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https://github.com/yanzhanglab/Graph2GO

Software tool as graph based representation learning method for protein function prediction. Multi modal graph based representation learning model that can integrate heterogeneous information including multiple types of interaction networks including sequence similarity network and protein-protein interaction network, and protein features including amino acid sequence, sub cellular location and protein domains, to predict protein functions on Gene Ontology.

Proper citation: Graph2GO (RRID:SCR_018726) Copy   

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http://www.ici.upmc.fr/cluego/

A Cytoscape plug-in that visualizes the non-redundant biological terms for large clusters of genes in a functionally grouped network. It can be used in combination with GOlorize. The identifiers can be uploaded from a text file or interactively from a network of Cytoscape. The type of identifiers supported can be easily extended by the user. ClueGO performs single cluster analysis and comparison of clusters. From the ontology sources used, the terms are selected by different filter criteria. The related terms which share similar associated genes can be combined to reduce redundancy. The ClueGO network is created with kappa statistics and reflects the relationships between the terms based on the similarity of their associated genes. On the network, the node colour can be switched between functional groups and clusters distribution. ClueGO charts are underlying the specificity and the common aspects of the biological role. The significance of the terms and groups is automatically calculated. ClueGO is easy updatable with the newest files from Gene Ontology and KEGG. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ClueGO (RRID:SCR_005748) Copy   

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http://bioinf-apache.charite.de/supertarget_v2/

Database for analyzing drug-target interactions, it integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present (May 2013), the updated database contains >6000 target proteins, which are annotated with >330 000 relations to 196 000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range.

Proper citation: SuperTarget (RRID:SCR_002696) Copy   

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http://search.cpan.org/dist/ONTO-PERL/

ONTO-PERL is a collection of Perl modules to handle OBO-formatted ontologies (like the Gene Ontology). This code distribution gathers object-oriented modules (for dealing with ontology elements such as Term, Relationship and so forth), scripts (for typical tasks such as format conversions: obo2owl, owl2obo; besides, there are also many examples that can be easily adapted for specific applications), and a set of test files to ensure the suite''''s implementation quality. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: ONTO-PERL (RRID:SCR_005731) Copy   

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http://owlsim.org

Software package that provides the ability to do a number of standard semantic similarity methods and includes novel methods for combining these with dynamic selection of anonymous grouping classes. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: OwlSim (RRID:SCR_006819) 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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http://www.agbase.msstate.edu/cgi-bin/tools/GOanna.cgi

GOanna is used to find annotations for proteins using a similarity search. The input can be a list of IDs or it can be a list of sequences in FASTA format. GOanna will retrieve the sequences if necessary and conduct the specified BLAST search against a user-specified database of GO annotated proteins. The resulting file contains GO annotations of the top BLAST hits. The sequence alignments are also provided so the user can use these to access the quality of the match. Platform: Online tool

Proper citation: GOanna (RRID:SCR_005684) Copy   

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http://www.seqexpress.com/

A comprehensive analysis and visualization software package for gene expression experiments that provides: a number of clustering and analysis techniques; integrated gene expression and analysis result visualizations, integration with the Gene Expression Omnibus; and an optional data sharing architecture. GO is used to assign functional enrichment scores to clusters, using a combination of specially developed techniques and general statistical methods. These results can be explored using the in built ontology browsing tool or through the generated web pages. SeqExpress also supports numerous data transformation, projection, visualization, file export/import, searching, integration (with R), and clustering options.

Proper citation: SeqExpress (RRID:SCR_007075) Copy   

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http://llama.mshri.on.ca/gofish/GoFishWelcome.html

Software program, available as a Java applet online or to download, allows the user to select a subset of Gene Ontology (GO) attributes, and ranks genes according to the probability of having all those attributes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GoFish (RRID:SCR_005682) Copy   

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http://www.ebi.ac.uk/webservices/whatizit/info.jsf

A text processing system that allows you to do textmining tasks on text. It is great at identifying molecular biology terms and linking them to publicly available databases. Whatizit is also a Medline abstracts retrieval/search engine. Instead of providing the text by Copy&Paste, you can launch a Medline search. The abstracts that match your search criteria are retrieved and processed by a pipeline of your choice. Whatizit is also available as 1) a webservice and as 2) a streamed servlet. The webservice allows you to enrich content within your website in a similar way as in the wikipedia. The streamed servlet allows you to process large amounts of text.

Proper citation: Whatizit (RRID:SCR_005824) Copy   

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http://amp.pharm.mssm.edu/l2n/upload/register.php

A web-based software system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system.

Proper citation: Lists2Networks (RRID:SCR_006323) Copy   

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https://biofactoid.org

Web tool for creating digital profile of scientific discoveries in article and connecting them to related research. Authors describe molecular interactions supported by their results, letting researchers explore first hand account of article findings and connect to related articles and knowledge. Web based system for scientists to compose structured representation of networks of interactions between genes, their products, and chemical compounds, represented using power of formal ontology.

Proper citation: Biofactoid (RRID:SCR_021011) Copy   

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http://www.yeastract.com

A curated repository of more than 206000 regulatory associations between transcription factors (TF) and target genes in Saccharomyces cerevisiae, based on more than 1300 bibliographic references. It also includes the description of 326 specific DNA binding sites shared among 113 characterized TFs. Further information about each Yeast gene has been extracted from the Saccharomyces Genome Database (SGD). For each gene the associated Gene Ontology (GO) terms and their hierarchy in GO was obtained from the GO consortium. Currently, YEASTRACT maintains a total of 7130 terms from GO. The nucleotide sequences of the promoter and coding regions for Yeast genes were obtained from Regulatory Sequence Analysis Tools (RSAT). All the information in YEASTRACT is updated regularly to match the latest data from SGD, GO consortium, RSA Tools and recent literature on yeast regulatory networks. YEASTRACT includes DISCOVERER, a set of tools that can be used to identify complex motifs found to be over-represented in the promoter regions of co-regulated genes. DISCOVERER is based on the MUSA algorithm. These algorithms take as input a list of genes and identify over-represented motifs, which can then be compared with transcription factor binding sites described in the YEASTRACT database.

Proper citation: Yeast Search for Transcriptional Regulators And Consensus Tracking (RRID:SCR_006076) Copy   

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http://hoctar.tigem.it/

Database serving as a tool for microRNA target prediction. The HOCTAR procedure is based on the integration of expression profiling and sequence-based miRNA target recognition softwares. HOCTAR database (db) is the first and unique database to use transcriptomic data to score putative miRNA targets looking at the expression behaviour of their host genes, and it includes and re-analyzes all miRNA target predictions generated by softwares such as miRanda, TargetScan and PicTar. The HOCTARdb contains the prediction target lists for 290 human intragenic miRNAs and also provides tentative assignments of miRNA function based on Gene Ontology analyses of their predicted targets. There are two ways to interrogate HOCTARdb: (i) by selecting a miRNA using either an alphabetically sorted pull-down menu in the microRNA query, or (ii) by typing a target gene symbol (HUGO Gene Name-approved) in the Target Gene Name query.

Proper citation: HOCTAR (RRID:SCR_010842) Copy   

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http://bcb.cs.tufts.edu/dflat/

We are an interdisciplinary team dedicated to annotating gene function related to human fetal development. We are contributing new functional annotation to the Gene Ontology, curating and mining gene sets suitable for the interpretation of developmental genomic data, and creating the computational tools needed to apply genomics for better understanding the molecular mechanisms of human development. Our GO annotation is in the process of being incorporated into the GOA public release. The GONE (Gene Ontology Non-Eligible) database is where we store annotations relevant to our research but that don''t quite meet GOA''s standards. Usually an annotation falls into this category because either the gene/protein described is a family of genes/proteins rather than a specific one, there is no UniProt ID to identify the gene/protein in the system, a GO term does not yet exist to describe the particular function, process, or location of the gene/protein, the species is not clearly identifiable in the paper, or the evidence is not as reliable (GO evidence codes TAS and NAS). As individual annotations these are more suspect than current GO annotation. However, for functional analysis of expression data, these gene sets can be valuable even with a certain amount of noise. We also include here a link to the supplementary data from our forthcoming PSB 2011 paper on gene set mining.

Proper citation: DFLAT (RRID:SCR_010738) Copy   

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http://plantgrn.noble.org/LegumeIP/

LegumeIP is an integrative database and bioinformatics platform for comparative genomics and transcriptomics to facilitate the study of gene function and genome evolution in legumes, and ultimately to generate molecular based breeding tools to improve quality of crop legumes. LegumeIP currently hosts large-scale genomics and transcriptomics data, including: * Genomic sequences of three model legumes, i.e. Medicago truncatula, Glycine max (soybean) and Lotus japonicus, including two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt TrEMBL, InterProScan, Gene Ontology and KEGG databases. LegumeIP covers a total 222,217 protein-coding gene sequences. * Large-scale gene expression data compiled from 104 array hybridizations from L. japonicas, 156 array hybridizations from M. truncatula gene atlas database, and 14 RNA-Seq-based gene expression profiles from G. max on different tissues including four common tissues: Nodule, Flower, Root and Leaf. * Systematic synteny analysis among M. truncatula, G. max, L. japonicus and A. thaliana. * Reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species. LegumeIP features comprehensive search and visualization tools to enable the flexible query on gene annotation, gene family, synteny, relative abundance of gene expression.

Proper citation: LegumeIP (RRID:SCR_008906) Copy   

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http://sparkinsight.org

A clustering and visualization tool that enables the interactive exploration of genome-wide data, with a specialization in epigenomics data. Spark is also available as a service within the Epigenome toolset of the Genboree Workbench. The approach utilizes data clusters as a high-level visual guide and supports interactive inspection of individual regions within each cluster. The cluster view links to gene ontology analysis tools and the detailed region view connects to existing genome browser displays taking advantage of their wealth of annotation and functionality.

Proper citation: Spark (RRID:SCR_006207) 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://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.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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