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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   

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http://vortex.cs.wayne.edu/projects.htm#Onto-Express

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

Proper citation: Onto-Express (RRID:SCR_005670) Copy   

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http://www.ebi.ac.uk/QuickGO/

A web-based browser for Gene Ontology terms and annotations, which is provided by the UniProtKB-GOA group at the EBI. It is able to offer a range of facilities including bulk downloads of GO annotation data which can be extensively filtered by a range of different parameters and GO slim set generation. The software for QuickGO is freely available under the Apache 2 license. QuickGO can supply GO term information and GO annotation data via REST web services.

Proper citation: QuickGO (RRID:SCR_004608) Copy   

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http://agbase.msstate.edu/cgi-bin/tools/goretriever_select.pl

GORetriever is used to find all of the GO annotations corresponding to a list of user-supplied protein identifiers. GORetriever produces a list of proteins and their annotations and a separate list of entries with no GO annotation. Platform: Online tool

Proper citation: GORetriever (RRID:SCR_005633) Copy   

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https://github.com/Sung-Huan/ANNOgesic

Software tool for bacterial/archaeal RNA-Seq based genome annotations. Used for integrating, detecting, predicting, and grouping RNA-Seq data.

Proper citation: ANNOgesic (RRID:SCR_016326) Copy   

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http://gostat.wehi.edu.au

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   

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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.cdtdb.brain.riken.jp/CDT/Top.jsp

Transcriptomic information (spatiotemporal gene expression profile data) on the postnatal cerebellar development of mice (C57B/6J & ICR). It is a tool for mining cerebellar genes and gene expression, and provides a portal to relevant bioinformatics links. The mouse cerebellar circuit develops through a series of cellular and morphological events, including neuronal proliferation and migration, axonogenesis, dendritogenesis, and synaptogenesis, all within three weeks after birth, and each event is controlled by a specific gene group whose expression profile must be encoded in the genome. To elucidate the genetic basis of cerebellar circuit development, CDT-DB analyzes spatiotemporal gene expression by using in situ hybridization (ISH) for cellular resolution and by using fluorescence differential display and microarrays (GeneChip) for developmental time series resolution. The CDT-DB not only provides a cross-search function for large amounts of experimental data (ISH brain images, GeneChip graph, RT-PCR gel images), but also includes a portal function by which all registered genes have been provided with hyperlinks to websites of many relevant bioinformatics regarding gene ontology, genome, proteins, pathways, cell functions, and publications. Thus, the CDT-DB is a useful tool for mining potentially important genes based on characteristic expression profiles in particular cell types or during a particular time window in developing mouse brains.

Proper citation: Cerebellar Development Transcriptome Database (RRID:SCR_013096) 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://www.t-profiler.org

One of the key challenges in the analysis of gene expression data is how to relate the expression level of individual genes to the underlying transcriptional programs and cellular state. The T-profiler tool hosted on this website uses the t-test to score changes in the average activity of pre-defined groups of genes. The gene groups are defined based on Gene Ontology categorization, ChIP-chip experiments, upstream matches to a consensus transcription factor binding motif, and location on the same chromosome, respectively. If desired, an iterative procedure can be used to select a single, optimal representative from sets of overlapping gene groups. A jack-knife procedure is used to make calculations more robust against outliers. T-profiler makes it possible to interpret microarray data in a way that is both intuitive and statistically rigorous, without the need to combine experiments or choose parameters. Currently, gene expression data from Saccharomyces cerevisiae and Candida albicans are supported. Users can submit their microarray data for analysis by clicking on one of the two organism-specific tabs above. Platform: Online tool

Proper citation: T-profiler (RRID:SCR_003452) Copy   

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http://eagl.unige.ch/EAGLi

A software tool, text mining based, for annotating text with gene ontology terms. The tool combines knowledge-driven methods on top of a standard vector- space retrieval approach to identify pointers to ontology terms. Passage selection methods were tested based on vocabulary density estimation using several terminologies of the domain. The tool improves on standard retrieval approaches based on vector-space similarities by using a Boolean completion principle.

Proper citation: Engine for question-Answering in Genomics Literature (RRID:SCR_008490) Copy   

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

Software tool for analysis of short time series gene expression data. Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments. Allows to identify significant temporal expression profiles and genes associated with these profiles and to compare behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology.

Proper citation: Stem (RRID:SCR_024499) Copy   

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http://www.arexdb.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. The Arabidopsis gene Expression Database collects Arabidopsis gene expression data from genome-wide and gene-specific sources and integrative search tools are provided. Currently the database contains only root gene expression data, but has the capability to contain data from any part of the plant. The aim of Arabidopsis gene Expression Database is to: (1) Integrate genome-wide and gene-specific ("traditional") types of expression pattern data, using ontologies to describe data whenever possible, in particular to describe expression patterns. (2) Provide user-friendly search tools, for example to search for genes expressed with a certain pattern, or to search for the expression pattern of specific genes (from gene-specific experiments and from microarray data). Expression pattern predicted from the microarray data is called digital in situ.

Proper citation: AREX (RRID:SCR_002170) Copy   

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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.candidagenome.org/

Database of genetic and molecular biological information about Candida albicans. Contains information about genes and proteins, descriptions and classifications of their biological roles, molecular functions, and subcellular localizations, gene, protein, and chromosome sequence information, tools for analysis and comparison of sequences and links to literature information. Each CGD gene or open reading frame has an individual Locus Page. Genetic loci that are not tied to DNA sequence also have Locus Pages. Provides Gene Ontology, GO, to all its users. Three ontologies that comprise GO (Molecular Function, Cellular Component, and Biological Process) are used by multiple databases to annotate gene products, so that this common vocabulary can be used to compare gene products across species. Development of ontologies is ongoing in order to incorporate new information. Data submissions are welcome.

Proper citation: Candida Genome Database (RRID:SCR_002036) Copy   

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https://bio.tools

Community registry of software tools and data resources for life sciences. Tools and data services registry as community effort to document bioinformatics resources. Registry of software and databases, facilitating researchers from across spectrum of biological and biomedical science. When adding tools to registry, information including URL, contact information, resource function, field its relevant in, and its primary publication are required. Development is supported by ELIXIR - the European Infrastructure for Biological Information.

Proper citation: bio.tools (RRID:SCR_014695) Copy   

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http://rgd.mcw.edu

Database for genetic, genomic, phenotype, and disease data generated from rat research. Centralized database that collects, manages, and distributes data generated from rat genetic and genomic research and makes these data available to scientific community. Curation of mapped positions for quantitative trait loci, known mutations and other phenotypic data is provided. Facilitates investigators research efforts by providing tools to search, mine, and analyze this data. Strain reports include description of strain origin, disease, phenotype, genetics, immunology, behavior with links to related genes, QTLs, sub-strains, and strain sources.

Proper citation: Rat Genome Database (RGD) (RRID:SCR_006444) Copy   

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http://www.bioinformatics.org/go2msig/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on April 24, 2020. Software tool as automated Gene Ontology based multi species gene set generator for gene set enrichment analysis. Used to generate gene sets required for Gene Set Enrichment Analysis for almost any organism for which GO term association data exists.
Gene set collections can be automatically created for wide variety of species.

Proper citation: GO2MSIG (RRID:SCR_018359) Copy   

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http://bioinformatics.ubc.ca/ermineJ/

Data analysis software for gene sets in expression microarray data or other genome-wide data that results in rankings of genes. A typical goal is to determine whether particular biological pathways are doing something interesting in the data. The software is designed to be used by biologists with little or no informatics background. A command-line interface is available for users who wish to script the use of ermineJ. Major features include: * Implementation of multiple methods for gene set analysis: ** Over-representation analysis ** A resampling-based method that uses gene scores ** A rank-based method that uses gene scores ** A resampling-based method that uses correlation between gene expression profiles (a type of cluster-enrichment analysis). * Gene sets receive statistical scores (p-values), and multiple test correction is supported. * Support of the Gene Ontology terminology; users can choose which aspects to analyze. * User files use simple text formats. * Users can modify gene sets or create new ones. * The results can be visualized within the software. * It is simple to compare multiple analyses of the same data set with different settings. * User-definable hyperlinks are provided to external sites to allow more efficient browsing of the results. * For programmers, there is a command line interface as well as a simple application programming interface that can be used to plug ermineJ functionality into your own code Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: ErmineJ (RRID:SCR_006450) Copy   

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http://phenomebrowser.net/

PhenomeNet is a cross-species phenotype similarity network. It contains the experimentally observed phenotypes of multiple species as well as the phenotypes of human diseases. PhenomeNet provides a measure of phenotypic similarity between the phenotypes it contains. The latest release (from 22 June 2012) contains 124,730 complex phenotype nodes taken from the yeast, fish, worm, fly, rat, slime mold and mouse model organism databases as well as human disease phenotypes from OMIM and OrphaNet. The network is a complete graph in which edge weights represent the degree of phenotypic similarity. Phenotypic similarity can be used to identify and prioritize candidate disease genes, find genes participating in the same pathway and orthologous genes between species. To compute phenotypic similarity between two sets of phenotypes, we use a weighted Jaccard index. First, phenotype ontologies are used to infer all the implications of a phenotype observation using several phenotype ontologies. As a second step, the information content of each phenotype is computed and used as a weight in the Jaccard index. Phenotypic similarity is useful in several ways. Phenotypic similarity between a phenotype resulting from a genetic mutation and a disease can be used to suggest candidate genes for a disease. Phenotypic similarity can also identify genes in a same pathway or orthologous genes. PhenomeNet uses the axioms in multiple species-dependent phenotype ontologies to infer equivalent and related phenotypes across species. For this purpose, phenotype ontologies and phenotype annotations are integrated in a single ontology, and automated reasoning is used to infer equivalences. Specifically, for every phenotype, PhenomeNet infers the related mammalian phenotype and uses the Mammalian Phenotype Ontology for computing phenotypic similarity. Tools: * PhenomeBLAST - A tool for cross-species alignments of phenotypes * PhenomeDrug - method for drug-repurposing

Proper citation: phenomeNET (RRID:SCR_006165) Copy   

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