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http://zebrafish.wi.mit.edu/rnai/

Community built zebrafish RNAi platform that contains plasmids, successfully targeted genes and shRNA sequences, and a forum for discussion. This is a true community platform with users who add data, modify entiries, request features and share using the discussion board.

Proper citation: Zebrafish RNAi Database (RRID:SCR_008965) Copy   

•   Source: SciCrunch Registry

http://gwas.biosciencedbc.jp/cgi-bin/hvdb/hv_top.cgi

A repository database to achieve continuous and intensive management of GWAS data and variation data identified by next generation sequencing (NGS) and data-sharing among researchers. In this database, variations including short/long insertions / deletions and structural variations related to disease susceptibility, virus resistance, and drug response are registered along with statistical genetic results and simple clinical characteristics to clarify the locus specific characteristics. Currently this database contains information extracted from scientific papers and next generation sequencing results and other small scale experimental results of several research laboratories. Mutation data submission is greatly appreciated.

Proper citation: Human Variation DB (RRID:SCR_009014) Copy   

•   Source: SciCrunch Registry

http://wpicr.wpic.pitt.edu/WPICCompGen/bars.htm

Software application that is a statistical method that bridges the gap between single-locus and haplotype-based tests of association. It is based on the non-parametric regression techniques embodied by Bayesian Adaptive Regression Splines. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BARS (RRID:SCR_009123) Copy   

•   Source: SciCrunch Registry

https://gmod.org/wiki/CMap.1

Web-based tool that allows users to view comparisons of genetic and physical maps. The package also includes tools for curating map data. (entry from Genetic Analysis Software)

Proper citation: CMAP (RRID:SCR_009034) Copy   

•   Source: SciCrunch Registry

https://wiki.nci.nih.gov/display/caGWAS/caGWAS

Too that allows researchers to integrate, query, report, and analyze significant associations between genetic variations and disease, drug response or other clinical outcomes. SNP array technologies make it possible to genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) simultaneously, enabling whole genome association studies. Within the Clinical Genomic Object Model (CGOM), the caIntegrator team created a domain model for Whole Genome Association Study Analysis. CGOM-caGWAS is a A semantically annotated domain model that captures associations between Study, Study Participant, Disease, SNP Association Analysis, SNP Population Frequency and SNP annotations. caGWAS APIs and web portal provide: * a semantically annotated domain model, database schema with sample data, seasoned middleware, APIs, and web portal for GWAS data; * platform and disease agnostic CGOM-caGWAS model and associated APIs; * the opportunity for developers to customize the look and feel of their GWAS portal; * a foundation of open source technologies; * a well-tested and performance-enhanced platform, as the same software is being used to house the CGEMS data portal; * accelerated analysis of results from various biomedical studies; and * a single application through which researchers and bioinformaticians can access and analyze clinical and experimental data from a variety of data types, as caGWAS objects are part of the CGOM, which includes microarray, genomic, immunohistochemistry, imaging, and clinical data.

Proper citation: caGWAS (RRID:SCR_009617) Copy   

•   Source: SciCrunch Registry

http://link.springer.com/article/10.1007%2Fs11357-003-0002-y

A database that stores information on the biomolecules which are modulated during aging and by caloric restriction (CR). To enhance its usefulness, data collected from studies of CR''''s anti-oxidative action on gene expression, oxidative stress, and many chronic age-related diseases are included. AgingDB is organized into two sections A) apoptosis and the various mitochondrial biomolecules that play a role in aging; B) nuclear transcription factors known to be_sensitive to oxidative environment. AgingDB features an imagemap of biomolecular signal pathways and visualized information that includes protein-protein interactions of biomolecules. Authorized users can submit a new biomolecule or edit an existing biomolecule to reflect latest developments.

Proper citation: AgingDB (RRID:SCR_010226) Copy   

•   Source: SciCrunch Registry

http://www.illumina.com/

American company incorporated that develops, manufactures and markets integrated systems for the analysis of genetic variation and biological function. Provides a line of products and services that serve the sequencing, genotyping and gene expression and proteomics markets. Its headquarters are located in San Diego, California.

Proper citation: Illumina (RRID:SCR_010233) Copy   

•   Source: SciCrunch Registry

http://evexdb.org

EVEX is a text mining resource built on top of PubMed abstracts and PubMed Central full texts. It contains over 40 million bio-molecular events among more than 76 million automatically extracted gene/protein name mentions. The text mining data further has been enriched with gene normalization results, allowing straightforward integration with external resources. Further, gene families from Ensembl and HomoloGene provide homology-based event generalizations. EVEX presents both direct and indirect associations between genes and proteins, enabling explorative browsing of relevant literature.

Proper citation: Evex (RRID:SCR_010509) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/GRO

Ontology that is a conceptual model for the domain of gene regulation. It covers processes that are linked to the regulation of gene expression as well as physical entities that are involved in these processes (such as genes and transcription factors) in terms of ontology classes and semantic relations between classes. GRO is intended to represent common knowledge about gene regulation in a formal way rather than representing extremely fine-grained classes as can be found in ontologies such as the Gene Ontology (GO) (created for data base annotation purposes) and various relevant databases. The main purpose of the ontology is to support NLP applications. It has a particular focus on the relations between processes and the molecules (participants) involved. The basic structure of the GRO is a direct acyclic graph (DAG) with ontology classes as nodes and is-a relations between classes as edges. The taxonomic backbone is further enriched by several semantic relation types (part-of, from-species, participates-in with the two sub-relations agent-of and patient-of).

Proper citation: Gene Regulation Ontology (RRID:SCR_010590) Copy   

•   Source: SciCrunch Registry

https://www.mdc-berlin.de/8551903/en/research/research_teams/systems_biology_of_gene_regulatory_elements/projects/miRDeep

Software tool to identify known and novel miRNA genes in seven animal clades by analyzing sequenced RNAs. Used for discovering known and novel miRNAs from small RNA sequencing data.

Proper citation: miRDeep (RRID:SCR_010829) Copy   

•   Source: SciCrunch Registry

http://genecloud.org/

Gene Cloud is a novel tool presenting gene-gene associations based on the scientific literature. It was developed by the Knockout Mouse Repository (www.komp.org) to help our customers find products related to other products they chose. We have built a detailed graph model of gene-gene associations based on how many times two genes are cited in the same article. If two genes are cited in many papers together, they are considered strongly connected. Each instance of Gene Cloud is centered around a specific gene. A list of the top most related genes is plotted as a branching structure from the center. A secondary branch can occur if a gene in the graph is more related a non-central gene than it is to the center gene. The font size of a branched gene indicates the relative strength of connection--always to the center gene. The distribution of genes in space is randomized each time Gene Cloud is run so a different picture will result for the same central gene. Color is used to indicate the availability of Knockout Mouse products at the KOMP Repository. If a gene is colored green in the graph there are products (mutant ES cells, sperm, embryos, or mice) ready to be ordered. Blue colored genes do not yet have products available, but you can follow the links back to the KOMP Repository and register interest to be alerted when products do become available. Gene Cloud is driven by a database of gene-gene associations that currently contains 82,000 genes and other biotypes, 113,000 annotated publications, and 467 million connections. The latest gene symbols, names and gene-publication annotation information is updated daily from the Mouse Genome Informatics database. The graphing is accomplished through the use of a modified version of jsViz.

Proper citation: Gene Cloud: Exploring Connections in the Mouse Genome (RRID:SCR_003503) Copy   

•   Source: SciCrunch Registry

http://www.c2b2.columbia.edu/danapeerlab/html/jistic.html

Software tool for analyzing datasets of genome-wide copy number variation to identify driver aberrations in cancer.

Proper citation: JISTIC (RRID:SCR_003482) Copy   

•   Source: SciCrunch Registry

http://brainarray.mbni.med.umich.edu/Brainarray/Database/ProbeMatchDB/ncbi_probmatch_para_step1.asp

Matches a list of microarray probes across different microrarray platforms (GeneChip, EST from different vendors, Operon Oligos) and species (human, mouse and rat), based on NCBI UniGene and HomoloGene. The capability to match protein sequence IDs has just been added to facilitate proteomic studies. The ProbeMatchDB is mainly used for the design of verification experiments or comparing the microarray results from different platforms. It can be used for finding equivalent EST clones in the Research Genetics sequence verified clone set based on results from Affymetirx GeneChips. It will also help to identify probes representing orthologous genes across human, mouse and rat on different microarray platforms.

Proper citation: ProbeMatchDB 2.0 (RRID:SCR_003433) Copy   

•   Source: SciCrunch Registry

http://www.imi-marcar.eu/

Consortium to identify early biological indicators (biomarkers) that can be used to predict the development of cancer, as an unintended and adverse response to a new drug. The use of these biomarkers that detect early carcinogenicity will hopefully accelerate drug development and increase patient safety. The project focuses on non-genotoxic carcinogenesis (NGC) specifically looking at the role of epigenetic effects that could be caused as unintended consequences of new drugs. Using a combination of molecular analysis technologies, the consortium combines expertise in the field of biomarkers, human and rodent cancer models, imaging, molecular profiling and bioinformatics. Participants will focus on liver tumors, the organ most affected by non-genotoxic carcinogenesis, during the preclinical safety evaluations of candidate-medicines. Their findings aim to facilitate tumor identification in other organs as well, in hopes of providing insights in the mechanisms of tumor growth. The main objectives of the consortium are to: * Identify early biomarkers for predicting which compounds have a potential for later cancer development * Improve the scientific basis for assessing carcinogenic potential of non-genotoxic (NGC) drugs * Identify the molecular response to NGC exposure that underpins development of early exposure biomarkers * Improve drug safety and the efficiency of drug development by advancing the development of alternative research methods

Proper citation: MARCAR (RRID:SCR_003755) Copy   

•   Source: SciCrunch Registry

http://www.blueprint-epigenome.eu/

Consortium to further the understanding of how genes are activated or repressed in both healthy and diseased human cells with a focus on distinct types of haematopoietic cells from healthy individuals and on their malignant leukemic counterparts. They will generate at least 100 reference epigenomes and study them to advance and exploit knowledge of the underlying biological processes and mechanisms in health and disease. Reference epigenomes will be generated by state-of-the-art technologies from highly purified cells for a comprehensive set of epigenetic marks in accordance with quality standards set by International Human Epigenome Consortium (IHEC). Access to the data is provided as well as the protocols used to collect the different blood cell types, to perform the different types of epigenomic analyses, etc.). This resource-generating activity will be complemented by hypothesis-driven research into blood-based diseases, including common leukemias and autoimmune disease (Type 1 Diabetes), by discovery and validation of epigenetic markers for diagnostic use and by epigenetic target identification. Since epigenetic changes are reversible, they can be targets for the development of novel and more individualized medical treatments. The involvement of companies will energize epigenomic research in the private sector by the development of smart technologies for better diagnostic tests and by identifying new targets for compounds. Thus the results of the project may lead to targeted diagnostics, new treatments and preventive measures for specific diseases in individual patients, an approach known as "personalized medicine". The Blueprint Data Access Committee will consider applications for access to data sets stored in the European Genome-phenome Archive (EGA) when authorized to do so by the Blueprint consortium and the holders of the original consent documents. Access is conditional upon availability of samples and/or data and signed agreement by the researcher(s) and the responsible employing Institution to abide by policies related to publication, data disposal, ethical approval and confidentiality. At EBI, the ftp site with the data can be found. You can either opt to link to the track hubs yourself or you can add the track hub to a genome browser - UCSC or ENSEMBL. Also Meta Data files and README are available. The data can also be accessed via the BIOMART system.

Proper citation: Blueprint Epigenome (RRID:SCR_003844) Copy   

•   Source: SciCrunch Registry

http://www.adgenetics.org/

Consortium to conduct genome-wide association studies (GWAS) to identify genes associated with an increased risk of developing late-onset Alzheimer''''s disease (LOAD). The goal of the ADGC is to identify genetic variants associated with risk for AD. It plans to do this through the following collaborative goals: # Identify genes responsible for AD susceptibility # Identify AD sub-phenotype genes rate-of-progression plaque / tangle load / distribution biomarker variability # Generate a genetic data resource for the AD research community Data generated by ADGC is available at the following website: https://www.niagads.org/content/alzheimers-disease-genetics-consortium-adgc-collection

Proper citation: Alzheimers Disease Genetics Consortium (RRID:SCR_004004) Copy   

•   Source: SciCrunch Registry

http://eaglep.case.edu/iamdgc_web/

Consortium aiming to identify the remaining genetic risk variants for Age-related Macular Degeneration (AMD). To increase the statistical power needed to identify genes that have small, yet significant contributions to AMD, the consortium is conducting a meta-analysis on 15 Genome Wide Association Studies (GWAS) pooled from consortium members representing over 8,000 patients with advanced AMD (dry type, neovascular, or both) and 50,000 controls. In addition to verifying known genes, the consortium identified 19 new gene variants. The genes identified in these studies function in the immune system, cholesterol transport and metabolism, and formation and maintenance of connective tissue. This study provides a nearly complete picture of genetic heritability for AMD.

Proper citation: International AMD Genetics Consortium (RRID:SCR_004009) Copy   

•   Source: SciCrunch Registry

http://www.wholecellkb.org/

A collection of free, open-source model organism databases designed specifically to enable comprehensive, dynamic simulations of entire cells and organisms. WholeCellKB provides comprehensive, quantitative descriptions of individual species including: * Their subcellular organization, * Their chromosome sequences, * The essentiality, location, length, direction, and homologs of each gene, * The organization and promoter of each transcription unit, * The expression and degradation rate of each RNA gene product, * The specific folding and maturation pathway of each RNA and protein species including the localization, N-terminal cleavage, signal sequence, prosthetic groups, disulfide bonds, and chaperone interactions of each protein species, * The subunit composition of each macromolecular complex, * Their genetic code, * The binding sites and footprint of every DNA-binding protein, * The structure, charge, and hydrophobicity of every metabolite, * The stoichiometry, catalysis, coenzymes, energetics, and kinetics of every chemical reaction, * The regulatory strength of each transcription factor on each promoter, * Their chemical composition, and * The composition of its typical SP-4 laboratory growth medium. WholeCellKB currently contains a single database of Mycoplasma genitalium, an extremely small gram-positive bacterium and common human pathogen. This database is the most comprehensive description of any single organism to date, and was used to develop the first whole-cell computational model. Users can download the WholeCellKB source code and content to create and customize - including the content, data model, and user interface - their own model organism database.

Proper citation: WholeCellKB (RRID:SCR_004104) Copy   

•   Source: SciCrunch Registry

http://www.wikigenes.org/e/art/e/258.html

Consortium to discover and map the genes that contribute to Alzheimer's disease and completely understand the role inheritance plays. To achieve this goal, they will work to identify all the genes that contribute to the risk of developing this disease. Investigators will have access to combined genetic data from a large number of Alzheimer's disease subjects and compare it to genetic data from an equally large number of elderly people who do not have Alzheimer's. In the initial phase of the work, more than 20,000 people with Alzheimer's and about 20,000 healthy elderly subjects will be compared. As the study progresses, 10,000 additional people with Alzheimer's and the same number of healthy elderly subjects will be added to the study. The subjects for these studies come from different Alzheimer research project locations across Europe, the UK, the US, and Canada. Data is available from their 2014 publication in Translational Psychiatry at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3944635/ (http://www.ncbi.nlm.nih.gov/pubmed/24495969) Currently, there is no public access to the raw individual level genetic data because of privacy considerations. Researchers working with US cohorts deposit data in the database of genotypes and phenotypes (dbGaP), where it is available to all researchers who can show that they are able to guarantee the security of the data. After scanning the DNA of over 74,000 patients and controls from 15 countries, the IGAP consortium reported 11 new regions of the genome involved in late-onset Alzheimer's disease. IGAP published its results in Nature Genetics on October 27, http://www.ncbi.nlm.nih.gov/pubmed/24162737

Proper citation: International Genomics of Alzheimers Project (RRID:SCR_004029) Copy   

•   Source: SciCrunch Registry

http://www.cmhd.ca/genetrap/

Generate gene trap insertions using mutagenic polyA trap vectors, followed by sequence tagging to develop a library of mutagenized ES cells freely available to the scientific community. This library is searchable by sequence or key word searches including gene name or symbol, chromosome location, or Gene Ontology (GO) terms. In addition,they offer a custom email alert service in which researchers are able to submit search criteria. Researchers will receive automated e-mail notification of matching gene trap clones as they are entered into the library and database. The resource features the use of complementary second and third generation polyA trap vectors developed by the Stanford lab and the laboratory of Professor Yasumasa Ishida of the Nara Institute of Science and Technology (NAIST) in Japan to mutagenize murine embryonic stem (ES) cells. CMHD gene trap clones are distributed by the Canadian Mouse Mutant Repository(CMMR). Information about ordering, services, and pricing can be found on their web site (http://www.cmmr.ca/services/index.html)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 15,2026.

Proper citation: Centre for Modeling Human Disease Gene Trap Resource (RRID:SCR_002785) Copy   

•   Source: SciCrunch Registry