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http://rostlab.org/services/nlsdb/

A database of nuclear localization signals (NLSs) and of nuclear proteins targeted to the nucleus by NLS motifs. NLSs are short stretches of residues mediating transport of nuclear proteins into the nucleus. The database contains 114 experimentally determined NLSs that were obtained through an extensive literature search. Using "in silico mutagenesis" this set was extended to 308 experimental and potential NLSs. This final set matched over 43% of all known nuclear proteins and matches no currently known non-nuclear protein. NLSdb contains over 6000 predicted nuclear proteins and their targeting signals from the PDB and SWISS-PROT/TrEMBL databases. The database also contains over 12 500 predicted nuclear proteins from six entirely sequenced eukaryotic proteomes (Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana and Saccharomyces cerevisiae). NLS motifs often co-localize with DNA-binding regions. This observation was used to also annotate over 1500 DNA-binding proteins. From this site you can: * Query NLSdb * Find out how to use NLSdb * Browse the entries in NLSdb * Find out if your protein has an NLS using PredictNLS * Predict subcellular localization of your protein using LOCtree

Proper citation: NLSdb: a database of nuclear localization signals (RRID:SCR_003273) Copy   

•   Source: SciCrunch Registry

http://www.reactome.org

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

Proper citation: Reactome (RRID:SCR_003485) Copy   

•   Source: SciCrunch Registry

http://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

http://dnatraffic.ibb.waw.pl/

DNAtraffic database is dedicated to be an unique comprehensive and richly annotated database of genome dynamics during the cell life. DNAtraffic contains extensive data on the nomenclature, ontology, structure and function of proteins related to control of the DNA integrity mechanisms such as chromatin remodeling, DNA repair and damage response pathways from eight model organisms commonly used in the DNA-related study: Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli and Arabidopsis thaliana. DNAtraffic contains comprehensive information on diseases related to the assembled human proteins. Database is richly annotated in the systemic information on the nomenclature, chemistry and structure of the DNA damage and drugs targeting nucleic acids and/or proteins involved in the maintenance of genome stability. One of the DNAtraffic database aim is to create the first platform of the combinatorial complexity of DNA metabolism pathway analysis. Database includes illustrations of pathway, damage, protein and drug. Since DNAtraffic is designed to cover a broad spectrum of scientific disciplines it has to be extensively linked to numerous external data sources. Database represents the result of the manual annotation work aimed at making the DNAtraffic database much more useful for a wide range of systems biology applications. DNAtraffic database is freely available and can be queried by the name of DNA network process, DNA damage, protein, disease, and drug.

Proper citation: DNAtraffic (RRID:SCR_008886) Copy   

•   Source: SciCrunch Registry

http://www.benoslab.pitt.edu/comir/

Data analysis service that predicts whether a given mRNA is targeted by a set of miRNAs. ComiR uses miRNA expression to improve and combine multiple miRNA targets for each of the four prediction algorithms: miRanda, PITA, TargetScan and mirSVR. The composite scores of the four algorithms are then combined using a support vector machine trained on Drosophila Ago1 IP data.

Proper citation: ComiR (RRID:SCR_013023) Copy   

•   Source: SciCrunch Registry

http://dorina.mdc-berlin.de/rbp_browser/dorina.html

In animals, RNA binding proteins (RBPs) and microRNAs (miRNAs) post-transcriptionally regulate the expression of virtually all genes by binding to RNA. Recent advances in experimental and computational methods facilitate transcriptome-wide mapping of these interactions. It is thought that the combinatorial action of RBPs and miRNAs on target mRNAs form a post-transcriptional regulatory code. We provide a database that supports the quest for deciphering this regulatory code. Within doRiNA, we are systematically curating, storing and integrating binding site data for RBPs and miRNAs. Users are free to take a target (mRNA) or regulator (RBP and/or miRNA) centric view on the data. We have implemented a database framework with short query response times for complex searches (e.g. asking for all targets of a particular combination of regulators). All search results can be browsed, inspected and analyzed in conjunction with a huge selection of other genome-wide data, because our database is directly linked to a local copy of the UCSC genome browser. At the time of writing, doRiNA encompasses RBP data for the human, mouse and worm genomes. For computational miRNA target site predictions, we provide an update of PicTar predictions.

Proper citation: doRiNA (RRID:SCR_013222) Copy   

•   Source: SciCrunch Registry

https://github.com/lucventurini/mikado/

Mikado is a lightweight Python3 pipeline whose purpose is to facilitate the identification of expressed loci from RNA-Seq data * and to select the best models in each locus.

Proper citation: Mikado (RRID:SCR_016159) Copy   

•   Source: SciCrunch Registry

http://cbl-gorilla.cs.technion.ac.il/

A tool for identifying and visualizing enriched GO terms in ranked lists of genes. It can be run in one of two modes: * Searching for enriched GO terms that appear densely at the top of a ranked list of genes or * Searching for enriched GO terms in a target list of genes compared to a background list of genes.

Proper citation: GOrilla: Gene Ontology Enrichment Analysis and Visualization Tool (RRID:SCR_006848) Copy   

•   Source: SciCrunch Registry

http://gbrowse.org/

A database and interactive web site for manipulating and displaying annotations on genomes. Features include: detailed views of the genome; use of a variety of premade or personally made glyphs ; customizable order and appearance of tracks by administrators and end-users; search by annotation ID, name, or comment; support of third party annotation using GFF formats; DNA and GFF dumps; connectivity to different databases, including BioSQL and Chado; and a customizable plug-in architecture (e.g. run BLAST, find oligonucleotides, design primers, etc.). GBrowse is distributed as source code for Macintosh OS X, UNIX and Linux platforms, and as pre-packaged binaries for Windows machines. It can be installed using the standard Perl module build procedure, or automated using a network-based install script. In order to use the net installer, you will need to have Perl 5.8.6 or higher and the Apache web server installed. The wiki portion accepts data submissions.

Proper citation: GBrowse (RRID:SCR_006829) Copy   

•   Source: SciCrunch Registry

http://www.wormguides.org/home

A worm atlas that provides an interactive 4D atlas of nuclear positions, from zygote until hatching which can be used to guide cell identification. The tools enable examination of the connectome during development from integrate knowledge of C. elegans embryogenesis to widely used resources, such as WormAtlas and WormBase.

Proper citation: WormGUIDES (RRID:SCR_013733) Copy   

•   Source: SciCrunch Registry

https://www.sourcebioscience.com/products/life-sciences-research/clones/rnai-resources/c-elegans-rnai-collection-ahringer/

C. elegans RNAi feeding library distributed by Source BioScience Ltd. Designed for genome wide study of gene function in C. elegans through loss of function studies.

Proper citation: C. elegans RNAi Collection (Ahringer) (RRID:SCR_017064) Copy   

•   Source: SciCrunch Registry

http://www.pictar.org

An algorithm for the identification of microRNA targets. Details are provided (3' UTR alignments with predicted sites, links to various public databases etc) regarding: # microRNA target predictions in vertebrates (Krek et al, Nature Genetics 37:495-500 (2005)) # microRNA target predictions in seven Drosophila species (Grn et al, PLoS Comp. Biol. 1:e13 (2005)) # microRNA targets in three nematode species (Lall et al, Current Biology 16, 1-12 (2006)) # human microRNA targets that are not conserved but co-expressed (i.e. the microRNA and mRNA are expressed in the same tissue) (Chen and Rajewsky, Nat Genet 38, 1452-1456 (2006)) co-expressed targets

Proper citation: PicTar (RRID:SCR_003343) Copy   

•   Source: SciCrunch Registry

http://www.sbpdiscovery.org/technology/sr/Pages/LaJolla_ModelOrganisms.aspx

Facility that provides Drosophila melanogaster and Caenorhabditis elegans as model systems. It also provides training in how to manipulate the organisms and use equipment and provides technician assistance.

Proper citation: Sanford Burnham Prebys Medical Discovery Institute Model Organisms (RRID:SCR_014853) Copy   

•   Source: SciCrunch Registry

http://www.microrna.org

Database of microRNA target predictions and expression profiles. Target predictions are based on a development of the miRanda algorithm which incorporates current biological knowledge on target rules and on the use of an up-to-date compendium of mammalian microRNAs. MicroRNA expression profiles are derived from a comprehensive sequencing project of a large set of mammalian tissues and cell lines of normal and disease origin. This website enables users to explore: * The set of genes that are potentially regulated by a particular microRNA. * The implied cooperativity of multiple microRNAs on a particular mRNA. * MicroRNA expression profiles in various mammalian tissues. The web resource provides users with functional information about the growing number of microRNAs and their interaction with target genes in many species and facilitates novel discoveries in microRNA gene regulation. The microRNA Target Detection Software, miRanda, is an algorithm for finding genomic targets for microRNAs. This algorithm has been written in C and is available as an open-source method under the GPL., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: microRNA.org (RRID:SCR_006997) Copy   

•   Source: SciCrunch Registry

https://cgc.umn.edu

Center that acquires, maintains, and distributes genetic stocks and information about stocks of the small free-living nematode Caenorhabditis elegans for use by investigators initiating or continuing research on this genetic model organism. A searchable strain database, general information about C. elegans, and links to key Web sites of use to scientists, including WormBase, WormAtlas, and WormBook are available.

Proper citation: Caenorhabditis Genetics Center (RRID:SCR_007341) Copy   

•   Source: SciCrunch Registry

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://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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry