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http://caintegrator-info.nci.nih.gov/rembrandt

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 28,2023. An initiative to develop a molecular classification schema that is both clinically and biologically meaningful, based on gene expression and genomic data from tumors (Gliomas) of patients who will be prospectively followed through natural history and treatment phase of their illness. The study will also explore gene expression profiles to determine the responsiveness of the patients and correlate with discrete chromosomal abnormalities. The initiative was designed to obtain a large amount of molecular data on DNA and RNA of freshly collected tumor samples that were collected, processed and analyzed in a standardized fashion to allow for large-scale cross sample analysis. The sample collection is accompanied by careful and prospective clinical data acquisition, allowing a variety of matched molecular and clinical data permitting a wide variety of analyses. GMDI has accrued fresh frozen tumors in the retrospective phase (all from the Henry Ford Hospital, without germline DNA) and fresh frozen tumors in the prospective phase (from a variety of institutions). In addition to characterizing the samples from patients enrolled in GMDI, the microarray group has generated genomic-scale analyses of the many human and canine glioma initiating cells/glioma stem cells (GIC/GSC) lines, as well as many canine and murine normal neural stem cell (NSC) lines produced in laboratory.

Proper citation: Glioma Molecular Dignostic Initiatives (RRID:SCR_003329) Copy   

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http://www.xenbase.org/

Data collection for Xenopus laevis and Xenopus tropicalis biology and genomics.

Proper citation: Xenbase (RRID:SCR_003280) Copy   

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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   

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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   

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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   

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http://pfs.nus.edu.sg/(S(dyrcwejlfws33vxe23zlvrf3))/CopyRightNotice.aspx?ReturnURL=%2fQueryInterface_V5_2.aspx

Search engine integrating various bio-informatic resources and algorithims to produce a one-stop resource for biologists to identify potentially functional SNPs. It caters to different groups of scientists interested in SNPs including those working in the following areas: * Whole-genome association studies * Gene-based association studies * Designing experiments to address the functionality of specific SNPs * Determining potentially functionally significant SNPs that are in LD with non-pfSNPs of interest. Users may add published SNP functions.

Proper citation: pfSNP (RRID:SCR_002167) 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   

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http://genome.jgi.doe.gov/

Portal providing access to all JGI genomic databases and analytical tools, sequencing projects and their status, search for and download assemblies and annotations of sequenced genomes, and interactively explore those genomes and compare them with other sequenced microbes, fungi, plants or metagenomes using specialized systems tailored to each particular class of organisms. The Department of Energy (DOE) Joint Genome Institute (JGI) is a national user facility with massive-scale DNA sequencing and analysis capabilities dedicated to advancing genomics for bioenergy and environmental applications. Beyond generating tens of trillions of DNA bases annually, the Institute develops and maintains data management systems and specialized analytical capabilities to manage and interpret complex genomic data sets, and to enable an expanding community of users around the world to analyze these data in different contexts over the web.

Proper citation: JGI Genome Portal (RRID:SCR_002383) Copy   

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https://www.genevestigator.com/gv/

A high performance search engine for gene expression that integrates thousands of manually curated public microarray and RNAseq experiments and nicely visualizes gene expression across different biological contexts (diseases, drugs, tissues, cancers, genotypes, etc.). There are two basic analysis approaches: # for a gene of interest, identify which conditions affect its expression. # for condition(s) of interest, identify which genes are specifically expressed in this/these conditions. Genevestigator builds on the deep integration of data, both at the level of data normalization and on the level of sample annotations. This deep integration allows scientists to ask new types of questions that cannot be addressed using conventional tools.

Proper citation: Genevestigator (RRID:SCR_002358) 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

http://reflect.embl.de/

Web service that tags gene, protein, and small molecule names in any web page. Clicking on a tagged term opens a small popup showing summary information, and allows the user to quickly link to more detailed information. For each protein or gene, Reflect provides domain structure, sub-cellular localization, 3D structure, and interaction partners. For small molecules, it provides the chemical structure and interaction partners. Reflect can be installed as a plugin to Firefox or Internet Explorer, or can be used by entering a URL in the field provided. It can also be accessed programmatically via a REST or SOAP API, and a Reflect button can easily be added to any web page using Javascript or using a CGI proxy. Reflect was first-prize winner out of over 70 submissions in the Elsevier Grand Challenge, an international competition for systems that improve the way scientific information is communicated and used. Reflect can be edited and improved by the community.

Proper citation: Reflect (RRID:SCR_002714) Copy   

•   Source: SciCrunch Registry

http://camera.calit2.net/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016; however, the URL provides links to associated projects and data. A suite of data query, download, upload, analysis and sharing tools serving the needs of the microbial ecology research community, and other scientists using metagenomics data.

Proper citation: Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (RRID:SCR_002676) Copy   

•   Source: SciCrunch Registry

http://www.g2conline.org/

Genes to Cognition (G2C) Online is about modern neuroscience. It focuses on cognitive disorders, cognitive processes, and research approaches. Use the dynamic network maps to explore our library of 750+ unique items. Or, use the linear Selected Items menu on top of each map to tour selected content. Read the G2C blog, use simple mapper, or the 3-D brain, an interactive model of the brain. Disorders included in this site: ADHD, Alzheimer's Disease, Autism, Bipolar Disorder, Depression, Schizophrenia Cognitive Processes include: Attention, Language, Learning and Memory, Perception, and Thinking Research Approaches include: Bioinformatics, Ethics, Gene Finding, Model systems, Neuroimaging, Psychology. Navigation: Interact with the dynamic Networks Maps to explore the full catalog of content. Roll-over a node on the map for a preview and click to open the content. Move on to other content by returning to the network map. Each node you visit on the map gets flagged. Follow the Selected Items Subway Line for an overview of a topic. Roll-over a subway node for a preview and click to open the content. Other Features: Most content items include links to Related Items, which allow you to explore further. The Glossary includes over 300 neuroscience keywords. Search for content using keywords or id number. Select a preferred network map to view the content in context. Open/close the History at the lower left to view visited content. Your history is stored until you clear it. Simple Mapper - We developed Simple Mapper to power this web site on the brain. Now, you can use it to organize what comes out of yours! With Simple Mapper create and save concept maps, network diagrams, or flowcharts for personal use or to share with others. 3-D Brain - The G2C Brain is an interactive 3-D model of the brain, with 29 structures that can be rotated in three-dimensional space. Each structure has information on brain disorders, brain damage, case studies, and links to modern neuroscience research. Ideal for students, researchers, and educators in psychology and biology. Also available for download: 3D Brain App for iPhone and iPod Touch!

Proper citation: Genes to Cognition Online (RRID:SCR_002746) Copy   

•   Source: SciCrunch Registry

http://thomsonreuters.com/metadrug/

A leading systems pharmacology solution that incorporates extensive manually curated information on biological effects of small molecule compounds. Predictive and analytical algorithms look at chemical compounds from different angles in one integrated workflow are available for: * Individual previously described compounds to look up their known information and predict currently unknown properties * Individual newly synthesized or isolated compounds to predict their properties from its structures * Compound libraries to extract known and predict new properties of individual compounds and perform their comparison and prioritization

Proper citation: MetaDrug (RRID:SCR_000461) Copy   

•   Source: SciCrunch Registry

http://wannovar.usc.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Web interface to the ANNOVAR software, a tool to annotate functional consequences of genetic variation from high-throughput sequencing data, to help biologists without bioinformatics skills to easily submit a list of mutations (even whole-genome variants calls) to the web server, select the desired annotation categories, and receive functional annotation back by emails. Given a list of single nucleotide variants (SNVs) and insertions / deletions in VCF or ANNOVAR input format, wANNOVAR annotates their functional effects on genes (such as amino acid changes for non-synonymous SNPs), calculate their predicted functional importance scores (such as SIFT and PolyPhen scores), retrieve allele frequencies in public databases (such as the 1000 Genomes Project and NHLBI-ESP 6500 exomes), and implement a variants reduction protocol to identify a subset of potentially deleterious variants., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: wANNOVAR (RRID:SCR_000565) Copy   

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http://dbserv2.informatik.uni-leipzig.de:8080/onex/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Web-based application that integrates versions of 16 life science ontologies including the Gene Ontology, NCI Thesaurus and selected OBO ontologies with data leading back to 2002 in a common repository to explore ontology changes. It allows to study and apply the evolution of these integrated ontologies on three different levels. It provides global ontology evolution statistics and ontology-specific evolution trends for concepts and relationships and it allows the migration of annotations in case a new ontology version was released

Proper citation: OnEx - Ontology Evolution Explorer (RRID:SCR_000602) Copy   

•   Source: SciCrunch Registry

http://linus.nci.nih.gov./BRB-ArrayTools.html

A software package for the visualization and statistical analysis of DNA microarray gene expression data. The tools have been developed from the R statistical system, in C and fortran programs and Java applications. They are integrated into Excel as an add-in.

Proper citation: Biometric Research Branch: ArrayTools (RRID:SCR_000778) Copy   

•   Source: SciCrunch Registry

http://www.isrec.isb-sib.ch/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Computational Cancer Genomics (CCG) group is dedicated to the development of analysis tools and databases relating molecular sequences and biological functions. Sponsors: This group is supported by the Swiss Institute of Bioinformatics (SIB).

Proper citation: Computational Cancer Genomics Group (RRID:SCR_000772) Copy   

•   Source: SciCrunch Registry

http://www.genet.sickkids.on.ca/cftr/

Collection of mutations in CFTR gene for international cystic fibrosis genetics research community. Provides up to date information about individual mutations in CFTR gene. All known CFTR mutations and sequence variants have been converted to standard nomenclature recommended by Human Genome Variation Society. On line process for submission of new mutations has been added.While they continue to ensure quality of data, they urge international community to give them feedback and suggestions. Clinical information in this database relates only to details of discovery of specific mutations. As part of 2010 upgrade, CFTR1 joined new project called CFTR2 - Clinical and Functional TRanslation of CFTR. Links to CFTR2 for many mutations in CFTR1 will provide up-to-date summaries of genotype-phenotype information from patient registries around the world.

Proper citation: Cystic Fibrosis Mutation Database (RRID:SCR_000685) Copy   

•   Source: SciCrunch Registry

http://megasoftware.net/

Software integrated tool for conducting automatic and manual sequence alignment, inferring phylogenetic trees, mining web based databases, estimating rates of molecular evolution, and testing evolutionary hypotheses. Used for comparative analysis of DNA and protein sequences to infer molecular evolutionary patterns of genes, genomes, and species over time. MEGA version 4 expands on existing facilities for editing DNA sequence data from autosequencers, mining Web-databases, performing automatic and manual sequence alignment, analyzing sequence alignments to estimate evolutionary distances, inferring phylogenetic trees, and testing evolutionary hypotheses. MEGA version 6 enables inference of timetrees, as it implements RelTime method for estimating divergence times for all branching points in phylogeny.

Proper citation: MEGA (RRID:SCR_000667) Copy   

•   Source: SciCrunch Registry