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on page 1 showing 20 out of 221 results from 1 sources

Cite this (3D-Interologs, RRID:SCR_003101)

URL: http://gemdock.life.nctu.edu.tw/3D-Interologs

Resource Type: Resource, data analysis service, production service resource, analysis service resource, database, service resource, data or information resource

Database of physical protein-protein interactions across multiple genomes. Based on 3D-domain interolog mapping and a scoring function, protein-protein interactions are inferred by using three-dimensional (3D) structure heterodimers to search the UniProt database. For a query protein, the database utilizes BLAST to identify homologous proteins and the interacting partners from multiple species. Based on the scoring function and structure complexes, it provides the statistic significances, the interacting models (e.g. hydrogen bonds and conserved amino acids), and functional annotations of interacting partners of a query protein. The identification of orthologous proteins of multiple species allows the study of protein-protein evolution, protein functions, and cross-referencing of proteins.

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    AgingDB

Cite this (AgingDB, RRID:SCR_010226)

URL: http://link.springer.com/article/10.1007%2Fs11357-003-0002-y

Resource Type: Resource, service resource, data or information resource, data repository, storage service resource, database

A database that stores information on the biomolecules which are modulated during aging and by caloric restriction (CR). To enhance its usefulness, data collected from studies of CR''''s anti-oxidative action on gene expression, oxidative stress, and many chronic age-related diseases are included. AgingDB is organized into two sections A) apoptosis and the various mitochondrial biomolecules that play a role in aging; B) nuclear transcription factors known to be_sensitive to oxidative environment. AgingDB features an imagemap of biomolecular signal pathways and visualized information that includes protein-protein interactions of biomolecules. Authorized users can submit a new biomolecule or edit an existing biomolecule to reflect latest developments.

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    agriGO

Cite this (agriGO, RRID:SCR_006989)

URL: http://bioinfo.cau.edu.cn/agriGO/

Resource Type: Resource, data analysis service, database, analysis service resource, production service resource, service resource, data or information resource

A web-based tool and database for the gene ontology analysis. Its focus is on agricultural species and is user-friendly. The agriGO is designed to provide deep support to agricultural community in the realm of ontology analysis. Compared to other available GO analysis tools, unique advantages and features of agriGO are: # The agriGO especially focuses on agricultural species. It supports 45 species and 292 datatypes currently. And agriGO is designed as an user-friendly web server. # New tools including PAGE (Parametric Analysis of Gene set Enrichment), BLAST4ID (Transfer IDs by BLAST) and SEACOMPARE (Cross comparison of SEA) were developed. The arrival of these tools provides users with possibilities for data mining and systematic result exploration and will allow better data analysis and interpretation. # The exploratory capability and result visualization are enhanced. Results are provided in different formats: HTML tables, tabulated text files, hierarchical tree graphs, and flash bar graphs. # In agriGO, PAGE and SEACOMPARE can be used to carry out cross-comparisons of results derived from different data sets, which is very important when studying multiple groups of experiments, such as in time-course research. Platform: Online tool

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Cite this (Algal Functional Annotation Tool, RRID:SCR_012034)

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

Resource Type: Resource, data analysis service, database, analysis service resource, production service resource, service resource, data or information resource

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.

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Cite this (AnimalTFDB, RRID:SCR_001624)

URL: http://www.bioguo.org/AnimalTFDB/

Resource Type: Resource, data or information resource, database

A comprehensive transcription factor (TF) database in which they identified and classified all the genome-wide TFs in 50 sequenced animal genomes (Ensembl release version 60). In addition to TFs, it also collects transcription co-factors and chromatin remodeling factors of those genomes, which play regulatory roles in transcription. Here they defined the TFs as proteins containing a sequence-specific DNA-binding domain (DBD) and regulating target gene expression. Currently, the AnimalTFDB classifies all the animal TFs into 72 families according to their conserved DBDs. Gene lists of transcription factors, transcription co-factors and chromatin remodeling factors of each species are available for downloading.

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Cite this (APID: Agile Protein Interaction DataAnalyzer, RRID:SCR_008871)

URL: http://bioinfow.dep.usal.es/apid

Resource Type: Resource, data analysis service, database, analysis service resource, production service resource, service resource, data or information resource

An interactive bioinformatics web tool developed to integrate and analyze in a unified and comparative platform known information about protein-protein interactions demonstrated by specific small-scale or large-scale experimental methods. At present, the application includes information coming from five main source databases enclosing an unified sever to explore more than 40000 different proteins and close to 150000 different proven interactions. The website includes search tools to query and browse upon the data, allowing selection of the interaction pairs based in calculated parameters that weight and qualify the reliability of each given protein interaction. Parameters for the proteins are connectivity, cluster coefficient, Gene Ontology (GO) functional environment, GO environment enrichment; for the interactions: number of methods, GO overlapping, Pfam domain-domain interaction. APID also includes a graphic interactive tool to visualize selected sub-networks and to navigate on them or along the whole interaction network. Platform: Online tool

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Cite this (Arabidopsis Hormone Database, RRID:SCR_001792)

URL: http://ahd.cbi.pku.edu.cn

Resource Type: Resource, data repository, ontology, database, service resource, storage service resource, controlled vocabulary, data or information resource

Database providing a systematic and comprehensive view of morphological phenotypes regulated by plant hormones, as well as regulatory genes participating in numerous plant hormone responses. By integrating the data from mutant studies, transgenic analysis and gene ontology annotation, genes related to the stimulus of eight plant hormones were identified, including abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid and salicylic acid. Another pronounced characteristics of this database is that a phenotype ontology was developed to precisely describe all kinds of morphological processes regulated by plant hormones with standardized vocabularies. To increase the coverage of phytohormone related genes, the database has been updated from AHD to AHD2.0 adding and integrating several pronounced features: (1) added 291 newly published Arabidopsis hormone related genes as well as corrected information (e.g. the arguable ABA receptors) based on the recent 2-year literature; (2) integrated orthologues of sequenced plants in OrthoMCLDB into each gene in the database; (3) integrated predicted miRNA splicing site in each gene in the database; (4) provided genetic relationship of these phytohormone related genes mining from literature, which represents the first effort to construct a relatively comprehensive and complex network of hormone related genes as shown in the home page of our database; (5) In convenience to in-time bioinformatics analysis, they also provided links to a powerful online analysis platform Weblab that they have recently developed, which will allow users to readily perform various sequence analysis with these phytohormone related genes retrieved from AHD2.0; (6) provided links to other protein databases as well as more expression profiling information that would facilitate users for a more systematic analysis related to phytohormone research. Please help to improve the database with your contributions.

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Cite this (Arabidopsis thaliana Protein Interactome Database, RRID:SCR_001896)

URL: http://www.megabionet.org/atpid/webfile/

Resource Type: Resource, service resource, data or information resource, data repository, storage service resource, database

Centralized platform to depict and integrate the information pertaining to protein-protein interaction networks, domain architecture, ortholog information and GO annotation in the Arabidopsis thaliana proteome. The Protein-protein interaction pairs are predicted by integrating several methods with the Naive Baysian Classifier. All other related information curated is manually extracted from published literature and other resources from some expert biologists. You are welcomed to upload your PPI or subcellular localization information or report data errors. Arabidopsis proteins is annotated with information (e.g. functional annotation, subcellular localization, tissue-specific expression, phosphorylation information, SNP phenotype and mutant phenotype, etc.) and interaction qualifications (e.g. transcriptional regulation, complex assembly, functional collaboration, etc.) via further literature text mining and integration of other resources. Meanwhile, the related information is vividly displayed to users through a comprehensive and newly developed display and analytical tools. The system allows the construction of tissue-specific interaction networks with display of canonical pathways.

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    AutismKB

Cite this (AutismKB, RRID:SCR_006937)

URL: http://autismkb.cbi.pku.edu.cn/

Resource Type: Resource, data analysis service, production service resource, analysis service resource, database, service resource, data or information resource

Genetic factors contribute significantly to ASD. AutismKB is an evidence-based knowledgebase of Autism spectrum disorder (ASD) genetics. The current version contains 2193 genes (99 syndromic autism related genes and 2135 non-syndromic autism related genes), 4617 Copy Number Variations (CNVs) and 158 linkage regions associated with ASD by one or more of the following six experimental methods: # Genome-Wide Association Studies (GWAS); # Genome-wide CNV studies; # Linkage analysis; # Low-scale genetic association studies; # Expression profiling; # Other low-scale gene studies. Based on a scoring and ranking system, 99 syndromic autism related genes and 383 non-syndromic autism related genes (434 genes in total) were designated as having high confidence. Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with a prevalence of 1.0-2.6%. The three core symptoms of ASD are: # impairments in reciprocal social interaction; # communication impairments; # presence of restricted, repetitive and stereotyped patterns of behavior, interests and activities.

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Cite this (Automated Microarray Pipeline, RRID:SCR_001219)

URL: http://compbio.dfci.harvard.edu/amp/

Resource Type: Resource, analysis service resource, data analysis service, service resource, production service resource

THIS RESOURCE IS NO LONGER IN SERVICE, documented November 4, 2015. Web application based on the TM4 Microarray Software Suite to provide a means of normalization and analysis of microarray data. Users can upload data in the form of Affymetrix CEL files, and define an analysis pipeline by selecting several intuitive options. It performs data normalization (eg RMA), basic statistical analysis (eg t-test, ANOVA), and analysis of annotation using gene classification (eg Gene Ontology term assignment). The analysis are performed without user intervention and the results are presented in a web-based summary that allows data to be downloaded in a variety of formats compatible with further directed analysis.

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    Avadis

Cite this (Avadis, RRID:SCR_000644)

URL: http://www.avadis-ngs.com

Resource Type: Resource, data analysis software, data processing software, software library, software application, data management software, data visualization software, software resource, software toolkit, commercial organization

An 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. Genes can be clustered based on ontology terms to identify functional signatures in gene expression clusters. AVADIS platform has a rich 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,

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Cite this (Babelomics, RRID:SCR_002969)

URL: http://babelomics.bioinfo.cipf.es

Resource Type: Resource, analysis service resource, data analysis service, service resource, production service resource

An integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Version 4 of Babelomics integrates primary (normalization, calls, etc.) and secondary (signatures, predictors, associations, TDTs, clustering, etc.) analysis tools within an environment that allows relating genomic data and/or interpreting them by means of different functional enrichment or gene set methods. Such interpretation is made not only using functional definitions (GO, KEGG, Biocarta, etc.) but also regulatory information (from Transfac, Jaspar, etc.) and other levels of regulation such as miRNA-mediated interference, protein-protein interactions, text-mining module definitions and the possibility of producing de novo annotations through the Blast2GO system . Babelomics has been extensively re-engineered and now it includes the use of web services and Web 2.0 technology features, a new user interface with persistent sessions and a new extended database of gene identifiers. In this release GEPAS and Babelomics have integrated into a unique web application with many new features and improvements: * Data input: import and quality control for the most common microarray formats * Normalization and base calling: for the most common expression, tiling and SNP microarrays (Affymetrix and Agilent). * Transcriptomics: diverse analysis options that include well established as well as novel algorithms for normalization, gene selection, class prediction, clustering and time-series analysis. * Genotyping: stratification analysis, association, TDT. * Functional profiling: functional enrichment and gene set enrichment analysis with functional terms (GO, KEGG, Biocarta, etc.), regulatory (Transfac, Jaspar, miRNAs, etc.), text-mining, derived bioentities, protein-protein interaction analysis. * Integrative analysis: Different variables can be related to each other (e.g. gene expression to gnomic copy number) and the results subjected to functional analysis. Platform: Online tool

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Cite this (BiNGO: A Biological Networks Gene Ontology tool, RRID:SCR_005736)

URL: http://www.psb.ugent.be/cbd/papers/BiNGO/Home.html

Resource Type: Resource, software resource

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape''''s versatile visualization environment to produce an intuitive and customizable visual representation of the results. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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Cite this (Bioconductor, RRID:SCR_006442)

URL: http://www.bioconductor.org/

Resource Type: Resource, catalog, data or information resource, database

A catalog of tools and software packages for the analysis and comprehension of high-throughput genomic data that uses the R statistical programming language. Bioconductor has a development version to which new features and packages are added prior to incorporation in the release. A large number of meta-data packages provide pathway, organism, microarray and other annotations. The broad goals of the Bioconductor project are: to provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data; to facilitate the inclusion of biological metadata in the analysis of genomic data; to provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software; and to train researchers on computational and statistical methods for the analysis of genomic data.

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    Biomine

Cite this (Biomine, RRID:SCR_003552)

URL: http://biomine.cs.helsinki.fi/

Resource Type: Resource, service resource, data or information resource, database

Service that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. In particular, it formulates protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph.

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    BioPerl

Cite this (BioPerl, RRID:SCR_002989)

URL: http://www.bioperl.org

Resource Type: Resource, wiki, source code, narrative resource, software repository, software resource, software toolkit, data or information resource

BioPerl is a community effort to produce Perl code which is useful in biology. This toolkit of perl modules is useful in building bioinformatics solutions in Perl. It is built in an object-oriented manner so that many modules depend on each other to achieve a task. The collection of modules in the bioperl-live repository consist of the core of the functionality of bioperl. Additionally auxiliary modules for creating graphical interfaces (bioperl-gui), persistent storage in RDMBS (bioperl-db), running and parsing the results from hundreds of bioinformatics applications (Run package), software to automate bioinformatic analyses (bioperl-pipeline) are all available as Git modules in our repository. The BioPerl toolkit provides a library of hundreds of routines for processing sequence, annotation, alignment, and sequence analysis reports. It often serves as a bridge between different computational biology applications assisting the user to construct analysis pipelines. This chapter illustrates how BioPerl facilitates tasks such as writing scripts summarizing information from BLAST reports or extracting key annotation details from a GenBank sequence record. BioPerl includes modules written by Sohel Merchant of the GO Consortium for parsing and manipulating OBO ontologies. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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    Blast2GO

Cite this (Blast2GO, RRID:SCR_005828)

URL: http://www.blast2go.com/b2ghome

Resource Type: Resource, software resource, software application

An ALL in ONE tool for functional annotation of (novel) sequences and the analysis of annotation data. Blast2GO (B2G) joins in one universal application similarity search based GO annotation and functional analysis. B2G offers the possibility of direct statistical analysis on gene function information and visualization of relevant functional features on a highlighted GO direct acyclic graph (DAG). Furthermore B2G includes various statistics charts summarizing the results obtained at BLASTing, GO-mapping, annotation and enrichment analysis (Fisher''''s Exact Test). All analysis process steps are configurable and data import and export are supported at any stage. The application also accepts pre-existing BLAST or annotation files and takes them to subsequent steps. The tool offers a very suitable platform for high throughput functional genomics research in non-model species. B2G is a species-independent, intuitive and interactive desktop application which allows monitoring and comprehending the whole annotation and analysis process supported by additional features like GO Slim integration, evidence code (EC) consideration, a Batch-Mode or GO-Multilevel-Pies. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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Cite this (Blip: Biomedical Logic Programming, RRID:SCR_005733)

URL: http://www.blipkit.org/

Resource Type: Resource, software resource

Biomedical Logical Programming (Blip) is a research-oriented deductive database and prolog application library for handling biological and biomedical data. It includes packages for advanced querying of ontologies and annotations. Blip underpins the Obol tool. Here are some distinguishing characteristics of Blip * Lightweight. Bloat-free: Blip only has as many modules as it needs to do its job. * Fast. * Declarative. Say what you want to do, not how you want to do it * Blip can be Query-oriented: specify your data sources and ask your query * Blip can be Application-oriented: it is designed to be used as an application library used by other bioinformatics tools * Mature and fully functional ontology module for handling both OBO-style ontologies and OWL ontologies. * Modules for handling biological sequences and sequence features. (currently limited functionality, added as needed) * A systems biology module for querying pathway and interaction data. (currently limited functionality, added as needed) * Relational database integration. SQL can be viewed as a highly restricted dialect of Prolog. Although the SWI-Prolog in-memory database is fast and scalable, sometimes it is nice to be able to fetch data from an external database. Blip contains a generic SQL utility module and predicate mappings for the GO database, Ensembl and Chado * Integration with a variety of bioinformatics file formats. SWI-Prolog has a variety of fast libraries for dealing with XML, RDF and tabular data files. Blip provides bridges from bio file formats encoded using these syntaxes into its native models. For other syntaxes, Blip seamlessly integrates other packages such as BioPerl and go-perl. Although these dependencies require extra installation, there is no point reinventing the wheel * Rapid development of web applications. Blip extends SWI-Prolog''''s excellent http support with a simple and powerful logical-functional-programming style application server, serval. This has been used to prototype a fully-featured next-generation replacement for the GO project amigo browser. * Scalable. Blip is not intended to be a toy system on toy data (although it is happy to be used as a toy if you like!). It is intended to be used as an application component and a tool operating on real-world biological and biomedical data Blip is written in SWI-Prolog, a fast, robust and scalable implementation of ISO Prolog. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

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Cite this (Candida Genome Database, RRID:SCR_002036)

URL: http://www.candidagenome.org/

Resource Type: Resource, service resource, data or information resource, data repository, storage service resource, database

Database of genetic and molecular biological information about Candida albicans, a yeast that is an opportunistic pathogen of humans, and about other Candida-related species, such as Candida glabrata. It 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 a DNA sequence also have Locus Pages. The Locus Page is the central clearinghouse for all information specific to that gene and tools for its analysis, including: * gene name, synonyms, and systematic name * Gene Ontology (GO) annotations * descriptions of the gene and gene product * phenotype of mutations in the gene * chromosomal and contig coordinates * interactive graphical chromosome map and browsing tool * tools for retrieval and analysis of the gene and protein sequences * a curated collection of literature CGD also provides a Gene Ontology, GO, to all its users. GO is a collaborative project involving CGD and other model organism databases to provide controlled vocabularies that are used to describe the molecular function and cellular location of gene products and the biological process in which they are involved. The 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. The development of the ontologies is ongoing in order to incorporate new information. Data submissions are welcome.

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Cite this (Candidate Genes to Inherited Diseases, RRID:SCR_008190)

URL: http://coot.embl.de/g2d/

Resource Type: Resource, data analysis service, production service resource, analysis service resource, database, service resource, data or information resource

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.

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