Searching the RRID Resource Information Network
Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.
SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
GOTaxExplorer presents a new approach to comparative genomics that integrates functional information and families with the taxonomic classification. It integrates UniProt, Gene Ontology, NCBI Taxonomy, Pfam and SMART in one database. GOTaxExplorer provides four different query types: selection of entity sets, comparison of sets of Pfam families, semantic comparison of sets of GO terms, functional comparison of sets of gene products. This permits to select custom sets of GO terms, families or taxonomic groups. For example, it is possible to compare arbitrarily selected organisms or groups of organisms from the taxonomic tree on the basis of the functionality of their genes. Furthermore, it enables to determine the distribution of specific molecular functions or protein families in the taxonomy. The comparison of sets of GO terms allows to assess the semantic similarity of two different GO terms. The functional comparison of gene products makes it possible to identify functionally equivalent and functionally related gene products from two organisms on the basis of GO annotations and a semantic similarity measure for GO. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: GOTaxExplorer (RRID:SCR_005720) Copy
http://great.stanford.edu/public/html/splash.php
Data analysis service that predicts functions of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. The utility of GREAT extends to data generated for transcription-associated factors, open chromatin, localized epigenomic markers and similar functional data sets, and comparative genomics sets. Platform: Online tool
Proper citation: GREAT: Genomic Regions Enrichment of Annotations Tool (RRID:SCR_005807) Copy
http://webclu.bio.wzw.tum.de/profcom/
Profiling of Complex Functionality (ProfCom) is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of complex function. A complex function is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes. Platform: Online tool
Proper citation: ProfCom - Profiling of complex functionality (RRID:SCR_005797) Copy
The Functional Similarity Search Tool (FSST) has been implemented for comparing user defined sets of annotated entities. FSST supports the computation of functional similarity scores based on an individual ontology and of combined scores. Its multi-threaded Java implementation takes advantage of symmetric multi-processing computers, decreasing runtime considerably. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: FSST - Functional Similarity Search Tool (RRID:SCR_005819) Copy
GOstat is a tool that allows you to find statistically overrepresented Gene Ontologies within a group of genes. The Gene-Ontology database (GO: http://www.geneontology.org) provides a useful tool to annotate and analyze the function of large numbers of genes. Modern experimental techniques, as e.g. DNA microarrays, often result in long lists of genes. To learn about the biology in this kind of data it is desirable to find functional annotation or Gene-Ontology groups which are highly represented in the data. This program (GOstat) should help in the analysis of such lists and will provide statistics about the GO terms contained in the data and sort the GO annotations giving the most representative GO terms first. Run GOstat: * Go to search form - Computes GO statistics of a list of genes selected from a microarray. * GOstat Display - You can store results from a previously run and view them here, either by uploading them as a file or putting them on a selected URL. * Upload Custom GO Annotations - This allows you to upload your own GO annotation database and use it with GOstat. Variants of GOstat: * Rank GOstat - Takes input from all genes on microarray instead of using a fixed cutoff and uses ranks using a Wilcoxon test or either ranks or pvalues to score GOs using Kolmogorov-Smirnov statistics. * Gene Abundance GOstats - Takes input from all genes on microarray and sums up the gene abundances for each GO to compute statistics. * Two list GOstat - Compares GO statistics in two independent lists of genes, not necessarily one of them being the complete list the other list is sampled from. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.
Proper citation: GOstat (RRID:SCR_008535) Copy
http://compbio.charite.de/contao/index.php/ontologizer2.html
The Ontologizer is a Java webstart application for GO term enrichment analysis that provides browsing and graph visualization capabilities. The Ontologizer allows users to analyze data with the standard Fisher exact test and also the parent-child method and topology methods. The tool can be started directly from the web using Java webstart. For graph visualizations, users need to install the GraphViz library. The tool is freely available to all, and source code is available at SourceForge. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: Ontologizer (RRID:SCR_005801) Copy
http://cellfinder.de/about/ontology/
Structured vocabulary to organize cell-associated data and to place these data in clearly defined semantic relations to other biological facts. It describes cell types, their properties and origin and links this information to other existing ontologies like the Cell Ontology (CL), Foundational Model of Anatomy (FMA), Gene Ontology (GO), Mouse Anatomy and others using the top-level ontology BioTop.
Proper citation: CELDA Ontology (RRID:SCR_001601) Copy
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
Can't find your Tool?
We recommend that you click next to the search bar to check some helpful tips on searches and refine your search firstly. Alternatively, please register your tool with the SciCrunch Registry by adding a little information to a web form, logging in will enable users to create a provisional RRID, but it not required to submit.
Welcome to the NIF Resources search. From here you can search through a compilation of resources used by NIF and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that NIF has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on NIF then you can log in from here to get additional features in NIF such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into NIF you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the sources that were queried against in your search that you can investigate further.
Here are the categories present within NIF that you can filter your data on
Here are the subcategories present within this category that you can filter your data on
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
