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http://gdm.fmrp.usp.br/tools_bit.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 29, 2012. Gene Class Expression allows functional annotation of SAGE data using the Gene Ontology database. This tool performs searches in the GO database for each SAGE tag, making associations in the selected GO category for a level selected in the hierarchy. This system provides user-friendly data navigation and visualization for mapping SAGE data onto the gene ontology structure. This tool also provides graphical visualization of the percentage of SAGE tags in each GO category, along with confidence intervals and hypothesis testing. Platform: Online tool

Proper citation: Gene Class Expression (RRID:SCR_005679) Copy   

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

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

Proper citation: Onto-Compare (RRID:SCR_005669) Copy   

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http://www.pathbase.net/

Database of histopathology photomicrographs and macroscopic images derived from mutant or genetically manipulated mice. The database currently holds more than 1000 images of lesions from mutant mice and their inbred backgrounds and further images are being added continuously. Images can be retrieved by searching for specific lesions or class of lesion, by genetic locus, or by a wide set of parameters shown on the Advanced Search Interface. Its two key aims are: * To provide a searchable database of histopathology images derived from experimental manipulation of the mouse genome or experiments conducted on genetically manipulated mice. * A reference / didactic resource covering all aspects of mouse pathology Lesions are described according to the Pathbase pathology ontology developed by the Pathbase European Consortium, and are available at the site or on the Gene Ontology Consortium site - OBO. As this is a community resource, they encourage everyone to upload their own images, contribute comments to images and send them their feedback. Please feel free to use any of the SOAP/WSDL web services. (under development)

Proper citation: Pathbase (RRID:SCR_006141) Copy   

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http://code.google.com/p/behavior-ontology

An ontology consisting of two main components, an ontology of behavioral processes and an ontology of behavioral phenotypes. The behavioral process branch of NBO contains a classification of behavior processes complementing and extending the GO process ontology. The behavior phenotype branch of NBO consists of a classification of both normal and abnormal behavioral characteristics of organisms. The prime application of NBO is to provide the vocabulary that is required to integrate behavior observations within and across species. It is currently being applied by several model organism communities as well as in the description of human behavior-related disease phenotypes. The main ontology is available in both the OBO Flatfile Format and the Web Ontology Language (OWL).

Proper citation: Neurobehavior Ontology (RRID:SCR_006201) Copy   

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http://uberon.org

An integrated cross-species anatomy ontology representing a variety of entities classified according to traditional anatomical criteria such as structure, function and developmental lineage. The ontology includes comprehensive relationships to taxon-specific anatomical ontologies, allowing integration of functional, phenotype and expression data. Uberon consists of over 10000 classes (March 2014) representing structures that are shared across a variety of metazoans. The majority of these classes are chordate specific, and there is large bias towards model organisms and human.

Proper citation: UBERON (RRID:SCR_010668) Copy   

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http://pathways.mcdb.ucla.edu/algal/

Tools to search gene lists for functional term enrichment as well as to dynamically visualize proteins onto pathway maps. Additionally, integrated expression data may be used to discover similarly expressed genes based on a starting gene of interest.

Proper citation: Algal Functional Annotation Tool (RRID:SCR_012034) Copy   

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http://www.evidenceontology.org

A controlled vocabulary that describes types of scientific evidence within the realm of biological research that can arise from laboratory experiments, computational methods, manual literature curation, and other means. Researchers can use these types of evidence to support assertions about research subjects that result from scientific research, such as scientific conclusions, gene annotations, or other statements of fact. ECO comprises two high-level classes, evidence and assertion method, where evidence is defined as a type of information that is used to support an assertion, and assertion method is defined as a means by which a statement is made about an entity. Together evidence and assertion method can be combined to describe both the support for an assertion and whether that assertion was made by a human being or a computer. However, ECO can not be used to make the assertion itself; for that, one would use another ontology, free text description, or other means. ECO was originally created around the year 2000 to support gene product annotation by the Gene Ontology. Today ECO is used by many groups concerned with provenance in scientific research. ECO is used in AmiGO 2

Proper citation: ECO (RRID:SCR_002477) Copy   

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https://omictools.com/l2l-tool

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 26, 2019.

Database of published microarray gene expression data, and a software tool for comparing that published data to a user''''s own microarray results. It is very simple to use - all you need is a web browser and a list of the probes that went up or down in your experiment. If you find L2L useful please consider contributing your published data to the L2L Microarray Database in the form of list files. L2L finds true biological patterns in gene expression data by systematically comparing your own list of genes to lists of genes that have been experimentally determined to be co-expressed in response to a particular stimulus - in other words, published lists of microarray results. The patterns it finds can point to the underlying disease process or affected molecular function that actually generated the observed changed in gene expression. Its insights are far more systematic than critical gene analyses, and more biologically relevant than pure Gene Ontology-based analyses. The publications included in the L2L MDB initially reflected topics thought to be related to Cockayne syndrome: aging, cancer, and DNA damage. Since then, the scope of the publications included has expanded considerably, to include chromatin structure, immune and inflammatory mediators, the hypoxic response, adipogenesis, growth factors, hormones, cell cycle regulators, and others. Despite the parochial origins of the database, the wide range of topics covered will make L2L of general interest to any investigator using microarrays to study human biology. In addition to the L2L Microarray Database, L2L contains three sets of lists derived from Gene Ontology categories: Biological Process, Cellular Component, and Molecular Function. As with the L2L MDB, each GO sub-category is represented by a text file that contains annotation information and a list of the HUGO symbols of the genes assigned to that sub-category or any of its descendants. You don''''t need to download L2L to use it to analyze your microarray data. There is an easy-to-use web-based analysis tool, and you have the option of downloading your results so you can view them at any time on your own computer, using any web browser. However, if you prefer, the entire L2L project, and all of its components, can be downloaded from the download page. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: L2L Microarray Analysis Tool (RRID:SCR_013440) Copy   

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http://www.nabc.go.kr/sgd/

Database for ESTs (Expressed Sequence Tags), consensus sequences, bacterial artificial chromosome (BAC) clones, BES (BAC End Sequences). They have generated 69,545 ESTs from 6 full-length cDNA libraries (Porcine Abdominal Fat, Porcine Fat Cell, Porcine Loin Muscle, Liver and Pituitary gland). They have also identified a total of 182 BAC contigs from chromosome 6. It is very valuable resources to study porcine quantitative trait loci (QTL) mapping and genome study. Users can explore genomic alignment of various data types, including expressed sequence tags (ESTs), consensus sequences, singletons, QTL, Marker, UniGene and BAC clones by several options. To estimate the genomic location of sequence dataset, their data aligned BES (BAC End Sequences) instead of genomic sequence because Pig Genome has low-coverage sequencing data. Sus scrofa Genome Database mainly provide comparative map of four species (pig, cattle, dog and mouse) in chromosome 6.

Proper citation: PiGenome (RRID:SCR_013394) Copy   

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http://bis.zju.edu.cn/pnatdb/

Natural Antisense Transcripts (NATs), a kind of regulatory RNAs, occur prevalently in plant genomes and play significant roles in physiological and/or pathological processes. PlantNATsDB (Plant Natural Antisense Transcripts DataBase) is a platform for annotating and discovering NATs by integrating various data sources involving approximately 2 million NAT pairs in 69 plant species. PlantNATsDB also provides an integrative, interactive and information-rich web graphical interface to display multidimensional data, and facilitate plant research community and the discovery of functional NATs. GO annotation and high-throughput small RNA sequencing data currently available were integrated to investigate the biological function of NATs. A ''''Gene Set Analysis'''' module based on GO annotation was designed to dig out the statistical significantly overrepresented GO categories from the specific NAT network. PlantNATsDB is currently the most comprehensive resource of NATs in the plant kingdom, which can serve as a reference database to investigate the regulatory function of NATs.

Proper citation: PlantNATsDB - Plant Natural Antisense Transcripts DataBase (RRID:SCR_013278) Copy   

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http://tools.dice-database.org/GOnet/)

Web tool for interactive Gene Ontology analysis of any biological data sources resulting in gene or protein lists.

Proper citation: GOnet (RRID:SCR_018977) Copy   

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https://syngoportal.org/

Evidence based, expert curated knowledge base for synapse. Universal reference for synapse research and online analysis platform for interpretation of omics data. Interactive knowledge base that accumulates available research about synapse biology using Gene Ontology annotations to novel ontology terms.

Proper citation: SynGO (RRID:SCR_017330) Copy   

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http://discover.nci.nih.gov/gominer/

GoMiner is a tool for biological interpretation of "omic" data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question, What does it all mean biologically? To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research. GoMiner displays the genes within the framework of the Gene Ontology hierarchy in two ways: * In the form of a tree, similar to that in AmiGO * In the form of a "Directed Acyclic Graph" (DAG) The program also provides: * Quantitative and statistical analysis * Seamless integration with important public databases GoMiner uses the databases provided by the GO Consortium. These databases combine information from a number of different consortium participants, include information from many different organisms and data sources, and are referenced using a variety of different gene product identification approaches.

Proper citation: GoMiner (RRID:SCR_002360) Copy   

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http://bioinformatics.biol.rug.nl/standalone/fiva/

Functional Information Viewer and Analyzer (FIVA) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. Currently, seven different modules containing functional information have been implemented: (i) gene regulatory interactions, (ii) cluster of orthologous groups (COG) of proteins, (iii) gene ontologies (GO), (iv) metabolic pathways (v) Swiss Prot keywords, (vi) InterPro domains - and (vii) generic functional categories. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FIVA - Functional Information Viewer and Analyzer (RRID:SCR_005776) Copy   

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http://ftp://ftp.geneontology.org/pub/go/www/GO.tools_by_type.term_enrichment.shtml#gobean

GoBean is a Java application for gene ontology enrichment analysis. It utilizes the NetBeans platform framework. Features * Graphical comparison of multiple enrichment analysis results * Versatile filter facility for focused analysis of enrichment results * Effective exploitation of the graphical/hierarchical structure of GO * Evidence code based association filtering * Supports local data files such as the ontology obo file and gene association files * Supports late enrichment methods and multiple testing corrections * Built-in ID conversion for common species using Ensembl biomart service Platform: Windows compatible, Mac OS X compatible, Linux compatible

Proper citation: GoBean - a Java application for Gene Ontology enrichment analysis (RRID:SCR_005808) Copy   

•   Source: SciCrunch Registry

https://neuinfo.org/mynif/search.php?list=cover&q=*

Service that partners with the community to expose and simultaneously drill down into individual databases and data sets and return relevant content. This type of content, part of the so called hidden Web, is typically not indexed by existing web search engines. Every record links back to the originating site. In order for NIF to directly query these independently maintained databases and datasets, database providers must register their database or dataset with the NIF Data Federation and specify permissions. Databases are concept mapped for ease of sharing and to allow better understanding of the results. Learn more about registering your resource, http://neuinfo.org/nif_components/disco/interoperation.shtm Search results are displayed under the Data Federation tab and are categorized by data type and nervous system level. In this way, users can easily step through the content of multiple resources, all from the same interface. Each federated resource individually displays their query results with links back to the relevant datasets within the host resource. This allows users to take advantage of additional views on the data and tools that are available through the host database. The NIF site provides tutorials for each resource, indicated by the Professor Icon professor icon showing users how to navigate the results page once directed there through the NIF. Additionally, query results may be exported as an Excel document. Note: NIF is not responsible for the availability or content of these external sites, nor does NIF endorse, warrant or guarantee the products, services or information described or offered at these external sites. Integrated Databases: Theses virtual databases created by NIF and other partners combine related data indexed from multiple databases and combine them into one view for easier browsing. * Integrated Animal View * Integrated Brain Gene Expression View * Integrated Disease View * Integrated Nervous System Connectivity View * Integrated Podcasts View * Integrated Software View * Integrated Video View * Integrated Jobs * Integrated Blogs For a listing of the Federated Databases see, http://neuinfo.org/mynif/databaseList.php or refer to the Resources Listed by NIF Data Federation table below.

Proper citation: NIF Data Federation (RRID:SCR_004834) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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://titan.biotec.uiuc.edu/bee/honeybee_project.htm

A database integrating data from the bee brain EST sequencing project with data from sequencing and gene research projects from other organisms, primarily the fruit fly Drosophila melanogaster. The goal of Bee-ESTdb is to provide updated information on the genes of the honey bee, currently using annotation primarily from flies to suggest cellular roles, biological functions, and evolutionary relationships. The site allows searches by sequence ID, EST annotations, Gene Ontology terms, Contig ID and using BLAST. Very nice resource for those interested in comparative genomics of brain. A normalized unidirectional cDNA library was made in the laboratory of Prof. Bento Soares, University of Iowa. The library was subsequently subtracted. Over 20,000 cDNA clones were partially sequenced from the normalized and subtracted libraries at the Keck Center, resulting in 15,311 vector-trimmed, high-quality, sequences with an average read length of 494 bp. and average base-quality of 41. These sequences were assembled into 8966 putatively unique sequences, which were tested for similarity to sequences in the public databases with a variety of BLAST searches. The Clemson University Genomics Institute is the distributor of these public domain cDNA clones. For information on how to purchase an individual clone or the entire collection, please contact www.genome.clemson.edu/orders/ or generobi (at) life.uiuc.edu.

Proper citation: Honey Bee Brain EST Project (RRID:SCR_002389) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/

Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.

Proper citation: Bioconductor (RRID:SCR_006442) Copy   

•   Source: SciCrunch Registry