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https://www.sanger.ac.uk/collaboration/sequencing-idd-regions-nod-mouse-genome/

Genetic variations associated with type 1 diabetes identified by sequencing regions of the non-obese diabetic (NOD) mouse genome and comparing them with the same areas of a diabetes-resistant C57BL/6J reference mouse allowing identification of single nucleotide polymorphisms (SNPs) or other genomic variations putatively associated with diabetes in mice. Finished clones from the targeted insulin-dependent diabetes (Idd) candidate regions are displayed in the NOD clone sequence section of the website, where they can be downloaded either as individual clone sequences or larger contigs that make up the accession golden path (AGP). All sequences are publicly available via the International Nucleotide Sequence Database Collaboration. Two NOD mouse BAC libraries were constructed and the BAC ends sequenced. Clones from the DIL NOD BAC library constructed by RIKEN Genomic Sciences Centre (Japan) in conjunction with the Diabetes and Inflammation Laboratory (DIL) (University of Cambridge) from the NOD/MrkTac mouse strain are designated DIL. Clones from the CHORI-29 NOD BAC library constructed by Pieter de Jong (Children's Hospital, Oakland, California, USA) from the NOD/ShiLtJ mouse strain are designated CHORI-29. All NOD mouse BAC end-sequences have been submitted to the International Nucleotide Sequence Database Consortium (INSDC), deposited in the NCBI trace archive. They have generated a clone map from these two libraries by mapping the BAC end-sequences to the latest assembly of the C57BL/6J mouse reference genome sequence. These BAC end-sequence alignments can then be visualized in the Ensembl mouse genome browser where the alignments of both NOD BAC libraries can be accessed through the Distributed Annotation System (DAS). The Mouse Genomes Project has used the Illumina platform to sequence the entire NOD/ShiLtJ genome and this should help to position unaligned BAC end-sequences to novel non-reference regions of the NOD genome. Further information about the BAC end-sequences, such as their alignment, variation data and Ensembl gene coverage, can be obtained from the NOD mouse ftp site.

Proper citation: Sequencing of Idd regions in the NOD mouse genome (RRID:SCR_001483) Copy   

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http://www.mouse-genome.bcm.tmc.edu/ENU/MutagenesisProj.asp

THIS RESOURCE IS NO LONGER IN SERVICE. For updated mutant information, please visit MMRRC or The Jackson Laboratory. Produces, characterizes, and distributes mutant mouse strains with defects in embryonic and postembryonic development. The goal of the ENU Mutagenesis project III is to determine the function of genes on mouse Chromosome 11 by saturating the chromosome with recessive mutations. The distal 40 cM of mouse Chr 11 exhibits linkage conservation with human Chromosome 17. We are using the chemical N-ethyl-N-nitrosourea (ENU) to saturate wild type chromosomes with point mutations. By determining the function of genes on a mouse chromosome, we can extrapolate to predict function on a human chromosome. We expect many of the new mutants to represent models of human diseases such as birth defects, patterning defects, growth and endocrine defects, neurological anomalies, and blood defects. Because many of the mutations we expect to isolate may be lethal or detrimental to the mice, we are using a unique approach to isolate mutations. This approach uses a balancer chromosome that is homozygous lethal and carries a dominant coat color marker to suppress recombination over a reasonable interval.

Proper citation: Mouse Mutagenesis Center for Developmental Defects (RRID:SCR_007321) Copy   

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http://www.type2diabetesgenetics.org/

Portal and database of DNA sequence, functional and epigenomic information, and clinical data from studies on type 2 diabetes and analytic tools to analyze these data. .Provides data and tools to promote understanding and treatment of type 2 diabetes and its complications. Used for identifying genetic biomarkers correlated to Type 2 diabetes and development of novel drugs for this disease.

Proper citation: Accelerating Medicines Partnership Type 2 Diabetes Knowledge Portal (AMP-T2D) (RRID:SCR_003743) Copy   

•   Source: SciCrunch Registry

https://www.proteinspire.org/MOPED/

An expanding multi-omics resource that enables rapid browsing of gene and protein expression information from publicly available studies on humans and model organisms. MOPED also serves the greater research community by enabling users to visualize their own expression data, compare it with existing studies, and share it with others via private accounts. MOPED uniquely provides gene and protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis utilizing SPIRE (Systematic Protein Investigative Research Environment). Data can be queried for specific genes and proteins; browsed based on organism, tissue, localization and condition; and sorted by false discovery rate and expression. MOPED links to various gene, protein, and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED (MOPED 2.5) The current version of MOPED (MOPED 2.5, 2014) contains approximately 5 million total records including ~260 experiments and ~390 conditions.

Proper citation: MOPED - Model Organism Protein Expression Database (RRID:SCR_006065) Copy   

•   Source: SciCrunch Registry

https://gataca.cchmc.org/gataca/gudmap

A database which can be used to search for genes critical for a variety of Genito-Urinary system functions and diseases.

Proper citation: GATACA GUDMAP Gene Explorer (RRID:SCR_014518) Copy   

•   Source: SciCrunch Registry

https://hpap.pmacs.upenn.edu

Program is performing deep phenotyping of human endocrine pancreas and its interaction with immune system to better understand cellular and molecular events that precede and lead to beta cell loss in Type-1 Diabetes (T1D) and islet dysfunction in Type-2 Diabetes (T2D).

Proper citation: HIRN Human Pancreas Analysis Program (RRID:SCR_016202) Copy   

•   Source: SciCrunch Registry

http://www.med.umich.edu/mgpc/cores/vivo.htm

Core facility that consists of the following 4 distinct programs: In Vivo Small Animal Studies Program, Organoid/Enteroid Modeling Program, Biospecimens Banking Service, and Clinical Design and Statistics.

Proper citation: University of Michigan Center for Gastrointestinal Research In Vivo Animal and Human Studies Core (RRID:SCR_015608) Copy   

•   Source: SciCrunch Registry

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

Core whose services include anatomic pathology review of human and experimental animal tissues as well as consultation in the best approaches for such analyses, cost-effective and high quality processing and staining of formalin-fixed paraffin-embedded tissues, and making collections of human tissue and imaging technologies available to researchers.

Proper citation: University of Chicago Digestive Diseases Research Core Center Tissue and Cell Imaging Core (RRID:SCR_015607) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/pubs/MitoCarta/

Collection of genes encoding proteins with strong support of mitochondrial localization. Inventory of genes encoding mitochondrial-localized proteins and their expression across 14 mouse tissues. Database is based on human and mouse RefSeq proteins that are mapped to NCBI Gene loci. MitoCarta 2.0 inventory provides molecular framework for system-level analysis of mammalian mitochondria.

Proper citation: MitoCarta (RRID:SCR_018165) Copy   

•   Source: SciCrunch Registry

https://signalingpathways.org

Web multi omics knowledgebase based upon public, manually curated transcriptomic and cistromic datasets involving genetic and small molecule manipulations of cellular receptors, enzymes and transcription factors. Integrated omics knowledgebase for mammalian cellular signaling pathways. Web browser interface was designed to accommodate numerous routine data mining strategies. Datasets are biocurated versions of publically archived datasets and are formatted according to recommendations of the FORCE11 Joint Declaration on Data Citation Principles73, and are made available under Creative Commons CC 3.0 BY license. Original datasets are available.

Proper citation: Signaling Pathways Project (RRID:SCR_018412) Copy   

•   Source: SciCrunch Registry

https://www.sciencescott.com/pyminer

Software tool to automate cell type identification, cell type-specific pathway analyses, graph theory-based analysis of gene regulation, and detection of autocrine-paracrine signaling networks. Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq.

Proper citation: PyMINEr (RRID:SCR_016990) Copy   

•   Source: SciCrunch Registry

https://www.nature.com/articles/s41467-018-03367-w

Nanodroplet processing platform for deep and quantitative proteome profiling of 10 to 100 mammalian cells. It enhances efficiency and recovery of sample processing by downscaling processing volumes.

Proper citation: nanoPOTS (RRID:SCR_017129) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

https://www.zurich.ibm.com/cellcycletracer/

Software tool as supervised machine learning algorithm that classifies and sorts single cell mass cytometry data according to their cell cycle, which allows to correct for cell cycle state and cell volume heterogeneity. Reveals signaling relationships and cell heterogeneity that were otherwise masked. Computational method to quantify cell cycle and cell volume variability.

Proper citation: CellCycleTRACER (RRID:SCR_017128) Copy   

•   Source: SciCrunch Registry

https://github.com/epistasislab/hibachi

Software tool that creates data sets with particular characteristics. Method and open source software for simulating complex biological and biomedical data to aid in comparing and evaluating machine learning methods.

Proper citation: Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (RRID:SCR_017140) Copy   

•   Source: SciCrunch Registry

https://github.com/kwanjeeraw/metabox

Software R toolbox for thorough metabolomic data analysis, integration and interpretation. Metabox 2.0 is updated version of R package Metabox and includes several methods for data processing, statistical analysis, biomarker analysis, integrative analysis and data interpretation.

Proper citation: Metabox (RRID:SCR_024443) Copy   

•   Source: SciCrunch Registry

http://www.childrennetwork.org/

Database of clinical information and serum and tissue samples from children across the United States and Canada with Biliary Atresia, Idiopathic Neonatal Hepatitis, Cystic Fibrosis Liver Disease, Alagille Syndrome, Alpha-1 Antitrypsin Deficiency, Bile Acid Synthesis Defects, Mitochondrial Hepatopathies, and Progressive Familial Intrahepatic Cholestasis in order to facilitate research and to perform clinical, epidemiological, and therapeutic trials in these important pediatric liver diseases. Three NIDDK-funded consortia, Biliary Atresia Research Consortium (BARC), Cholestatic Liver Disease Consortium (CLiC), and the Cystic Fibrosis Liver Disease (CFLD) Network were consolidated to form ChiLDREN. Most of the ChiLDREN studies are natural history studies aimed at acquiring information and data that will provide a better understanding of these rare conditions. Participants will be asked to allow study personnel to obtain information from medical records and an interview, and to collect blood, urine, and tissue samples when clinically indicated, in order to understand the causes of these diseases and to improve the diagnosis and treatment of children with these diseases. All of the information obtained in these studies is confidential and no names or identifying information are used in the study.

Proper citation: Childhood Liver Disease Research and Education Network (RRID:SCR_001497) Copy   

•   Source: SciCrunch Registry

http://www.scandb.org/newinterface/about.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 17, 2022. A large-scale database of genetics and genomics data associated to a web-interface and a set of methods and algorithms that can be used for mining the data in it. The database contains two categories of single nucleotide polymorphism (SNP) annotations: # Physical-based annotation where SNPs are categorized according to their position relative to genes (intronic, inter-genic, etc.) and according to linkage disequilibrium (LD) patterns (an inter-genic SNP can be annotated to a gene if it is in LD with variation in the gene). # Functional annotation where SNPs are classified according to their effects on expression levels, i.e. whether they are expression quantitative trait loci (eQTLs) for that gene. SCAN can be utilized in several ways including: (i) queries of the SNP and gene databases; (ii) analysis using the attached tools and algorithms; (iii) downloading files with SNP annotation for various GWA platforms. . eQTL files and reported GWAS from NHGRI may be downloaded., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SCAN (RRID:SCR_005185) Copy   

•   Source: SciCrunch Registry

http://www.med.upenn.edu/molecular/core_culture.shtml

Core facility that maintains a centralized repository of cells and reagents pertinent to digestive, liver and pancreatic disease research. It also provides training for labs in new cell culture (2D and 3D) techniques.

Proper citation: University of Pennsylvania Center for Molecular Studies in Digestive and Liver Diseases Cell Culture Core (RRID:SCR_015621) Copy   

•   Source: SciCrunch Registry

https://assess-aki.hmc.psu.edu/

A study which recruits patients with and without an episode of acute kidney injury during a hospitalization, and follows them longitudinally for major cardiac, renal and mortality events. An important aspect of the study is the prospective evaluation of potential biomarkers for renal and cardiac outcomes.

Proper citation: Assessment Serial Evaluation and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) (RRID:SCR_014386) Copy   

•   Source: SciCrunch Registry

http://livercenter.ucsf.edu

Center whose goal is to integrate bench science with clinical investigation, in support of its vision to understand and cure human liver diseases.

Proper citation: UCSF Liver Center (RRID:SCR_015595) Copy   

•   Source: SciCrunch Registry