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http://www.bioextract.org/GuestLogin

An open, web-based system designed to aid researchers in the analysis of genomic data by providing a platform for the creation of bioinformatic workflows. Scientific workflows are created within the system by recording tasks performed by the user. These tasks may include querying multiple, distributed data sources, saving query results as searchable data extracts, and executing local and web-accessible analytic tools. The series of recorded tasks can then be saved as a reproducible, sharable workflow available for subsequent execution with the original or modified inputs and parameter settings. Integrated data resources include interfaces to the National Center for Biotechnology Information (NCBI) nucleotide and protein databases, the European Molecular Biology Laboratory (EMBL-Bank) non-redundant nucleotide database, the Universal Protein Resource (UniProt), and the UniProt Reference Clusters (UniRef) database. The system offers access to numerous preinstalled, curated analytic tools and also provides researchers with the option of selecting computational tools from a large list of web services including the European Molecular Biology Open Software Suite (EMBOSS), BioMoby, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The system further allows users to integrate local command line tools residing on their own computers through a client-side Java applet.

Proper citation: BioExtract (RRID:SCR_005397) 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://metlin.scripps.edu/

A public repository of metabolite information as well as tandem mass spectrometry data is provided to facilitate metabolomics experiments. It contains structures and represents a data management system designed to assist in a broad array of metabolite research and metabolite identification. An annotated list of known metabolites and their mass, chemical formula, and structure are available. Each metabolite is linked to outside resources for further reference and inquiry. MS/MS data is also available on many of the metabolites.

Proper citation: METLIN (RRID:SCR_010500) Copy   

•   Source: SciCrunch Registry

http://agem.cnb.csic.es/VisualOmics/aGEM/

Database platform of an integrated view of eight databases (mouse gene expression resources: EMAGE, GXD, GENSAT, BioGPS, ABA, EUREXPRESS; human gene expression databases: HUDSEN, BioGPS and Human Protein Atlas) that allows the experimentalist to retrieve relevant statistical information relating gene expression, anatomical structure (space) and developmental stage (time). Moreover, general biological information from databases such as KEGG, OMIM and MTB is integrated too. It can be queried using gene and anatomical structure. Output information is presented in a friendly format, allowing the user to display expression maps and correlation matrices for a gene or structure during development. An in-depth study of a specific developmental stage is also possible using heatmaps that relate gene expression with anatomical components. This is a powerful tool in the gene expression field that makes easy the access to information related to the anatomical pattern of gene expression in human and mouse, so that it can complement many functional genomics studies. The platform allows the integration of gene expression data with spatial-temporal anatomic data by means of an intuitive and user friendly display., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: aGEM (RRID:SCR_013349) Copy   

•   Source: SciCrunch Registry

http://gtlinker.cnb.csic.es/

Web application that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. It can also be accessed through its web service.

Proper citation: GeneTerm Linker (RRID:SCR_006385) Copy   

•   Source: SciCrunch Registry

http://genetrail.bioinf.uni-sb.de/

A web-based application that analyzes gene sets for statistically significant accumulations of genes that belong to some functional category. Considered category types are: KEGG Pathways, TRANSPATH Pathways, TRANSFAC Transcription Factor, GeneOntology Categories, Genomic Localization, Protein-Protein Interactions, Coiled-coil domains, Granzyme-B clevage sites, and ELR/RGD motifs. The web server provides two statistical approaches, "Over-Representation Analysis" (ORA) comparing a reference set of genes to a test set, and "Gene Set Enrichment Analysis" (GSEA) scoring sorted lists of genes., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneTrail (RRID:SCR_006250) Copy   

•   Source: SciCrunch Registry

http://prodoric.tu-bs.de/

Database about gene regulation and gene expression in prokaryotes. It includes a manually curated and unique collection of transcription factor binding sites. A variety of bioinformatics tools for the prediction, analysis and visualization of regulons and gene reglulatory networks is included. The integrated approach provides information about molecular networks in prokaryotes with focus on pathogenic organisms. In detail this concerns: * transcriptional regulation (transcription factors and their DNA binding sites * signal transduction (two-component systems, phosphylation cascades) * protein interactions (complex formation, oligomerization) * biochemical pathways (chemical reactions) * other regulation events (e.g. codon usage, etc. ...) It aims to be a resource to model protein-host interactions and to be a suitable platform to analyze high-throughput data from proteomis and transcriptomics experiments (systems biology). Currently it mainly contains detailed information about operon and promoter structures including huge collections of transcription factor binding sites. If an appropriate number of regulatory binding sites is available, a position weight matrix (PWM) and a sequence logo is provided, which can be used to predict new binding sites. This data is collected manually by screening the original scientific literature. PRODORIC also handles protein-protein interactions and signal-transduction cascades that commonly occur in form of two-component systems in prokaryotes. Furthermore it contains metabolic network data imported from the KEGG database., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PRODORIC (RRID:SCR_007074) Copy   

•   Source: SciCrunch Registry

http://www.u-tokyo.ac.jp/index_e.html

Proper citation: University of Tokyo; Tokyo; Japan (RRID:SCR_011731) Copy   

•   Source: SciCrunch Registry

http://www.arabidopsisreactome.org

Curated database of core pathways and reactions in plant biology that covers biological pathways ranging from the basic processes of metabolism to high-level processes such as cell cycle regulation. While it is targeted at Arabidopsis pathways, it also includes many biological events from other plant species. This makes the database relevant to the large number of researchers who work on other plants. Arabidopsis Reactome currently contains both in-house curated pathways as well as imported pathways from AraCyc and KEGG databases. All the curated information is backed up by its provenance: either a literature citation or an electronic inference based on sequence similarity. Their ontology ensures that the various events are linked in an appropriate spatial and temporal context.

Proper citation: Arabidopsis Reactome (RRID:SCR_002063) Copy   

•   Source: SciCrunch Registry

http://cpdb.molgen.mpg.de

An integrative interaction database that integrates different types of functional interactions from heterogeneous interaction data resources. Physical protein interactions, metabolic and signaling reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With human, yeast and mouse complex functional interactions, it currently constitutes the most comprehensive publicly available interaction repository for these species. Different ways of utilizing these integrated interaction data, in particular with tools for visualization, analysis and interpretation of high-throughput expression data in the light of functional interactions and biological pathways is offered.

Proper citation: ConsensusPathDB (RRID:SCR_002231) Copy   

•   Source: SciCrunch Registry

http://kt.ijs.si/software/SEGS/

A web tool for descriptive analysis of microarray data. The analysis is performed by looking for descriptions of gene sets that are statistically significantly over- or under-expressed between different scenarios within the context of a genome-scale experiments (DNA microarray). Descriptions are defined by using the terms from the Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and gene-gene interactions found in the ENTREZ database. Gene annotations by GO and KEGG terms can also be found in the ENTREZ database. The tool provides three procedures for testing the enrichment of the gene sets (over- or under-expressed): Fisher's exact test, GSEA and PAGE, and option for combining the results of the tests. Because of the multiple-hypothesis testing nature of the problem, all the p-values are computed using the permutation testing method.

Proper citation: SEGS (RRID:SCR_003554) Copy   

•   Source: SciCrunch Registry

http://www.inmex.ca./INMEX/

A web-based tool to support meta-analysis of multiple gene-expression data sets, as well as to enable integration of data sets from gene expression and metabolomics experiments. INMEX contains three functional modules. The data preparation module supports flexible data processing, annotation and visualization of individual data sets. The statistical analysis module allows researchers to combine multiple data sets based on P-values, effect sizes, rank orders and other features. The significant genes can be examined in functional analysis module for enriched Gene Ontology terms or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, or expression profile visualization. INMEX has built-in support for common gene/metabolite identifiers (IDs), as well as 45 popular microarray platforms for human, mouse and rat. Complex operations are performed through a user-friendly web interface in a step-by-step manner.

Proper citation: INMEX (RRID:SCR_004173) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/FunTree/

FunTree provides a range of data resources to detect the evolution of enzyme function within distant structurally related clusters within domain super families as determined by CATH. To access the resource enter a specific CATH superfamily code or search for a structure / sequence / function (either via a EC code or KEGG ligand / reaction ID, PDB ID or UniProtKB ID). Or browse the resource via superfamily / function / structure / metabolites & reactions via the menu on the left panel. FunTree is a new resource that brings together sequence, structure, phylogenetic, chemical and mechanistic information for structurally defined enzyme superfamilies. Gathering together this range of data into a single resource allows the investigation of how novel enzyme functions have evolved within a structurally defined superfamily as well as providing a means to analyse trends across many superfamilies. This is done not only within the context of an enzyme''''s sequence and structure but also the relationships of their reactions. Developed in tandem with the CATH database, it currently comprises 276 superfamilies covering 1800 (70%) of sequence assigned enzyme reactions. Central to the resource are phylogenetic trees generated from structurally informed multiple sequence alignments using both domain structural alignments supplemented with domain sequences and whole sequence alignments based on commonality of multi-domain architectures. These trees are decorated with functional annotations such as metabolite similarity as well as annotations from manually curated resources such the catalytic site atlas and MACiE for enzyme mechanisms.

Proper citation: FunTree (RRID:SCR_006014) Copy   

•   Source: SciCrunch Registry

http://metabolomics.pharm.uconn.edu/iimdb/

Database of known biochemical compounds collected from existing biochemical databases, as well as computationally generated human phase I and phase II metabolites of known compounds.

Proper citation: In vivo - In silico Metabolite Database (RRID:SCR_015537) Copy   

•   Source: SciCrunch Registry

http://krebs.iab.keio.ac.jp/HERMIT1/app/NichoAnu/

Metabolic pathway map that collects metabolic data gathered from multiple public databases and organizes them in one central location.

Proper citation: Ultimate Rough Aggregation of Metabolic Map (RRID:SCR_014694) Copy   

•   Source: SciCrunch Registry

https://www.genome.jp/kegg/pathway.html

Reference database for pathway mapping in KEGG Mapper. Collection of manually drawn pathway maps representing knowledge on molecular interaction, reaction and relation networks for metabolism, genetic information processing, environmental information processing, cellular processes, organisms systems, human diseases, drug development.

Proper citation: KEGG PATHWAY Database (RRID:SCR_018145) Copy   

•   Source: SciCrunch Registry