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http://www.stanford.edu/group/exonarray/cgi-bin/plot_selector.pl

Transcriptome database of acutely isolated purified astrocytes, neurons, and oligodendrocytes. Provides improved cell-type-specific markers for better understanding of neural development, function, and disease.

Proper citation: Exon Array Browser (RRID:SCR_008712) Copy   

•   Source: SciCrunch Registry
  

http://clonesearch.jdrfnpod.org/

Database of sequence data generated from high-throughput immunosequencing of the TCR beta chain (TRB) and B cell receptor (BCR) immunoglobulin heavy chain (IGH). This data comes from cells from NPOD donors.

Proper citation: nPOD TCR/BCR Search (RRID:SCR_015851) 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   

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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   

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http://toppcluster.cchmc.org/

A tool for performing multi-cluster gene functional enrichment analyses on large scale data (microarray experiments with many time-points, cell-types, tissue-types, etc.). It facilitates co-analysis of multiple gene lists and yields as output a rich functional map showing the shared and list-specific functional features. The output can be visualized in tabular, heatmap or network formats using built-in options as well as third-party software. It uses the hypergeometric test to obtain functional enrichment achieved via the gene list enrichment analysis option available in ToppGene.

Proper citation: ToppCluster (RRID:SCR_001503) Copy   

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http://www.digestive.niddk.nih.gov

Information dissemination service of the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) established to increase knowledge and understanding about digestive diseases among people with these conditions and their families, health care professionals, and the general public: online, in booklets and fact sheets, by email, and over the phone. To carry out this mission, NDDIC works closely with a coordinating panel of representatives from Federal agencies, voluntary organizations on the national level, and professional groups to identify and respond to informational needs about digestive diseases. NDDIC provides the following informational products and services: * Response to inquiries about digestive diseases - ranging from information about available patient and professional education materials to statistical data. By phone (8:30 a.m. to 5 p.m. eastern time, M-F), fax, mail, and email. * Publications about specific digestive diseases, provided free of copyright, in varying reading levels. Available online or as booklets and brochures. NDDIC also sends publications to health fairs and community events. * Referrals to health professionals through the National Library of Medicine''''s MEDLINEplus includes a consumer-friendly listing of organizations that will assist you in your search for physicians and other health professionals. * Exhibits at professional meetings specific to digestive diseases, as well as cross-cutting professional meetings. NDDIC exhibits at nine professional meetings each year, including Digestive Diseases Week, American College of Gastroenterology, Society of Gastroenterology Nurses and Associates, American Academy of Family Physicians, American Academy of Physician Assistants, American Nurses Association, and the National Conference for Nurse Practitioners.

Proper citation: National Digestive Diseases Information Clearinghouse (RRID:SCR_006771) 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
  

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   

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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   

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Ratings or validation data are available for this resource

http://iidp.coh.org/Default.aspx

The goal of the Integrated Islet Distribution Program (IIDP) is to work with the leading islet isolation centers in the U.S. to distribute high quality human islets to the diabetes research community, in order to advance scientific discoveries and translational medicine.

Proper citation: Integrated Islet Distribution Program (IIDP) (RRID:SCR_014387) Copy   

•   Source: SciCrunch Registry
  

http://www.diabetes-translation.org

Centers that are part of an integrated program whose cores support and enhance diabetes type II translation research. The CDTRs aim to enhance the efficiency, productivity, effectiveness and multidisciplinary nature of diabetes translation research.

Proper citation: Centers for Diabetes Translation Research (RRID:SCR_015149) Copy   

•   Source: SciCrunch Registry
  

http://globalprojects.ucsf.edu/project/novel-small-molecule-therapies-cystic-fibrosis

Research center that focuses on developing novel therapies for cystic fibrosis, enhancing research projects examining the mechanisms of the disease, and developing new small-molecule therapies that can be translated into the clinic.

Proper citation: Cystic Fibrosis Center - University of California San Francisco (RRID:SCR_015398) 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/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   

•   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://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   

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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://github.com/caleblareau/mgatk

Software python-based command line interface for processing .bam files with mitochondrial reads and generating high-quality heteroplasmy estimation from sequencing data. This package places a special emphasis on mitochondrial genotypes generated from single-cell genomics data, primarily mtscATAC-seq, but is generally applicable across other assays.

Proper citation: mgatk (RRID:SCR_021159) Copy   

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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.diacomp.org

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   

•   Source: SciCrunch Registry