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http://www.humphreyslab.com/SingleCell/

Software tool as analyzer for kidney single cell datasets. Allows users to query gene expression from mouse or human kidney and human kidney organoid single cell datasets. For details about datasets visit ReBuilding a Kidney website.

Proper citation: Kidney Interactive Transcriptomics (RRID:SCR_017209) Copy   

•   Source: SciCrunch Registry

http://www2.bsc.gwu.edu/bsc/oneproj.php?pkey=28

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. Collect, store, and distribute genetic samples from cases and controls of type 1 diabetes and diabetic nephropathy for investigator-driven research into the genetic basis of diabetic nephropathy. As the risk of kidney complications in type 1 diabetes appears to have a considerable genetic component, this study assembled a large data resource for researchers attempting to identify causative genetic variants. The types of data collected allowed traditional case-control testing, a rapid and often powerful approach, and family-based analysis, a robust approach that is not influenced by population substructure.

Proper citation: Genetics of Kidneys in Diabetes (RRID:SCR_000133) Copy   

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http://datahub.io/dataset/kupkb

A collection of omics datasets (mRNA, proteins and miRNA) that have been extracted from PubMed and other related renal databases, all related to kidney physiology and pathology giving KUP biologists the means to ask queries across many resources in order to aggregate knowledge that is necessary for answering biological questions. Some microarray raw datasets have also been downloaded from the Gene Expression Omnibus and analyzed by the open-source software GeneArmada. The Semantic Web technologies, together with the background knowledge from the domain's ontologies, allows both rapid conversion and integration of this knowledge base. SPARQL endpoint http://sparql.kupkb.org/sparql The KUPKB Network Explorer will help you visualize the relationships among molecules stored in the KUPKB. A simple spreadsheet template is available for users to submit data to the KUPKB. It aims to capture a minimal amount of information about the experiment and the observations made.

Proper citation: Kidney and Urinary Pathway Knowledge Base (RRID:SCR_001746) Copy   

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http://www.humgen.rwth-aachen.de/

Catalog of all changes detected in PKHD1 (Polycystic Kidney and Hepatic Disease 1) in a locus specific database. Investigators are invited to submit their novel data to this database. These data should be meaningful for clinical practice as well as of relevance for the reader interested in molecular aspects of polycystic kidney disease (PKD). There are also some links and information for ARPKD patients and their parents. Autosomal recessive polycystic kidney disease (ARPKD/PKHD1) is an important cause of renal-related and liver-related morbidity and mortality in childhood. This study reports mutation screening in 90 ARPKD patients and identifies mutations in 110 alleles making up a detection rate of 61%. Thirty-four of the detected mutations have not been reported previously. Two underlying mutations in 40 patients and one mutation in 30 cases are disclosed, and no mutation was detected on the remaining chromosomes. Mutations were found to be scattered throughout the gene without evidence of clustering at specific sites. PKHD1 mutation analysis is a powerful tool to establish the molecular cause of ARPKD in a given family. Direct identification of mutations allows an unequivocal diagnosis and accurate genetic counseling even in families displaying diagnostic challenges.

Proper citation: Autosomal Recessive Polycystic Kidney Disease Mutation Database (RRID:SCR_002290) Copy   

•   Source: SciCrunch Registry

http://archives.niddk.nih.gov/patient/aask/aask.aspx

Clinical trial investigating whether a specific class of antihypertensive drugs (beta-adrenergic blockers, calcium channel blockers, or angiotensin converting enzyme inhibitors) and/or the level of blood pressure would influence progression of hypertensive kidney disease in African Americans. The initiative consisting of 21 clinical centers and a data-coordinating center is followed by a Continuation of AASK Cohort Study to investigate the environmental, socio-economic, genetic, physiologic, and other co-morbid factors that influence progression of kidney disease in a well-characterized cohort of African Americans with hypertensive kidney disease. Only patients who were previously in the randomized trial are eligible for the cohort study. A significant discovery was made in the treatment strategy for slowing kidney disease caused by hypertension. Angiotensin-converting enzyme (ACE) inhibitors, compared with calcium channel blockers, were found to slow kidney disease progression by 36 percent, and they drastically reduced the risk of kidney failure by 48 percent in patients who had at least one gram of protein in the urine, a sign of kidney failure. ACE inhibitors have been the preferred treatment for hypertension caused by diabetes since 1994; however, calcium channel blockers have been particularly effective in controlling blood pressure in African Americans. The AASK study now recommends ACE inhibitors to protect the kidneys from the damaging effects of hypertension. The Continuation of AASK Cohort Study will be followed at the clinical centers. The patients will be provided with the usual clinical care given to all such patients at the respective centers. Baseline demographic information, selected laboratory tests, and other studies are being obtained at the initiation of the Continuation Study. The patients will be seen quarterly at the centers, and some selected studies done at these visits. Samples will be obtained and stored for additional studies and analyses at a later date.

Proper citation: AASK Clinical Trial and Cohort Study (RRID:SCR_006985) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu/rgdCuration/?module=portal&func=show&name=renal

An integrated resource for information on genes, QTLs and strains associated with a variety of kidney and renal system conditions such as Renal Hypertension, Polycystic Kidney Disease and Renal Insufficiency, as well as Kidney Neoplasms.

Proper citation: Renal Disease Portal (RRID:SCR_009030) Copy   

•   Source: SciCrunch Registry

http://cellfinder.de/

Database of mapped validated gene and protein expression, phenotype and images related to cell types. The data allow characterization and comparison of cell types and can be browsed by using the body browser and by searching for cells or genes. All cells are related to more complex systems such as tissues, organs and organisms and arranged according to their position in development. CellFinder provides long-term data storage for validated and curated primary research data and provides additional expert-validation through relevant information extracted from text. Operated under the Open Source/Access model, community and scientific networking applications will allow users to store and retrieve their data and to explore cells and their interactions on singular and complex resolution levels. The involvement of stem cell registries and banks will allow direct access to selected cells. The set up the stem cell data repository will involve three lines of action: * the acquisition of scientific data and contents * the standardized description of this data, its organization with the help of ontologies and technical implementation * the integration of existing sources/logistics and to ensure sustainable long-term operation

Proper citation: CellFinder (RRID:SCR_006598) Copy   

•   Source: SciCrunch Registry

http://pepr.cnmcresearch.org/

An experiment in web-database access to large multi-dimensional data sets using a standardized experimental platform to determine if the larger scientific community can be given simple, intuitive, and user-friendly web-based access to large microarray data sets. All data in PEPR is also available via NCBI GEO. The structure and goals of PEPR differ from other mRNA expression profiling databases in a number of important ways. * The experimental platform in PEPR is standardized, and is an Affymetrix - only database. All microarrays available in the PEPR web database should ascribe to quality control and standard operating procedures. A recent publication has described the QC/SOP criteria utilized in PEPR profiles ( The Tumor Analysis Best Practices Working Group 2004 ). * PEPR permits gene-based queries of large Affymetrix array data sets without any specialized software. For example, a number of large time series projects are available within PEPR, containing 40-60 microarrays, yet these can be simply queried via a dynamic web interface with no prior knowledge of microarray data analysis. * Projects in PEPR originate from scientists world-wide, but all data has been generated by the Research Center for Genetic Medicine, Children''''s National Medical Center, Washington DC. Future developments of PEPR will allow remote entry of Affymetrix data ascribing to the same QC/SOP protocols. They have previously described an initial implementation of PEPR, and a dynamic web-queried time series graphical interface ( Chen et al. 2004 ). A publication showing the utility of PEPR for pharmacodynamic data has recently been published ( Almon et al. 2003 ).

Proper citation: Public Expression Profiling Resource (RRID:SCR_007274) Copy   

•   Source: SciCrunch Registry

http://crezoo.crt-dresden.de/crezoo/

Database of helpful set of CreERT2 driver lines expressing in various regions of the developing and adult zebrafish. The lines have been generated via the insertion of a mCherry-T2A-CreERT2 in a gene trap approach or by using promoter fragments driving CreERT2. You can search the list of all transgenic lines or single entries by insertions (gene) or expression patterns (anatomy/region). In most cases the CreERT2 expression profile using in situ hybridization at 24 hpf and 48 hpf is shown, but also additional information (e.g. mCherry or CreERT2 expression at adult stages, transactivation of a Cre-dependent reporter line) is displayed. Currently, not all insertions have been mapped to a genomic location but the database will be regularly updated adding newly generated insertions and mapping information. Your help in improving and broadening the database by giving your opinion or knowledge of expression patterns is highly appreciated.

Proper citation: CreZoo (RRID:SCR_008919) Copy   

•   Source: SciCrunch Registry

http://www.nephromine.org/

THIS RESOURCE IS NO LONGER IN SERVICE; REPLACED BY NEPHROSEQ; A growing database of publicly available renal gene expression profiles, a sophisticated analysis engine, and a powerful web application designed for data mining and visualization of gene expression. It provides unique access to datasets from the Personalized Molecular Nephrology Research Laboratory incorporating clinical data which is often difficult to collect from public sources and mouse data.

Proper citation: Nephromine (RRID:SCR_003813) Copy   

•   Source: SciCrunch Registry

http://cddb.nhlbi.nih.gov/cddb/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 16, 2013. This database is intended to serve as a learning tool to obtain curated information for the design of microarray targets to scan collecting duct tissues (human, rat, mouse). The database focuses on regulatory and transporter proteins expressed in the collecting duct, but when collecting duct proteins are a member of a larger family of proteins, common additional members of the family are included even if they have not been demonstrated to be expressed in the collecting duct. An Internet-accessible database has been devised for major collecting duct proteins involved in transport and regulation of cellular processes. The individual proteins included in this database are those culled from literature searches and from previously published studies involving cDNA arrays and serial analysis of gene expression (SAGE). Design of microarray targets for the study of kidney collecting duct tissues is facilitated by the database, which includes links to curated base pair and amino acid sequence data, relevant literature, and related databases. Use of the database is illustrated by a search for water channel proteins, aquaporins, and by a subsequent search for vasopressin receptors. Links are shown to the literature and to sequence data for human, rat, and mouse, as well as to relevant web-based resources. Extension of the database is dynamic and is done through a maintenance interface. This permits creation of new categories, updating of existing entries, and addition of new ones. CDDB is a database that organizes lists of genes found in collecting duct tissues from three mammalian species: human, rat, and mouse. Proteins are divided into categories by family relationships and functional classification, and each category is assigned a section in the database. Each section includes links to the literature and to sequence information for genes, proteins, expressed sequence tags, and related information. The user can peruse a section or use a search engine at the bottom of the web page to search the database for a name or abbreviation or for a link to a sequence. Each entry in the database includes links to relevant papers in the kidney and collecting duct literature. It uses links to PubMed to generate MEDLINE searches for retrieval of references. In addition, each entry includes links to curated sequence data available in LocusLink. Individual links are made to sequence and protein data for human, rat, and mouse. Links are then added as curated sequences become available for proteins identified in the renal collecting duct and for proteins identified in kidney and similar in function or homologous to proteins identified in the collecting duct.

Proper citation: Collecting Duct Database (RRID:SCR_000759) Copy   

•   Source: SciCrunch Registry

http://www.gudmap.org

Project aggregates and provides experimental gene expression data from genito-urinary system. International consortium providing molecular atlas of gene expression for developing organs of GenitoUrinary (GU) tract. Mouse strains to facilitate developmental and functional studies within GU system. Experimental protocols and standard specifications. Tutorials describing GU organogenesis and primary data via database. Data are from large-scale in situ hybridization screens (wholemount and section) and microarray gene expression data of microdissected, laser-captured and FACS-sorted components of developing mouse genitourinary (GU) system.

Proper citation: GenitoUrinary Development Molecular Anatomy Project (RRID:SCR_001554) Copy   

•   Source: SciCrunch Registry

https://clinicaltrials.gov/study/NCT00342927?term=AREA%5BBasicSearch%5D(NIDDK%20endocrine%20and%20diabetes)%20AND%20AREA%5BSponsorSearch%5D(NIDDK)%20AND%20AREA%5BOverallStatus%5D(NOT_YET_RECRUITING%20OR%20RECRUITING%20OR%20ACTIVE_NOT_RECRUITING)&rank=1

Multicenter observational study designed to identify genetic determinants of diabetic nephropathy. It is conducted in eleven U.S. clinical centers and a coordinating center, and with four ethnic groups (European Americans, African Americans, Mexican Americans, and American Indians). Two strategies are used to localize susceptibility genes: a family-based linkage study and a case-control study using mapping by admixture linkage disequilibrium (MALD). In the family-based study, probands with diabetic nephropathy are recruited with their parents and selected siblings. Linkage analyses will be conducted to identify chromosomal regions containing genes that influence the development of diabetic nephropathy or related quantitative traits such as serum creatinine concentration, urinary albumin excretion, and plasma glucose concentrations. Regions showing evidence of linkage will be examined further with both genetic linkage and association studies to identify genes that influence diabetic nephropathy or related traits. Two types of MALD studies are being done. One is a case-control study of unrelated individuals of Mexican American heritage in which both cases and controls have diabetes, but only the case has nephropathy. The other is a case-control study of African American patients with nephropathy (cases) and their spouses (controls) unaffected by diabetes and nephropathy; offspring are genotyped when available to provide haplotype data. The specific goals of this program: * Delineate genomic regions associated with the development and progression of renal disease(s) * Evaluate whether there is a genetic link between diabetic nephropathy and diabetic retinopathy * Improve outcomes * Provide protection for people at risk and slow the progression of renal disease * Help establish a resource for genetic studies of kidney disease and diabetic complications by creating a repository of genetic samples and a database * Encourage studies of the genetics of progressive renal disease

Proper citation: Family Investigation of Nephropathy of Diabetes (RRID:SCR_001525) 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://hembase.niddk.nih.gov/

Database designed for web-based examination of the human erythroid transcriptome. The database is organized to provide a cytogenetic band position, a unique name as well as a concise annotation for each entry. Search queries may be performed by name, keyword or cytogenetic location. Search results are linked to primary sequence data and three major human genome browsers for access to information considered current at the time of each search. Hembase provides interested scientists and clinical hematologists with a genome-based approach toward the study of erythroid biology. Red blood cells in the circulation arise from hematopoietic stem cells that proliferate as erythroid progenitors and differentiate into erythroid precursor cells in response to the hormone erythropoietin. Messenger RNA was isolated from those cells and used to generate gene libraries. Sequencing several thousand expressed sequence tags (EST) from those libraries was then performed. Those EST and sequences encoding several hundred additional genes with known expression in erythroid cells are compiled here as a database of human erythroid gene activity. The database is organized and linked according to the location of these sequences within the human genome., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: HemBase (RRID:SCR_002880) Copy   

•   Source: SciCrunch Registry

http://www.mirbase.org/

Central online repository for microRNA nomenclature, sequence data, annotation and target prediction.Collection of published miRNA sequences and annotation.

Proper citation: miRBase (RRID:SCR_003152) Copy   

•   Source: SciCrunch Registry

http://dgv.tcag.ca/dgv/app/home

Public repository that accepts direct submissions and provides archiving, accessioning and distribution of publicly available genomic structural variants, in all species. Variants are accessioned at the study and sample level, granting stable identifiers that can be used in publications. DGVa data is integrated with other EBI resources, including comprehensive EBI search and Ensembl genome browser. Exchanges data with companion database, dbVar, at National Center for Biotechnology Information.NOTE: since 2019 DGVa doesn't accept submissions. Please send the data for submission to European Variation Archive (EVA).

Proper citation: Database of Genomic Variants Archive (DGVa) (RRID:SCR_004896) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/ena/

Public archive providing a comprehensive record of the world''''s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. All submitted data, once public, will be exchanged with the NCBI and DDBJ as part of the INSDC data exchange agreement. The European Nucleotide Archive (ENA) captures and presents information relating to experimental workflows that are based around nucleotide sequencing. A typical workflow includes the isolation and preparation of material for sequencing, a run of a sequencing machine in which sequencing data are produced and a subsequent bioinformatic analysis pipeline. ENA records this information in a data model that covers input information (sample, experimental setup, machine configuration), output machine data (sequence traces, reads and quality scores) and interpreted information (assembly, mapping, functional annotation). Data arrive at ENA from a variety of sources including submissions of raw data, assembled sequences and annotation from small-scale sequencing efforts, data provision from the major European sequencing centers and routine and comprehensive exchange with their partners in the International Nucleotide Sequence Database Collaboration (INSDC). Provision of nucleotide sequence data to ENA or its INSDC partners has become a central and mandatory step in the dissemination of research findings to the scientific community. ENA works with publishers of scientific literature and funding bodies to ensure compliance with these principles and to provide optimal submission systems and data access tools that work seamlessly with the published literature. ENA is made up of a number of distinct databases that includes the EMBL Nucleotide Sequence Database (Embl-Bank), the newly established Sequence Read Archive (SRA) and the Trace Archive. The main tool for downloading ENA data is the ENA Browser, which is available through REST URLs for easy programmatic use. All ENA data are available through the ENA Browser. Note: EMBL Nucleotide Sequence Database (EMBL-Bank) is entirely included within this resource.

Proper citation: European Nucleotide Archive (ENA) (RRID:SCR_006515) Copy   

•   Source: SciCrunch Registry

http://flybase.org/

Database of Drosophila genetic and genomic information with information about stock collections and fly genetic tools. Gene Ontology (GO) terms are used to describe three attributes of wild-type gene products: their molecular function, the biological processes in which they play a role, and their subcellular location. Additionally, FlyBase accepts data submissions. FlyBase can be searched for genes, alleles, aberrations and other genetic objects, phenotypes, sequences, stocks, images and movies, controlled terms, and Drosophila researchers using the tools available from the "Tools" drop-down menu in the Navigation bar.

Proper citation: FlyBase (RRID:SCR_006549) Copy   

•   Source: SciCrunch Registry

http://zfin.org

Model organism database that serves as central repository and web-based resource for zebrafish genetic, genomic, phenotypic and developmental data. Data represented are derived from three primary sources: curation of zebrafish publications, individual research laboratories and collaborations with bioinformatics organizations. Data formats include text, images and graphical representations.Serves as primary community database resource for laboratory use of zebrafish. Developed and supports integrated zebrafish genetic, genomic, developmental and physiological information and link this information extensively to corresponding data in other model organism and human databases.

Proper citation: Zebrafish Information Network (ZFIN) (RRID:SCR_002560) Copy   

•   Source: SciCrunch Registry