We support boolean queries, use +,-,<,>,~,* to alter the weighting of terms
With the CGAP GO browser, you can browse through the GO vocabularies, and find human and mouse genes assigned to each term. GO data updated every few months. Platform: Online tool
Software program that automatically annotates a protein list with information that helps in the meaningful interpretation of data from mass spectrometry and other techniques. It takes protein lists as input, in the form of plain text files, protXML files (usually from the TPP), or Dat files from MASCOT search results. From this, it generates protein annotation tables, and a variety of GO charts to aid individual and differential analysis of proteomics data. It downloads information from mainly the Uniprot and EBI QuickGO databases. STRAP requires Windows XP or higher with at least version 3.5 of the Microsoft .NET Framework installed. Platform: Windows compatible
THIS RESOURCE IS NO LONGER IN SERVICE, documented on February 07, 2013. Web based ontological visualization tool for interactive visualization of any ontological hierarchy for a specific node of interest, up to the chosen level of children and/or ancestor. It takes any ontology file in OBO format as input and generates output as DAG hierarchical graph for the chosen query. To enhance the navigation capabilities of complex networks, we have embedded several features such as search criteria, zoom in/out, center focus, nearest neighbor highlights and mouse hover events. The application has been tested on all 72 data sets available in OBO format through OBO foundry. The results for few of them can be accessed through OntoVisT-Gallery.
A collection of information about biodiversity compiled collaboratively by hundreds of expert and amateur contributors. Its goal is to contain a page with pictures, text, and other information for every species and for each group of organisms, living or extinct. Connections between Tree of Life web pages follow phylogenetic branching patterns between groups of organisms, so visitors can browse the hierarchy of life and learn about phylogeny and evolution as well as the characteristics of individual groups.
An R package for network-based feature (gene) selection for biomarkers discovery via integrating biological information. The package adapts the following 5 algorithms for classifying and predicting gene expression data using prior knowledge: # average gene expression of pathway (aep); # pathway activities classification (PAC); # Hub network classification (hubc); # filter via top ranked genes (FrSVM); # network smoothed t-statistic (stSVM).
THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 18,2023. Short reads mapping software for bisulfite sequencing reads.
The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independently of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function and chromosome location. Statistical significance values are calculated for each category. We demonstrated the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer data sets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms (Draghici et.al, Genomics, 81(2), 2003). Other results obtained with Onto-Express can be found in Khatri et.al., Genomics. 79(2), 2002. Custom level of abstraction of the Gene Ontology. User account required. Platform: Online tool
Microarrays are at the center of a revolution in biotechnology, allowing researchers to screen tens of thousands of genes simultaneously. Typically, they have been used in exploratory research to help formulate hypotheses. In most cases, this phase is followed by a more focused, hypothesis driven stage in which certain specific biological processes and pathways are thought to be involved. Since a single biological process can still involve hundreds of genes, microarrays are still the preferred approach as proven by the availability of focused arrays from several manufacturers. Since focused arrays from different manufacturers use different sets of genes, each array will represent any given regulatory pathway to a different extent. We argue that a functional analysis of the arrays available should be the most important criterion used in the array selection. We developed Onto-Compare as a database that can provide this functionality, based on the GO nomenclature. Compare commercially available microarrays based on GO. User account required. Platform: Online tool
OBO-Edit is an open source, platform-independent application written in Java for viewing and editing any OBO format ontologies. OBO-Edit is a graph-based tool; its emphasis on the overall graph structure of an ontology provides a friendly interface for biologists, and makes OBO-Edit excellent for the rapid generation of large ontologies focusing on relationships between relatively simple classes. The UI components are cleanly separated from the data model and data adapters, so these can be reused in other applications. The oboedit foward-chaining reasoner can also be used independently (for example, for traversing ontology graphs). OBO-Edit uses the OBO format flat file. See the GO wiki, http://wiki.geneontology.org/index.php/OBO-Edit:_Getting_the_Source_Code, for instructions on downloading the source code. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Network Ontology Analysis (NOA) (abbreviated to NOA) is a freely available collection of Gene Ontology tools aiming to analyze functions of gene network instead of gene list. Network rewiring facilitates the function changes between conditions even with the same gene list. Therefore, it is necessary to annotate the specific function of networks by considering the fundamental roles of interactions from the viewpoint of systems biology. NOA is such a novel functional enrichment analysis method capable to handle both dynamic and static networks. The application of NOA in biological networks shows that NOA can not only capture changing functions in rewiring networks but also find more relevant and specific functions in traditional static networks. Platform: Online tool
go-moose is intended as a replacement for the aging go-perl and go-db-perl Perl libraries. It is written using the object oriented Moose libraries. It can be used for performing a number of analyses on GO data, including the remapping of GO annotations to a selected subset of GO terms. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Service to summarize the GO function associated with a data set using prepared GO Slim sets. The input is a tab separated list of gene product IDs and GO IDs.
THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. KI Biobank - Gallstone aims at investigating genetics of gallstone disease on Swedish Twins. Types of samples * EDTA whole blood * DNA * Plasma Number of sample donors: 82
Web-service providing access to database that brings together information from broad range of resources. Web application for functional annotation and statistical hypothesis testing. Provides tools for analysis of genomic and microarray data. Collection of tools include Bibliographic Information,Databases,Gene Annotation,Gene Regulation, Microarray,Proteins,Sequence Manipulation - Nucleic Acids,Sequence Manipulation - Protein, Systems Biology.
Gene Ontology Browsing Utility (GOBU) (GOBU) is a Java-based software program for integrating biological annotation catalogs under an extendable software architecture. Users may interact with the Gene Ontology and user-defined hierarchy data of genes, and then use its plugins to (and not limited to) (1) browse the GO hierarchy with user defined data, (2) browse GO-oriented expression levels in the user data, (3) compute GO enrichment, and/or (4) customize data reporting. A set of classes and utility functions has been established so that a customized program can be made as a plugin or a command-line tool that programmically manipulate the Gene Ontology and specified user data. See the source code repository for examples. Reference Lin WD, Chen YC, Ho JM, Hsiao CD. GOBU: Toward an Integration Interface for Biological Objects. Journal of Information Science and Engineering. 2006 22(1):19-29. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Collects mammalian cis- and trans-regulatory elements together with experimental evidence. Regulatory elements were mapped on to assembled genomes. Resource for gene regulation and function studies. Users can retrieve primers, search TF target genes, retrieve TF motifs, search Gene Regulatory Networks and orthologs, and make use of sequence analysis tools. Uses databases such as Genbank, EPD and DBTSS, and employ promoter finding program FirstEF combined with mRNA/EST information and cross-species comparisons. Manually curated.
Audio clips that highlight research efforts at the National Institute on Drug Abuse and include interviews with prominent NIDA scientists. To listen to these clips, just click Listen Now under the clip summary. You must have Real Media Player or Windows Media Player installed to download these clips. To view a printable transcript of a clip, click View Transcript under the clip summary.
A public repository of information on tandem repeats in genomic DNA and contains a variety of tools for their analysis. These currently include the Tandem Repeats Finder algorithm, query and filtering capabilities for finding particular repeats of interest, repeat clustering algorithms based on sequence similarity, polymorphism prediction based on common patterns of mutation, PCR primer selection, and data download in a variety of formats. In addition, TRDB serves as a centralized research workbench, provides storage space for results of analysis, and permits collaborators to privately share their data and analysis.
This TRbase is a relational tandem repeat database that relates tandem repeats to gene locations and disease genes of the human genome. The TRbase stores both perfect and imperfect repeats of 1 to 2000 bp unit lengths that were identified using the Tandem Repeat Finder program. Disease information for all 24 chromosomes was retrieved from the Online Mendelian Inheritance in Man (OMIM) database. There are five main search forms by which the user may query the database: 1. The Advanced tandem repeat search: This allows a complete search for tandem repeats using a combination of criteria, such as total tandem repeat length, repeat unit length, copy number of the repeats, percentage matches and the consensus repeat pattern. On submission, the number of repeats and the detailed tandem repeat characteristics of each repeat that match the user query are tabulated. 2. The Main search: This relates tandem repeat data to genes and diseases. The user may specify a gene of interest to view details of all repeats associated with it or search for tandem repeats present in a particular disease by entering the name/keyword for the disease or the MIM number of the disease gene. 3. The Composite search: This more advanced search allows the user to query specifically for repeats present in exons, introns or intergenic regions of a gene or disease gene. 4. The Gene Search: Further information on genes can be available by a simple gene name search on this page. 5. The Disease search: This allows extensive information on disease genes on all chromosomes of the human genome. Searching for a MIM number, or keyword searches specifying the features of the disease, will retrieve the information on the disease and the chromosome in which the disease gene occurs. Each entry retrieved is linked to the OMIM database for detailed literature and gene map information on the disease.
Platform for sharing, download, and re-analysis or meta-analysis of sophisticated, fully annotated, human electrophysiological data sets. It uses EEG Study Schema (ESS) files to provide task, data collection, and subject metadata, including Hierarchical Event Descriptor (HED) tag descriptions of all identified experimental events. Visospatial task data also available from, http://sccn.ucsd.edu/eeglab/data/headit.html: A 238-channel, single-subject EEG data set recorded at the Swartz Center, UCSD, by Arnaud Delorme, Julie Onton, and Scott Makeig is al.