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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   

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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.wormbase.org

Central data repository for nematode biology including complete genomic sequence, gene predictions and orthology assignments from range of related nematodes.Data concerning genetics, genomics and biology of C. elegans and related nematodes. Derived from initial ACeDB database of C. elegans genetic and sequence information, WormBase includes genomic, anatomical and functional information of C. elegans, other Caenorhabditis species and other nematodes. Maintains public FTP site where researchers can find many commonly requested files and datasets, WormBase software and prepackaged databases.

Proper citation: WormBase (RRID:SCR_003098) Copy   

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http://genecodis.cnb.csic.es/

Web-based tool for the ontological analysis of large lists of genes. It can be used to determine biological annotations or combinations of annotations that are significantly associated to a list of genes under study with respect to a reference list. As well as single annotations, this tool allows users to simultaneously evaluate annotations from different sources, for example Biological Process and Cellular Component categories of Gene Ontology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneCodis (RRID:SCR_006943) Copy   

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http://www.oeb.harvard.edu/faculty/hartl/old_site/lab/publications/GeneMerge.html

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Web-based and standalone application that returns a wide range of functional genomic data for a given set of study genes and provides rank scores for over-representation of particular functions or categories in the data. It uses the hypergeometric test statistic which returns statistically correct results for samples of all sizes and is the #2 fastest GO tool available (Khatri and Draghici, 2005). GeneMerge can be used with any discrete, locus-based annotation data, including, literature references, genetic interactions, mutant phenotypes as well as traditional Gene Ontology queries. GeneMerge is particularly useful for the analysis of microarray data and other large biological datasets. The big advantage of GeneMerge over other similar programs is that you are not limited to analyzing your data from the perspective of a pre-packaged set of gene-association data. You can download or create gene-association files to analyze your data from an unlimited number of perspectives. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMerge (RRID:SCR_005744) Copy   

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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   

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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   

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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

https://elegansvariation.org/

Supplier and researcher of wild C. elegans strains. CeNDR supplies organisms, analyzes whole-genome sequences, and facilitates genetic mappings to aid researchers in gene discovery.

Proper citation: Caenorhabditis elegans Natural Diversity Resource (CeNDR) (RRID:SCR_014958) Copy   

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https://github.com/cyaguesa/SL-quant/

Source code for a bash pipeline that quantifies splice-leader (SL) trans-splicing events by genes in the nematode C. elegans. It is designed to work downstream of read mapping and takes the reads left unmapped as primary input.

Proper citation: SL-quant (RRID:SCR_016205) Copy   

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http://gfpweb.aecom.yu.edu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 12,2023. Database of expression patterns of C. elegans promoter::GFP constructs. A text description of the observed pattern is provided, indicating the stage(s) and tissue(s) in which GFP is expressed. Also available for some strains are the corresponding 2D and 3D images. Investigators may browse the entire list, search by gene name, tissue, stage, and pattern. Search results may be downloaded in .csv and .txt formats. All of the strains in the expression pattern database are displayed in the browse page. The records are organized by gene; information such as locus name, genomic location (WormBase), the presence of images and videos, and the actual expression pattern are shown in a tabular format.

Proper citation: Expression Patterns for C. elegans promoter GFP fusions (RRID:SCR_001619) Copy   

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http://bodymap.genes.nig.ac.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A taxonomical and anatomical database of latest cross species animal EST data, clustered by UniGene and inter connected by Inparanoid. Users can search by Unigene, RefSeq, or Entrez Gene ID, or search for Gene Name or Tissue type. Data is also sortable and viewable based on qualities of normal, Neoplastic, or other. The last data import appears to be from 2008

Proper citation: BodyMap-Xs (RRID:SCR_001147) Copy   

•   Source: SciCrunch Registry

http://www.cisred.org/

Database for conserved sequence motifs identified by genome scale motif discovery, similarity, clustering, co-occurrence and coexpression calculations. Sequence inputs include low-coverage genome sequence data and ENCODE data. The database offers information on atomic motifs, motif groups and patterns. In promoter-based cisRED databases, sequence search regions for motif discovery extend from 1.5 Kb upstream to 200b downstream of a transcription start site, net of most types of repeats and of coding exons. Many transcription factor binding sites are located in such regions. For each target gene's search region, a base set of probabilistic ab initio discovery tools is used, in parallel, to find over-represented atomic motifs. Discovery methods use comparative genomics with over 40 vertebrate input genomes. In ChIP-seq-based cisRED databases, sequence search regions for motif discovery correspond to significant peaks that represent genome-wide sites of protein-DNA binding. Because such peaks occur in a wide range of genic and intergenic locations, ChIP-seq and promoter-based databases are complementary. Currently, motif discovery for ChIP-seq data uses scan-based approaches that make more explicit use of sets of sequences known to be functional transcription factor binding sites, and that consider a wide range of levels of conservation. For the human STAT1 ChIP-seq database search regions in the target species (human) was selected +/- 300 bp around the ChIP-seq peak maximum. Repeats and coding regions were masked. Multiple sequence alignment were used to assemble orthologous input sequences from other species.

Proper citation: cisRED: cis-regulatory element (RRID:SCR_002098) Copy   

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http://spliceosomedb.ucsc.edu/

A database of proteins and RNAs that have been identified in various purified splicing complexes. Various names, orthologs and gene identifiers of spliceosome proteins have been cataloged to navigate the complex nomenclature of spliceosome proteins. Links to gene and protein records are also provided for the spliceosome components in other databases. To navigate spliceosome assembly dynamics, tools were created to compare the association of spliceosome proteins with complexes that form at specific stages of spliceosome assembly based on a compendium of mass spectrometry experiments that identified proteins in purified splicing complexes.

Proper citation: Spliceosome Database (RRID:SCR_002097) Copy   

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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   

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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   

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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

http://ccb.jhu.edu/software/glimmerhmm/

A gene finder based on a Generalized Hidden Markov Model (GHMM). Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models . Currently, GlimmerHMM's GHMM structure includes introns of each phase, intergenic regions, and four types of exons (initial, internal, final, and single).

Proper citation: GlimmerHMM (RRID:SCR_002654) Copy   

•   Source: SciCrunch Registry

http://www.unihi.org

A database of human molecular interaction networks that integrates human protein-protein and transcriptional regulatory interactions from 15 distinct resources and aims to give direct and easy access to the integrated data set and to enable users to perform network-based investigations. The database includes tools (i) to search for molecular interaction partners of query genes or proteins in the integrated dataset, (ii) to inspect the origin, evidence and functional annotation of retrieved proteins and interactions, (iii) to visualize and adjust the resulting interaction network, (iv) to filter interactions based on method of derivation, evidence and type of experiment as well as based on gene expression data or gene lists and (v) to analyze the functional composition of interaction networks.

Proper citation: Unified Human Interactome (RRID:SCR_005805) 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