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http://www.lasige.di.fc.ul.pt/webtools/proteinon/

ProteInOn calculates semantic similarity between GO terms or proteins annotated with GO terms. It also calculates term enrichment of protein sets, by applying a term representativity score, and gives additional information on protein interactions. The query compute protein semantic similarity returns the semantic similarity scores between all proteins entered, in matrix format. The option Measure allows users to choose one of several semantic similarity measures: Resnik, Lin, or Jiang & Conrath's measures with or without the DCA approach, plus the graph-based simUI and simGIC measures. These measures are listed by order of performance as evaluated with protein sequence similarity. The option GO type allows users to choose one of the aspects of GO: molecular function, biological process and cellular component. The option Ignore IEA limits the query to non-electronic annotations, excluding evidence types: IEA, NAS, ND, NR.

Proper citation: ProteInOn (RRID:SCR_005740) Copy   

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http://www.animalgenome.org/bioinfo/tools/catego/

CateGOrizer takes batch input of GO term IDs in a list format or unformatted plain text file, allows users to choose one of the available classifications such as GO_slim, GOA, EGAD, MGI_GO_slim, GO-ROOT, or a self-defined classification list, find its parental branch and performs an accumulative classification count, and returns the results in a sorted table of counts, percentages, and a pie chart (if it takes longer than standard time out period, it will email the user with a URL link to the results). This tool is comprised with a set of perl CGI programs coupled with a MySQL DBMS that stores the GO terms DAG data. Platform: Online tool

Proper citation: CateGOrizer (RRID:SCR_005737) Copy   

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http://probeexplorer.cicancer.org/principal.php

Probe Explorer is an open access web-based bioinformatics application designed to show the association between microarray oligonucleotide probes and transcripts in the genomic context, but flexible enough to serve as a simplified genome and transcriptome browser. Coordinates and sequences of the genomic entities (loci, exons, transcripts), including vector graphics outputs, are provided for fifteen metazoa organisms and two yeasts. Alignment tools are used to built the associations between Affymetrix microarrays probe sequences and the transcriptomes (for human, mouse, rat and yeasts). Search by keywords is available and user searches and alignments on the genomes can also be done using any DNA or protein sequence query. Platform: Online tool

Proper citation: ProbeExplorer (RRID:SCR_007116) Copy   

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http://www.medinfopoli.polimi.it/GFINDer/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 16, 2019. Multi-database system providing large-scale lists of user-classified sequence identifiers with genome-scale biological information and functional profiles biologically characterizing the different gene classes in the list. GFINDer automatically retrieves updated annotations of several functional categories from different sources, identifies the categories enriched in each class of a user-classified gene list, and calculates statistical significance values for each category. Moreover, GFINDer enables to functionally classify genes according to mined functional categories and to statistically analyze the obtained classifications, aiding in better interpreting microarray experiment results.

Proper citation: GFINDer: Genome Function INtegrated Discoverer (RRID:SCR_008868) Copy   

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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   

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http://funspec.med.utoronto.ca/

FunSpec is a web-based tool for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations, including GO terms. FunSpec (an acronym for Functional Specification) inputs a list of yeast gene names, and outputs a summary of functional classes, cellular localizations, protein complexes, etc. that are enriched in the list. The classes and categories evaluated were downloaded from the MIPS Database and the GO Database . In addition, many published datasets have been compiled to evaluate enrichment against. Hypertext links to the publications are given. The p-values, calculated using the hypergeometric distribution, represent the probability that the intersection of given list with any given functional category occurs by chance. The Bonferroni-correction divides the p-value threshold, that would be deemed significant for an individual test, by the number of tests conducted and thus accounts for spurious significance due to multiple testing over the categories of a database. After the Bonferroni correction, only those categories are displayed for which the chance probability of enrichment is lower than: p-value/#CD where #CD is the number of categories in the selected database. Without the Bonferroni Correction, all categories are displayed for which the same probability of enrichment is lower than: p-value threshold in an individual test Note that many genes are contained in many categories, especially in the MIPS database (which are hierarchical) and that this can create biases for which FunSpec currently makes no compensation. Also the databases are treated as independent from one another, which is really not the case, and each is searched seperately, which may not be optimal for statistical calculations. Nonetheless, we find it useful for sifting through the results of clustering analysis, TAP pulldowns, etc. Platform: Online tool

Proper citation: FunSpec (RRID:SCR_006952) Copy   

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Ratings or validation data are available for this resource

http://www.avadis-ngs.com

Software integrated platform that provides analysis, management and visualization tools for next-generation sequencing data. It supports workflows for RNA-Seq, DNA-Seq, ChIP-Seq and small RNA-Seq experiments. Avadis has a built-in Gene Ontology browser to view ontology hierarchies. There are common ontology paths for multiple genes. Platform has collection of data / text mining algorithms, data visualization libraries, workflow/application automation layers, and enterprise data organization functions. These functions are available as libraries that allow developers to rapidly build software prototypes, applications and off-the-shelf products. The collection of algorithms and visualizations in AVADIS grows as new applications using the platform are developed. Currently, the algorithms that AVADIS platform contains range from general purpose statistical mining and modelling algorithms, to text mining algorithms, to very application-specific algorithms for microarray / NGS data analysis, QSAR modelling and biological networks analysis. AVADIS has a collection of powerful mining algorithms like PCA, ANOVA, T-test, clustering, classification and regression methods. The range of visualizations includes most statistical and data modelling related graphing views, and very application-specific visualizations. Some of the statistical views include 2D/3D scatter plots, profile plots, heat maps, histograms and matrix plot; data modelling relevant views include dendrograms, cluster profiles, similarity images and SOM U-matrices. Application-specific views in AVADIS include pathway network views, genome browsers, chemical structure views and pipe-line views. Platform: Windows compatible, Mac OS X compatible, Linux compatible,

Proper citation: Avadis (RRID:SCR_000644) Copy   

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http://www.cs.cmu.edu/~jernst/stem/

The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Short Time-series Expression Miner (STEM) (RRID:SCR_005016) Copy   

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http://www.bumc.bu.edu/cardiovascularproteomics/cpctools/strap/

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

Proper citation: STRAP (RRID:SCR_005675) Copy   

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http://www.cs.ualberta.ca/~bioinfo/PA/GOSUB/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 30, 2015. Refer to Proteome Analyst 3.0. Subcellular Localization and GO General Molecular Function predictions for many model organism proteomes using Protein Analyst, with a very high coverage rate. When users blast their proteins against the database of results, they will not only be shown blast homologs from the model organisms, but also the Subcellular Localization and GO General Molecular Function predictions as well.

Proper citation: Proteome Analyst PA-GOSUB (RRID:SCR_008234) Copy   

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http://david.abcc.ncifcrf.gov/content.jsp?file=/ease/ease1.htm&type=1

Windows(c) desktop software application, customizable and standalone, that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. Provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools. Offers statistical models to deal with multi-test comparison problem. Platform: Windows compatible

Proper citation: EASE: the Expression Analysis Systematic Explorer (RRID:SCR_013361) Copy   

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http://www.ucl.ac.uk/cardiovasculargeneontology/

Full Gene Ontology annotation to genes associated with cardiovascular processes. Every GO annotation made, is attributed to an identified source, such as a publication identifier (PMID), and an indication of the type of evidence which supports the association between the gene product and the GO term. Over 4,000 cardiovascular associated genes have been identified. A variety of tools have been provided to enable cardiovascular scientists to review the annotation of their ''''favorite'''' gene and suggest information that may be missing, inaccurate or incomplete in these annotations. Annotation suggestions can be sent through the feedback form or by email. The Gene Ontology (GO) vocabulary is the established standard for the functional annotation of gene products. By using GO to curate scientific literature and by integrating results from high-quality high-throughput experiments they will create an information-rich resource for the cardiovascular-research community, enabling researchers to rapidly evaluate and interpret existing data and generate hypotheses to guide future research.

Proper citation: Cardiovascular Gene Ontology Annotation Initiative (RRID:SCR_004795) Copy   

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http://www.geneontology.org/GO.refgenome.shtml

The GO Consortium coordinates an effort to maximize and optimize the GO annotation of a large and representative set of key genomes, known as ''reference genomes''. The goal of the Reference Genome Annotation project is to completely annotate twelve reference genomes so that those annotations may be used to effectively seed the automatic annotation efforts of other genomes. With more and more genomes being sequenced, we are in the middle of an explosion of genomic information. The limited resources to manually annotate the growing number of sequenced genomes imply that automatic annotation will be the method of choice for many groups. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO''s logical structure and biological content. All GO annotations from this project are included in the gene association files that each group submits to GO. Annotations can also be viewed using the GO search engine and browser AmiGO. Annotated families can be viewed with the homolog set browser.

Proper citation: RefGenome (RRID:SCR_004263) Copy   

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http://bioconductor.org/packages/release/bioc/html/topGO.html

Software package which provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied.

Proper citation: topGO (RRID:SCR_014798) Copy   

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http://www.wandora.org/wandora/wiki/index.php?title=Main_Page

Wandora is a general purpose information extraction, management and publishing application based on Topic Maps and Java. Wandora has graphical user interface, layered and merging information model, multiple visualization models, huge collection of information extraction, import and export options, embedded HTTP server with several output modules and open plug-in architecture. Wandora is a FOSS application with GNU GPL license. Wandora is well suited for constructing ontologies and information mashups. Wandora is capable of extracting and converting a wide range of open data feeds to topic map formats. Beyond topic map conversion, this feature allows Wandora user to aggregate multidimensional information mashups where information from Flickr interleaves with information from GeoNames and YouTube, for example. Wandora is a software application to build, edit, publish and visualize information graphs, especially topic maps. Wandora is written in Java and suits for * Collecting, combining, aggregating, managing, refining and publishing information and knowledge graphs * Designing information, information modeling and prototyping * Information mashups * Ontology creation and management * Mind and concept mapping * Language technology applications * Graph visualizations * Knowledge format conversions * Digital preservation * Data journalism * Open data projects * Linked data projects Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Wandora (RRID:SCR_005689) Copy   

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http://systemsbio.ucsd.edu/GoSurfer/

GoSurfer uses Gene Ontology (GO) information to analyze gene sets obtained from genome-wide computations or microarray analyses. GoSurfer is a graphical interactive data mining tool. It associates user input genes with GO terms and visualizes such GO terms as a hierarchical tree. Users can manipulate the tree output by various means, like setting heuristic thresholds or using statistical tests. Significantly important GO terms resulted from a statistical test can be highlighted. All related information are exportable either as texts or as graphics. Platform: Windows compatible

Proper citation: GoSurfer (RRID:SCR_005789) Copy   

•   Source: SciCrunch Registry