.nih.gov/bax/software/TALOS-N/

Software package for prediction of protein backbone and sidechain torsion angles from NMR chemical shifts.

Proper citation: TALOS-N (RRID:SCR_022800) Copy   

•   Source: SciCrunch Registry

https://github.com/qianli10000/mtradeR

Software R package implements Joint model with Matching and Regularization and simulation pipeline. Used to test association between taxa and disease risk, and adjusted for correlated taxa screened by pre-selection procedure in abundance and prevalence, individually.

Proper citation: mtradeR (RRID:SCR_022977) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/Maaslin2.html

SoftwareR package that identifies microbial taxa correlated with factors of interest using generalized linear models and mixed models.Used for efficiently determining multivariable association between clinical metadata and microbial meta'omic features.

Proper citation: MaAsLin2 (RRID:SCR_023241) Copy   

•   Source: SciCrunch Registry

https://github.com/ParkerLab/ataqv

Software package for QC and visualization of ATAC-seq results. Used to examine aligned reads and report basic metrics, including reads mapped in proper pairs, optical or PCR duplicates, reads mapping to autosomal or mitochondrial references, ratio of short to mononucleosomal fragment counts, mapping quality, various kinds of problematic alignments.

Proper citation: ataqv (RRID:SCR_023112) Copy   

•   Source: SciCrunch Registry

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

http://www.cristudy.org/Chronic-Kidney-Disease/Chronic-Renal-Insufficiency-Cohort-Study/

A prospective observational national cohort study poised to make fundamental insights into the epidemiology, management, and outcomes of chronic kidney disease (CKD) in adults with intended long-term follow up. The major goals of the CRIC Study are to answer two important questions: * Why does kidney disease get worse in some people, but not in others? * Why do persons with kidney disease commonly experience heart disease and stroke? The CRIC Scientific and Data Coordinating Center at Penn receives data and provides ongoing support for a number of Ancillary Studies approved by the CRIC Cohort utilizing both data collected about CRIC study participants as well as their biological samples. The CRIC Study has enrolled over 3900 men and women with CKD from 13 recruitment sites throughout the country. Following this group of individuals over the past 10 years has contributed to the knowledge of kidney disease, its treatment, and preventing its complications. The NIDDKwill be extending the study for an additional 5 years, through 2018. An extensive set of study data is collected from CRIC Study participants. With varying frequency, data are collected in the domains of medical history, physical measures, psychometrics and behaviors, biomarkers, genomics/metabolomics, as well as renal, cardiovascular and other outcomes. Measurements include creatinine clearance and iothalamate measured glomerular filtration rate. Cardiovascular measures include blood pressure, ECG, ABI, ECHO, and EBCT. Clinical CV outcomes include MI, ischemic heart disease-related death, acute coronary syndromes, congestive heart failure, cerebrovascular disease, peripheral vascular disease, and composite outcomes. The CRIC Study has delivered in excess of 150,000 bio-samples and a dataset characterizing all 3939 CRIC participants at the time of study entry to the NIDDKnational repository. The CRIC Study will also be delivering a dataset to NCBI''''s Database for Genotypes and Phenotypes.

Proper citation: Chronic Renal Insufficiency Cohort Study (RRID:SCR_009016) Copy   

•   Source: SciCrunch Registry

https://www.t1dgc.org/

Data and biological samples were collected by this consortium organizing international efforts to identify genes that determine an individual risk of type 1 diabetes. It originally focused on recruiting families with at least two siblings (brothers and/or sisters) who have type 1 diabetes (affected sibling pair or ASP families). The T1DGC completed enrollment for these families in August 2009. They completed enrollment of trios (father, mother, and a child with type 1 diabetes), as well as cases (people with type 1 diabetes) and controls (people with no history of type 1 diabetes) from populations with a low prevalence of this disease in January 2010. T1DGC Data and Samples: Phenotypic and genotypic data as well as biological samples (DNA, serum and plasma) for T1DGC participants have been deposited in the NIDDKCentral Repositories for future research.

Proper citation: Type 1 Diabetes Genetics Consortium (RRID:SCR_001557) Copy   

•   Source: SciCrunch Registry

http://iscc.coh.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Consortium to advance the understanding of intestinal epithelial stem cell biology during development, homeostasis, regeneration and disease. Its immediate goals are to isolate, characterize, culture and validate populations of intestinal stem cells; answer major questions in stem cell biology of the intestinal epithelium; and accelerate research by making information and resources available to the research community. Resources include data sets, protocols, and a resource catalog. Long-term goals include: 1) laying the ground work for therapeutic manipulation of the intestinal epithelium 2) contributing to the greater understanding of stem cell biology through knowledge of the intestine as a model stem cell-driven system. Research Projects are housed at 8 institutions across the nation: Oregon Health & Science University, Stanford University, Stowers Institute for Medical Research, University of California, Los Angeles School of Medicine (UCLA) (partnered with the VA Greater Los Angeles), University of North Carolina, Chapel Hill (UNC), University of Oklahoma, University of Pennsylvania, and University of Pittsburgh.

Proper citation: Intestinal Stem Cell Consortium (RRID:SCR_001555) Copy   

•   Source: SciCrunch Registry

http://type1diabetes.jax.org/

International repository for importation, curation, genotypic and phenotypic validation, cryopreservation, and distribution of mouse stocks of value to the type 1 diabetes scientific community holding over 250 genetically modified or congenic mouse stocks that are being used to dissect genetic and biologic features of T1D. They provide extensive genotypic and phenotypic quality control and genetic stabilization for these strains, as well as incidence studies when available. An added value of T1DR stocks is their ability to propel advances in related areas of science, including research in non-T1D autoimmunity and infectious diseases. The staff provides information and technical assistance regarding selection and use of existing T1DR models, and will provide limited support for development of new models considered to be of high-value for the T1D community. The resource includes strains generated at the Jackson Laboratory as well as strains donated by external scientists. Investigators are highly encouraged to donate a strain to ensure its preservation and availability to other researchers.

Proper citation: Type 1 Diabetes Resource (RRID:SCR_001475) Copy   

•   Source: SciCrunch Registry