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http://profiles.utsouthwestern.edu/profile/18453/franklin-hamra.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 18,2023. Stock center of Knockout and Transgenic Rats at UT Southwestern in Dallas.

Proper citation: Sperm Stem Cell Libraries for Biological Research (RRID:SCR_014189) Copy   

•   Source: SciCrunch Registry

http://actimetrics.com/products/clocklab/

Point and click program used to quickly analyse circadian activity data using algorithms and embedded controls to make every graph interactive and useful for data analysis. The analysis program has been used for a variety of species including mice, hamsters, rats, sheep, Drosophila, and humans. This program has three separate applications: one for data collection, one for analysis, and a chamber control program.

Proper citation: Clocklab (RRID:SCR_014309) Copy   

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http://edwardslab.bmcb.georgetown.edu/

The Edwards lab conducts research in various aspects of computational biology and bioinformatics, particularly proteomics and mass spectrometry informatics and DNA and protein based signatures for pathogen detection. Some tools provided by Edwards Lab are the PepArML Meta-Search Engine, PeptideMapper Web-Service, Peptide Sequence Databases, Rapid Microorganism Identification Database (RMIDb), and GlycoPeptideSearch. Our primary area of research is the analysis of mass spectrometry experiments for proteomics. Proteomics, the qualitative and quantitative analysis of the expressed proteins of a cell, makes it possible to detect and compare the protein abundance profiles of different samples. Proteins observed to be under or over expressed in disease samples can lead to diagnostic markers or drug targets. The observation of mutated or alternatively spliced protein isoforms may provide domain experts with clues to the mechanisms by which a disease operates. The detection of proteins by mass spectrometry can even signal the presence of airborne microorganisms, such as anthrax, in the detect-to-protect time-frame. Recent research has focused on the discovery of novel peptides in proteomics datasets, improving the sensitivity and specificity of peptide identification using spectral matching with hidden Markov models, and unsupervised machine-learning based peptide identification result combining. Outside of proteomics, we work on computational tools for the design of highly specific oligonucleotides useful for pathogen signatures and PCR assay design. Recent research has focused on precomputing all human oligos of length 20 that are unique up to 4 string edits; and all bacterial 20-mer oligos that are species specific up to 4 string edits.

Proper citation: Edwards Lab (RRID:SCR_008860) Copy   

•   Source: SciCrunch Registry

http://akt.ucsf.edu/EGAN/

Exploratory Gene Association Networks (EGAN) is a software tool that allows a bench biologist to visualize and interpret the results of high-throughput exploratory assays in an interactive hypergraph of genes, relationships (protein-protein interactions, literature co-occurrence, etc.) and meta-data (annotation, signaling pathways, etc.). EGAN provides comprehensive, automated calculation of meta-data coincidence (over-representation, enrichment) for user- and assay-defined gene lists, and provides direct links to web resources and literature (NCBI Entrez Gene, PubMed, KEGG, Gene Ontology, iHOP, Google, etc.). EGAN functions as a module for exploratory investigation of analysis results from multiple high-throughput assay technologies, including but not limited to: * Transcriptomics via expression microarrays or RNA-Seq * Genomics via SNP GWAS or array CGH * Proteomics via MS/MS peptide identifications * Epigenomics via DNA methylation, ChIP-on-Chip or ChIP-Seq * In-silico analysis of sequences or literature EGAN has been built using Cytoscape libraries for graph visualization and layout, and is comparable to DAVID, GSEA, Ingenuity IPA and Ariadne Pathway Studio. There are pre-collated EGAN networks available for human (Homo sapiens), mouse (Mus musculus), rat (Rattus norvegicus), chicken (Gallus gallus), zebrafish (Danio rerio), fruit fly (Drosophila melanogaster), nematode (Caenorhabditis elegans), mouse-ear cress (Arabidopsis thaliana), rice (Oryza sativa) and brewer's yeast (Saccharomyces cerevisiae). There is now an EGAN module available for GenePattern (human-only). Platform: Windows compatible, Mac OS X compatible, Linux compatible

Proper citation: EGAN: Exploratory Gene Association Networks (RRID:SCR_008856) Copy   

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http://www.rrrc.us/

Supplies biomedical investigators with rat models, embryonic stem cells, related reagents, and protocols they require for their research. In addition to repository, cryostorage and distribution functions, RRRC can facilitate acquisition of rat strains from other international repositories as well as provide consultation and technical training to investigators using rat models.

Proper citation: Rat Resource and Research Center (RRID:SCR_002044) Copy   

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http://ccdb.ucsd.edu

THIS RESOURCE IS NO LONGER IN SERVICE, documented June 5, 2017. It has been merged with Cell Image Library. Database for sharing and mining cellular and subcellular high resolution 2D, 3D and 4D data from light and electron microscopy, including correlated imaging that makes unique and valuable datasets available to the scientific community for visualization, reuse and reanalysis. Techniques range from wide field mosaics taken with multiphoton microscopy to 3D reconstructions of cellular ultrastructure using electron tomography. Contributions from the community are welcome. The CCDB was designed around the process of reconstruction from 2D micrographs, capturing key steps in the process from experiment to analysis. The CCDB refers to the set of images taken from microscope the as the Microscopy Product. The microscopy product refers to a set of related 2D images taken by light (epifluorescence, transmitted light, confocal or multiphoton) or electron microscopy (conventional or high voltage transmission electron microscopy). These image sets may comprise a tilt series, optical section series, through focus series, serial sections, mosaics, time series or a set of survey sections taken in a single microscopy session that are not related in any systematic way. A given set of data may be more than one product, for example, it is possible for a set of images to be both a mosaic and a tilt series. The Microscopy Product ID serves as the accession number for the CCDB. All microscopy products must belong to a project and be stored along with key specimen preparation details. Each project receives a unique Project ID that groups together related microscopy products. Many of the datasets come from published literature, but publication is not a prerequisite for inclusion in the CCDB. Any datasets that are of high quality and interest to the scientific community can be included in the CCDB.

Proper citation: Cell Centered Database (RRID:SCR_002168) Copy   

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http://biodev.extra.cea.fr/interoporc/

Automatic prediction tool to infer protein-protein interaction networks, it is applicable for lots of species using orthology and known interactions. The interoPORC method is based on the interolog concept and combines source interaction datasets from public databases as well as clusters of orthologous proteins (PORC) available on Integr8. Users can use this page to ask InteroPorc for all species present in Integr8. Some results are already computed and users can run InteroPorc to investigate any other species. Currently, the following databases are processed and merged (with datetime of the last available public release for each database used): IntAct, MINT, DIP, and Integr8.

Proper citation: InteroPorc (RRID:SCR_002067) Copy   

•   Source: SciCrunch Registry

http://www.theearlab.org

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 13, 2026. Computationally oriented experimental laboratory interested in the encoding of auditory information in the cerebral cortex and brainstem, and in the mechanisms of tinnitus and the effect of various drugs (Lidocaine, steroids, anti-oxidants) in relieving noise trauma induced tinnitus. The ferret (Mustela putorius) and the rat serve as their system model. Through chronic implants, they obtain electrophysiological data from awake behaving animals in order to investigate the response properties and functional organization of the auditory system, both in health and after noise trauma that induces tinnitus in rats. Projects: * Response Modulation to Ongoing Broadband Sounds in Primary Auditory Cortex * Neuronal Response Characteristics in the Inferior Colliculus of the Awake Ferret and Rat * Spectro-Temporal Representation of Feature Onsets in Primary Auditory Cortex * Targeting the changes in inferior colliculus induced by tinnitus

Proper citation: Ear Lab (RRID:SCR_002531) Copy   

•   Source: SciCrunch Registry

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://genenet2.uthsc.edu/geneinfoviz/search.php

GeneInfoViz is a web based tool for batch retrieval of gene function information, visualization of GO structure and construction of gene relation networks. It takes a input list of genes in the form of LocusLink ID, UniGeneID, gene symbol, or accession number and returns their functional genomic information. Based on the GO annotations of the given genes, GeneInfoViz allows users to visualize these genes in the DAG structure of GO, and construct a gene relation network at a selected level of the DAG. Platform: Online tool

Proper citation: GeneInfoViz (RRID:SCR_005680) Copy   

•   Source: SciCrunch Registry

http://neuroviisas.med.uni-rostock.de/neuroviisas.html

An open framework for integrative data analysis, visualization and population simulations for the exploration of network dynamics on multiple levels. This generic platform allows the integration of neuroontologies, mapping functions for brain atlas development, and connectivity data administration; all of which are required for the analysis of structurally and neurobiologically realistic simulations of networks. What makes neuroVIISAS unique is the ability to integrate neuroontologies, image stacks, mappings, visualizations, analyzes and simulations to use them for modelling and simulations. Based on the analysis of over 2020 tracing studies, atlas terminologies and registered histological stacks of images, neuroVIISAS permits the definition of neurobiologically realistic networks that are transferred to the simulation engine NEST. The analysis on a local and global level, the visualization of connectivity data and the results of simulations offer new possibilities to study structural and functional relationships of neural networks. neuroVIISAS provide answers to questions like: # How can we assemble data of tracing studies? (Metastudy) # Is it possible to integrate tracing and brainmapping data? (Data Integration) # How does the network of analyzed tracing studies looks like? (Visualization) # Which graph theoretical properties posses such a network? (Analysis) # Can we perform population simulations of a tracing study based network? (Simulation and higher level data integration) neuroVIISAS can be used to organize mapping and connectivity data of central nervous systems of any species. The rat brain project of neuroVIISAS contains 450237 ipsi- and 175654 contralateral connections. A list of evaluated tracing studies are available. PyNEST script generation does work using WINDOWS OS, however, the script must be transferred to a UNIX OS with installed NEST. The results file of the NEST simulation can be visualized and analyzed by neuroVIISAS on a WINDOWS OS.

Proper citation: neuroVIISAS (RRID:SCR_006010) Copy   

•   Source: SciCrunch Registry

http://phenomebrowser.net/

PhenomeNet is a cross-species phenotype similarity network. It contains the experimentally observed phenotypes of multiple species as well as the phenotypes of human diseases. PhenomeNet provides a measure of phenotypic similarity between the phenotypes it contains. The latest release (from 22 June 2012) contains 124,730 complex phenotype nodes taken from the yeast, fish, worm, fly, rat, slime mold and mouse model organism databases as well as human disease phenotypes from OMIM and OrphaNet. The network is a complete graph in which edge weights represent the degree of phenotypic similarity. Phenotypic similarity can be used to identify and prioritize candidate disease genes, find genes participating in the same pathway and orthologous genes between species. To compute phenotypic similarity between two sets of phenotypes, we use a weighted Jaccard index. First, phenotype ontologies are used to infer all the implications of a phenotype observation using several phenotype ontologies. As a second step, the information content of each phenotype is computed and used as a weight in the Jaccard index. Phenotypic similarity is useful in several ways. Phenotypic similarity between a phenotype resulting from a genetic mutation and a disease can be used to suggest candidate genes for a disease. Phenotypic similarity can also identify genes in a same pathway or orthologous genes. PhenomeNet uses the axioms in multiple species-dependent phenotype ontologies to infer equivalent and related phenotypes across species. For this purpose, phenotype ontologies and phenotype annotations are integrated in a single ontology, and automated reasoning is used to infer equivalences. Specifically, for every phenotype, PhenomeNet infers the related mammalian phenotype and uses the Mammalian Phenotype Ontology for computing phenotypic similarity. Tools: * PhenomeBLAST - A tool for cross-species alignments of phenotypes * PhenomeDrug - method for drug-repurposing

Proper citation: phenomeNET (RRID:SCR_006165) Copy   

•   Source: SciCrunch Registry

http://www-personal.umich.edu/~brdsmith/Research.html

Data set of image collections and movies including Magnetic Resonance Imaging of Embryos, Human Embryo Imaging, MRI of Cardiovascular Development, and Live Embryo Imaging. Individual MRI slice images, three-dimensional images, animations, stereo-pair animations, animations of organ systems, and photo-micrographs are included.

Proper citation: Brad Smith Magnetic Resonance Imaging of Embryos (RRID:SCR_006300) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu

Database for genetic, genomic, phenotype, and disease data generated from rat research. Centralized database that collects, manages, and distributes data generated from rat genetic and genomic research and makes these data available to scientific community. Curation of mapped positions for quantitative trait loci, known mutations and other phenotypic data is provided. Facilitates investigators research efforts by providing tools to search, mine, and analyze this data. Strain reports include description of strain origin, disease, phenotype, genetics, immunology, behavior with links to related genes, QTLs, sub-strains, and strain sources.

Proper citation: Rat Genome Database (RGD) (RRID:SCR_006444) 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://mirdb.org/miRDB/

An online database for miRNA target prediction and functional annotations.

Proper citation: miRDB (RRID:SCR_010848) Copy   

•   Source: SciCrunch Registry

http://targetscan.org/

Web tool to predict biological targets of miRNAs by searching for presence of conserved 8mer, 7mer and 6mer sites that match seed region of each miRNA. Nonconserved sites are also predicted and sites with mismatches in seed region that are compensated by conserved 3' pairing. Used to search for predicted microRNA targets in mammals.

Proper citation: TargetScan (RRID:SCR_010845) Copy   

•   Source: SciCrunch Registry

http://adacgh.bioinfo.cnio.es/

A web tool for the analysis of aCGH data sets. They focus on calling gains and losses and estimating the number of copy changes. Note: ADaCGH will continue being maintained, but is deprecated. Their new tool for CGH and CNV is WaviCGH, http://wavi.bioinfo.cnio.es/

Proper citation: ADaCGH (RRID:SCR_010916) Copy   

•   Source: SciCrunch Registry

https://monarchinitiative.org/

Repository of information about model organisms, in vitro models, genes, pathways, gene expression, protein and genetic interactions, orthology, disease, phenotypes, publications, and authors, and ability to navigate multi-scale spatial and temporal phenotypes across in vivo and in vitro model systems in context of genetic and genomic data, using semantics and statistics. Discovery system provides basic and clinical science researchers, informaticists, and medical professionals with integrated interface and set of discovery tools to reveal genetic basis of disease, facilitate hypothesis generation, and identify novel candidate drug targets. Database that indexes authoritative information on experimental models of disease from MGI, RGD and ZFIN.

Proper citation: MONARCH Initiative (RRID:SCR_000824) Copy   

•   Source: SciCrunch Registry

http://ctdbase.org/

A public database that enhances understanding of the effects of environmental chemicals on human health. Integrated GO data and a GO browser add functionality to CTD by allowing users to understand biological functions, processes and cellular locations that are the targets of chemical exposures. CTD includes curated data describing cross-species chemical–gene/protein interactions, chemical–disease and gene–disease associations to illuminate molecular mechanisms underlying variable susceptibility and environmentally influenced diseases. These data will also provide insights into complex chemical–gene and protein interaction networks.

Proper citation: Comparative Toxicogenomics Database (CTD) (RRID:SCR_006530) Copy   

•   Source: SciCrunch Registry