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http://code.google.com/p/elk-reasoner/

ELK is an ontology reasoner with the goal of supporting the OWL 2 EL profile. ELK is a specialized reasoner for the lightweight ontology language OWL EL. The practical utility of ELK is in its combination of high performance and comprehensive support for language features. At its core, ELK employs a consequence-based reasoning engine that can take advantage of multi-core and multi-processor systems. A modular architecture allows ELK to be used as a stand-alone application, Protege plug-in, or programming library (either with or without the OWL API).

Proper citation: elk-reasoner (RRID:SCR_008913) Copy   

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http://www.butterflybase.org

An open-access Genomic Database for Lepidoptera. It includes all the Lepidoptera cDNA sequences available in NCBI's dbEST. Support for genomic-DNA sequences such as BACs and the Bombyx mori genome is being generated. Tools available on this website include BLAST search, Annotation search, Primer design, and Microsatellite repeat finders. Users can also download all data and sequences. Within the site, the Expressed Sequence Tag sequences are made with Trace2dbest from raw sequence data if available or downloaded from NCBI. We cluster them using PartiGene into gene-objects to reduce redundancy. Subsequently, we perform hierarchical BLAST searches to provide accurate similarity annotation and a robust protein translation protocol using prot4EST. Putative proteins objects have been further annotated with the Gene Ontology biological vocabulary (GO terms) and InterPro (EBI) domains to facilitate gene-hunters. The repository is open for all Lepidopteran researchers.

Proper citation: ButterflyBase (RRID:SCR_000727) 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://snf-515788.vm.okeanos.grnet.gr/

Web tool for integrating human and mouse microRNAs in pathways.Pathway analysis web-server, providing statistics, while being able to accommodate advanced pipelines. Web server for assessment of miRNA regulatory roles and identification of controlled pathways. Supports all analyses for KEGG molecular pathways and Gene Ontology (GO) in seven species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Gallus gallus and Danio rerio).DIANA miRPath v.2.0 includes investigating combinatorial effect of microRNAs in pathways.DIANA-miRPath v3.0 includes deciphering microRNA function with experimental support., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: DIANA-mirPath (RRID:SCR_017354) Copy   

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Ratings or validation data are available for this resource

http://www.avadis-ngs.com

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

Proper citation: Avadis (RRID:SCR_000644) Copy   

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http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

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http://www.funnet.info/

Functional Analysis of Transcriptional Networks (FunNet) is designed as an integrative tool for analyzing gene co-expression networks built from microarray expression data. The analytical model implemented in this tool involves two abstraction layers: transcriptional (i.e. gene expression profiles) and functional (i.e. biological themes indicating the roles of the analyzed transcripts). A functional analysis technique, which relies on Gene Ontology and KEGG annotations, is applied to extract a list of relevant biological themes from microarray gene expression data. Afterwards multiple-instance representations are built to relate relevant biological themes to their annotated transcripts. An original non-linear dynamical model is used to quantify the contextual proximity of relevant genomic themes based on their patterns of propagation in the gene co-expression network (i.e. capturing the similarity of the expression profiles of the transcriptional instances of annotating themes). In the end an unsupervised multiple-instance spectral clustering procedure is used to explore the modular architecture of the co-expression network by grouping together biological themes demonstrating a significant relationship in the co-expression network. Functional and transcriptional representations of the co-expression network are provided, together with detailed information on the contextual centrality of related transcripts and genomic themes. FunNet is provided both as a web-based tool and as a standalone R package. The standalone R implementation can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix) and can be downloaded from the FunNet website, or from the worldwide mirrors of CRAN. Both implementations of the FunNet tool are provided freely under the GNU General Public License 2.0. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FunNet - Transcriptional Networks Analysis (RRID:SCR_006968) Copy   

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http://www.genenetwork.org/

Web platform that provides access to data and tools to study complex networks of genes, molecules, and higher order gene function and phenotypes. Sequence data (SNPs) and transcriptome data sets (expression genetic or eQTL data sets). Quantitative trait locus (QTL) mapping module that is built into GN is optimized for fast on-line analysis of traits that are controlled by combinations of gene variants and environmental factors. Used to study humans, mice (BXD, AXB, LXS, etc.), rats (HXB), Drosophila, and plant species (barley and Arabidopsis). Users are welcome to enter their own private data.

Proper citation: GeneNetwork (RRID:SCR_002388) Copy   

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http://basalganglia.huji.ac.il/links.htm

GOdist is a Matlab program that analyzes Affymetrix microarray expression data implementing Kolmogorov-Smirnov (KS) continuous statistics approach. It also implements the discrete approach using Fisher exact test employing a two-tailed hypergeometric distribution. GOdist enables detection of both kinds of changes within specific GO terms represented on the array in relation to different populations: the global array population, the direct parents of the analyzed GO term and the global parent of it (e.g. biological process, molecular function or cellular component). Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GOdist (RRID:SCR_005770) Copy   

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http://www.geworkbench.org

geWorkbench (genomics Workbench) is a Java-based open-source platform for integrated genomics. Using a component architecture it allows individually developed plug-ins to be configured into complex bioinformatic applications. At present there are more than 70 available plug-ins supporting the visualization and analysis of gene expression and sequence data. Example use cases include: * loading data from local or remote data sources. * visualizing gene expression, molecular interaction networks, protein sequence and protein structure data in a variety of ways. * providing access to client- and server-side computational analysis tools such as t-test analysis, hierarchical clustering, self organizing maps, regulatory networks reconstruction, BLAST searches, pattern/motif discovery, etc. * validating computational hypothesis through the integration of gene and pathway annotation information from curated sources as well as through Gene Ontology enrichment analysis. geWorkbench is the Bioinformatics platform of MAGNet, the National Center for the Multi-scale Analysis of Genomic and Cellular Networks (one of the 7 National Centers for Biomedial Computing funded through the NIH Roadmap). Additionally, geWorkbench is supported by caBIG, NCI''s cancer Biomedical Informatics Grid initiative.

Proper citation: genomics Workbench (RRID:SCR_013599) Copy   

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http://owlapi.sourceforge.net/

The OWL API is a Java API and reference implementation for creating, manipulating and serializing OWL Ontologies. The latest version of the API is focused towards OWL 2. The OWLAPI underpins ontology browsing and editing tools and platforms such as SWOOP and Protege4. Note that this API, or any other OWL-based API, can be used without an integrated OWL parser if you download a pre-converted OWL file generated from OBO. See OBO Ontologies List for all OBO ontologies converted to OWL (we do not list the full complement of OWL-based APIs here, only those of direct relevance to GO). The OWL API includes the following components: * An API for OWL 2 and an efficient in-memory reference implementation * RDF/XML parser and writer * OWL/XML parser and writer * OWL Functional Syntax parser and writer * Turtle parser and writer * KRSS parser * OBO Flat file format parser * Reasoner interfaces for working with reasoners such as FaCT++, HermiT, Pellet and Racer Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: OWL API (RRID:SCR_005734) Copy   

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http://systers.molgen.mpg.de/

SYSTERS is a database of protein sequences grouped into homologous families and superfamilies. The SYSTERS project aims to provide a meaningful partitioning of the whole protein sequence space by a fully automatic procedure. A refined two-step algorithm assigns each protein to a family and a superfamily. The sequence data underlying SYSTERS release 4 now comprise several protein sequence databases derived from completely sequenced genomes (ENSEMBL, TAIR, SGD and GeneDB), in addition to the comprehensive Swiss-Prot/TrEMBL databases. To augment the automatically derived results, information from external databases like Pfam and Gene Ontology are added to the web server. Furthermore, users can retrieve pre-processed analyses of families like multiple alignments and phylogenetic trees. New query options comprise a batch retrieval tool for functional inference about families based on automatic keyword extraction from sequence annotations. A new access point, PhyloMatrix, allows the retrieval of phylogenetic profiles of SYSTERS families across organisms with completely sequenced genomes. Gene, Human, Vertebrate, Genome, Human ORFs

Proper citation: SYSTERS (RRID:SCR_007955) Copy   

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http://www.bioperl.org

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

Proper citation: BioPerl (RRID:SCR_002989) Copy   

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http://www.virtualflybrain.org

An interactive tool for neurobiologists to explore the detailed neuroanatomy, neuron connectivity and gene expression of the adult Drosophila melanogaster brain.

Proper citation: Virtual Fly Brain (RRID:SCR_004229) Copy   

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http://rice.plantbiology.msu.edu/

Database and resource that provides sequence and annotation data for the rice genome. This website provides genome sequence from the Nipponbare subspecies of rice and annotation of the 12 rice chromosomes. All structural and functional annotation is viewable through our Rice Genome Browser which currently supports 75 tracks of annotation. Enhanced data access is available through web interfaces, FTP downloads and a Data Extractor tool developed in order to support discrete dataset downloads. Rice is a model species for the monocotyledonous plants and the cereals which are the greatest source of food for the world''s population. While rice genome sequence is available through multiple sequencing projects, high quality, uniform annotation is required in order for genome sequence data to be fully utilized by researchers. The existence of a common gene set and uniform annotation allows researchers within the rice community to work from a common resource so that their results can be more easily interpreted by other scientists. The objective of this project has always been to provide high quality annotation for the rice genome. They generated, refined and updated gene models for the estimated 40,000-60,000 total rice genes, provided standardized annotation for each model, linked each model to functional annotation including expression data, gene ontologies, and tagged lines. They have provided a resource to extend the annotation of the rice genome to other plant species by providing comparative alignments to other plant species. Analysis/Tools are available including: BLAST, Locus Name Search, Functional Term Search, Protein Domain Search, Anatomy Expression Viewer, Highly Expressed Genes

Proper citation: Rice Genome Annotation (RRID:SCR_006663) Copy   

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http://www.aspgd.org/

Database of genetic and molecular biological information about the filamentous fungi of the genus Aspergillus including information about genes and proteins of Aspergillus nidulans and Aspergillus fumigatus; 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; as well as a multispecies comparative genomics browser tool (Sybil) for exploration of orthology and synteny across multiple sequenced Sgenus species. Also available are Gene Ontology (GO) and community resources. Based on the Candida Genome Database, the Aspergillus Genome Database is a resource for genomic sequence data and gene and protein information for Aspergilli. Among its many species, the genus contains an excellent model organism (A. nidulans, or its teleomorph Emericella nidulans), an important pathogen of the immunocompromised (A. fumigatus), an agriculturally important toxin producer (A. flavus), and two species used in industrial processes (A. niger and A. oryzae). Search options allow you to: *Search AspGD database using keywords. *Find chromosomal features that match specific properties or annotations. *Find AspGD web pages using keywords located on the page. *Find information on one gene from many databases. *Search for keywords related to a phenotype (e.g., conidiation), an allele (such as veA1), or an experimental condition (e.g., light). Analysis and Tools allow you to: *Find similarities between a sequence of interest and Aspergillus DNA or protein sequences. *Display and analyze an Aspergillus sequence (or other sequence) in many ways. *Navigate the chromosomes set. View nucleotide and protein sequence. *Find short DNA/protein sequence matches in Aspergillus. *Design sequencing and PCR primers for Aspergillus or other input sequences. *Display the restriction map for a Aspergillus or other input sequence. *Find similarities between a sequence of interest and fungal nucleotide or protein sequences. AspGD welcomes data submissions.

Proper citation: ASPGD (RRID:SCR_002047) Copy   

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http://corneliu.henegar.info/FunCluster.htm

FunCluster is a genomic data analysis algorithm which performs functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis aims to detect co-regulated biological processes through a specially designed clustering procedure involving biological annotations and gene expression data. FunCluster''''s functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo Sapiens, Mus Musculus and Saccharomyces Cerevisiae. FunCluster is provided as a standalone R package, which can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix). Download it from the FunCluster website, or from the worldwide mirrors of CRAN. FunCluster is provided freely under the GNU General Public License 2.0. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FunCluster (RRID:SCR_005774) Copy   

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http://www.textpresso.org/

An information extracting and processing package for biological literature that can be used online or installed locally via a downloadable software package, http://www.textpresso.org/downloads.html Textpresso's two major elements are (1) access to full text, so that entire articles can be searched, and (2) introduction of categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or describe one (e.g., methods, etc). A search engine enables the user to search for one or a combination of these categories and/or keywords within an entire literature. The Textpresso project serves the biological and biomedical research community by providing: * Full text literature searches of model organism research and subject-specific articles at individual sites. Major elements of these search engines are (1) access to full text, so that the entire content of articles can be searched, and (2) search capabilities using categories of biological concepts and classes that relate two objects (e.g., association, regulation, etc.) or identify one (e.g., cell, gene, allele, etc). The search engines are flexible, enabling users to query the entire literature using keywords, one or more categories or a combination of keywords and categories. * Text classification and mining of biomedical literature for database curation. They help database curators to identify and extract biological entities and facts from the full text of research articles. Examples of entity identification and extraction include new allele and gene names and human disease gene orthologs; examples of fact identification and extraction include sentence retrieval for curating gene-gene regulation, Gene Ontology (GO) cellular components and GO molecular function annotations. In addition they classify papers according to curation needs. They employ a variety of methods such as hidden Markov models, support vector machines, conditional random fields and pattern matches. Our collaborators include WormBase, FlyBase, SGD, TAIR, dictyBase and the Neuroscience Information Framework. They are looking forward to collaborating with more model organism databases and projects. * Linking biological entities in PDF and online journal articles to online databases. They have established a journal article mark-up pipeline that links select content of Genetics journal articles to model organism databases such as WormBase and SGD. The entity markup pipeline links over nine classes of objects including genes, proteins, alleles, phenotypes, and anatomical terms to the appropriate page at each database. The first article published with online and PDF-embedded hyperlinks to WormBase appeared in the September 2009 issue of Genetics. As of January 2011, we have processed around 70 articles, to be continued indefinitely. Extension of this pipeline to other journals and model organism databases is planned. Textpresso is useful as a search engine for researchers as well as a curation tool. It was developed as a part of WormBase and is used extensively by C. elegans curators. Textpresso has currently been implemented for 24 different literatures, among them Neuroscience, and can readily be extended to other corpora of text.

Proper citation: Textpresso (RRID:SCR_008737) Copy   

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http://www.bioinfo.tsinghua.edu.cn/dbsubloc.html

A database of protein subcellular localization containing proteins from primary protein database SWISS-PROT and PIR. By collecting the subcellular localization annotation, these information are classified and categorized by cross references to taxonomies and Gene Ontology database. Annotations were taken from primary protein databases, model organism genome projects and literature texts, and then were analyzed to dig out the subcellular localization features of the proteins. The proteins are also classified into different categories. Based on sequence alignment, nonredundant subsets of the database have been built, which may provide useful information for subcellular localization prediction. The database now contains >60 000 protein sequences including 30 000 protein sequences in the nonredundant data sets. Online download, SOAP server, Blast tools and prediction services are also available.

Proper citation: DBSubLoc - Database of protein Subcellular Localization (RRID:SCR_002339) Copy   

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http://services.nbic.nl/copub/portal/

Text mining tool that detects co-occuring biomedical concepts in abstracts from the MedLine literature database. It allows batch input of multiple human, mouse or rat genes and produces lists of keywords from several biomedical thesauri that are significantly correlated with the set of input genes. These lists link to Medline abstracts in which the co-occurring input genes and correlated keywords are highlighted. Furthermore, CoPub can graphically visualize differentially expressed genes and over-represented keywords in a network, providing detailed insight in the relationships between genes and keywords, and revealing the most influential genes as highly connected hubs.

Proper citation: CoPub (RRID:SCR_005327) Copy   

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