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http://senselab.med.yale.edu/ordb/

Database of vertebrate olfactory receptors genes and proteins. It supports sequencing and analysis of these receptors by providing a comprehensive archive with search tools for this expanding family. The database also incorporates a broad range of chemosensory genes and proteins, including the taste papilla receptors (TPRs), vomeronasal organ receptors (VNRs), insect olfaction receptors (IORs), Caenorhabditis elegans chemosensory receptors (CeCRs), and fungal pheromone receptors (FPRs). ORDB currently houses chemosensory receptors for more than 50 organisms. ORDB contains public and private sections which provide tools for investigators to analyze the functions of these very large gene families of G protein-coupled receptors. It also provides links to a local cluster of databases of related information in SenseLab, and to other relevant databases worldwide. The database aims to house all of the known olfactory receptor and chemoreceptor sequences in both nucleotide and amino acid form and serves four main purposes: * It is a repository of olfactory receptor sequences. * It provides tools for sequence analysis. * It supports similarity searches (screens) which reduces duplicate work. * It provides links to other types of receptor information, e.g. 3D models. The database is accessible to two classes of users: * General public www users have full access to all the public sequences, models and resources in the database. * Source laboratories are the laboratories that clone olfactory receptors and submit sequences in the private or public database. They can search any sequence they deposited to the database against any private or public sequence in the database. This user level is suited for laboratories that are actively cloning olfactory receptors.

Proper citation: Olfactory Receptor DataBase (RRID:SCR_007830) Copy   

•   Source: SciCrunch Registry

http://organelledb.lsi.umich.edu/

Database of organelle proteins, and subcellular structures / complexes from compiled protein localization data from organisms spanning the eukaryotic kingdom. All data may be downloaded as a tab-delimited text file and new localization data (and localization images, etc) for any organism relevant to the data sets currently contained in Organelle DB is welcomed. The data sets in Organelle DB encompass 138 organisms with emphasis on the major model systems: S. cerevisiae, A. thaliana, D. melanogaster, C. elegans, M. musculus, and human proteins as well. In particular, Organelle DB is a central repository of yeast protein localization data, incorporating results from both previous and current (ongoing) large-scale studies of protein localization in Saccharomyces cerevisiae. In addition, we have manually curated several recent subcellular proteomic studies for incorporation in Organelle DB. In total, Organelle DB is a singular resource consolidating our knowledge of the protein composition of eukaryotic organelles and subcellular structures. When available, we have included terms from the Gene Ontologies: the cellular component, molecular function, and biological process fields are discussed more fully in GO. Additionally, when available, we have included fluorescent micrographs (principally of yeast cells) visualizing the described protein localization. Organelle View is a visualization tool for yeast protein localization. It is a visually engaging way for high school and undergraduate students to learn about genetics or for visually-inclined researchers to explore Organelle DB. By revealing the data through a colorful, dimensional model, we believe that different kinds of information will come to light.

Proper citation: Organelle DB (RRID:SCR_007837) 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://modencode.org/

A comprehensive encyclopedia of genomic functional elements in the model organisms C. elegans and D. melanogaster. modENCODE is run as a Research Network and the consortium is formed by 11 primary projects, divided between worm and fly, spanning the domains of gene structure, mRNA and ncRNA expression profiling, transcription factor binding sites, histone modifications and replacement, chromatin structure, DNA replication initiation and timing, and copy number variation. The raw and interpreted data from this project is vetted by a data coordinating center (DCC) to ensure consistency and completeness. The entire modENCODE data corpus is now available on the Amazon Web Services EC2 cloud. What this means is that virtual machines and virtual compute clusters that you run within the EC2 cloud can mount the modENCODE data set in whole or in part. Your software can run analyses against the data files directly without experiencing the long waits and logistics associated with copying the datasets over to your local hardware. You may also view the data using GBrowse, Dataset Search, or download the data via FTP, as well as download pre-release datasets.

Proper citation: modENCODE (RRID:SCR_006206) Copy   

•   Source: SciCrunch Registry

http://146.189.76.171/query.php

Tool to search for targets of conserved microRNAs in Caenorhabditis elegans by weighting RISC-immunoprecipitation-enriched parameters.

Proper citation: mirWIP (RRID:SCR_005055) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/funcassociate/

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

Proper citation: FuncAssociate: The Gene Set Functionator (RRID:SCR_005768) Copy   

•   Source: SciCrunch Registry

http://www.btool.org/ADGO2

A web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. It provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. It screens redundant composite annotations when generating and prioritizing them. It also incorporates union and subtracted sets as well as intersection sets. Users can upload their gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets.

Proper citation: ADGO (RRID:SCR_006343) Copy   

•   Source: SciCrunch Registry

http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

http://www.cbs.dtu.dk/services/HMMgene/

Data analysis service for prediction of vertebrate and C. elegans genes.

Proper citation: HMMgene (RRID:SCR_011933) Copy   

•   Source: SciCrunch Registry

http://gpcr.biocomp.unibo.it/bacello/

A predictor for the subcellular localization of proteins in eukaryotes that is based on a decision tree of several support vector machines (SVMs). It classifies up to four localizations for Fungi and Metazoan proteins and five localizations for Plant ones. BaCelLo's predictions are balanced among different classes and all the localizations are considered as equiprobable.

Proper citation: BaCelLo (RRID:SCR_011965) Copy   

•   Source: SciCrunch Registry

http://genie.weizmann.ac.il/pubs/mir07/mir07_data.html

Catalogs of predicted microRNA targets in worm (based on ce6 genome assembly), fly (dm3), mouse (mm9) and human (hg18). We follow standard seed parameter settings and consider seeds of length 6-8 bases, beginning at position 2 of the microRNA. No mismatches or loops are allowed, but a single G:U wobble is allowed in 7- or 8-mers. In genes missing a 3' UTR annotation, 500 bp (fly), 800 bp (human and mouse) or 300 bp (worm) downstream of the annotated end of the coding sequence were used as the predicted UTR. For each organism, a catalog with zero flank and with a flank of 3 and 15 bases upstream and downstream.

Proper citation: PITA (RRID:SCR_010853) Copy   

•   Source: SciCrunch Registry

http://www.uab.edu/medicine/hrfdcc/cores/b

Core whose goals include Generation of New Animal and Cell Models of HRFDs, to establish In Vivo Biosensors to Study Signaling Pathways Involved in HRFD Ciliopathies, and to generate and distribute HRFD Related Biologicals to the Center?s Investigator Base.

Proper citation: UAB Hepatorenal Fibrocystic Diseases Core Center Engineered Models Resource (RRID:SCR_015310) Copy   

•   Source: SciCrunch Registry

http://www.mayo.edu/research/centers-programs/model-systems-core/overview

Core that makes available PKD model systems and technologies to PKD researchers at Mayo and at other institutions. Its services include C. elegans PKD-targeted services, Zebrafish PKD-targeted services, and Rodent PKD-targeted services.

Proper citation: Translational Polycystic Kidney Disease (PKD) Center at Mayo Clinic Rochester Model Systems Core (RRID:SCR_015312) Copy   

•   Source: SciCrunch Registry