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http://causal.uma.es

At the Website of the Causal Cognition Group (CCG) of the University of M��laga, you may read information about our group, its members, our research, main activities, and more. Our main interests are cognitive psychology and learning, and more recently cognitive neuroscience, physiological correlates of learning and cognitive control. This site is in constant evolution... though there are things that hardly change: Cognitio rei per causas.

Proper citation: Causal Cognition Group (RRID:SCR_004780) Copy   

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http://prodom.prabi.fr/

Comprehensive set of protein domain families automatically generated from UniProt Knowledge Database. Automated clustering of homologous domains generated from global comparison of all available protein sequences., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ProDom (RRID:SCR_006969) Copy   

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http://www.dkfz.de/en/mga/Groups/LIFEdb-Database.html

Database that integrates large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis. LifeDB integrates data regarding full length cDNA clones and data on expression of encoded protein and their subcellular localization on mammalian cell line. LifeDB enables the scientific community to systematically search and select genes, proteins as well as cDNA of interest by specific database identifiers as well as gene name. It enables to visualize cDNA clone and subcellular location of proteins. It also links the results to external biological databases in order to provide a broader functional information. LifeDB also provides an annotation pipeline which facilitates an improved mapping of clones to known human reference transcripts from the RefSeq database and the Ensembl database. An advanced web interface enables the researchers to view the data in a more user friendly manner. Users can search using any one of the following search options available both in Search gene and cDNA clones and Search Sub-cellular locations of human proteins: By Keyword, By gene/transcript identifier, By plate name, By clone name, By cellular location. * The Search genes and cDNA clones results include: Gene Name, Ensemble ID, Genomic Region, Clone name, Plate name, Plate position, Classification class, Synonymous SNP''s, Non- synonymous SNP''s, Number of ambiguous positions, and Alignment with reference genes. * The Search sub-cellular locations of human proteins results include: Subcellular location, Gene Name, Ensemble ID, Clone name, True localization, Images, Start tag and End tag. Every result page has an option to download result data (excluding the microscopy images). On click of ''Download results as CSV-file'' link in the result page the user will be given a choice to open or save result data in form of a CSV (Comma Separated Values) file. Later the CSV file can be easily opened using Excel or OpenOffice.

Proper citation: LifeDB (RRID:SCR_006899) Copy   

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http://hendrix.imm.dtu.dk/software/lyngby/

Matlab toolbox for the analysis of functional neuroimages (PET, fMRI). The toolbox contains a number of models: FIR-filter, Lange-Zeger, K-means clustering among others, visualizations and reading of neuroimaging files.

Proper citation: Lyngby (RRID:SCR_007143) Copy   

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http://www.aneurist.org/

Project focused on cerebral aneurysms and provides integrated decision support system to assess risk of aneurysm rupture in patients and to optimize their treatments. IT infrastructure has been developeded for management and processing of vast amount of heterogeneous data acquired during diagnosis.

Proper citation: aneurIST (RRID:SCR_007427) Copy   

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http://www.interaction-proteome.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 28, 2013. (URL is no longer valid) A platform for high-throughput proteomic analysis. Major objectives of IPP include the establishment of a broadly applicable platform of routine methods for the analysis of protein interaction networks in bio-medical research. A multidisciplinary approach will address; * their validation by cell biological, biochemical and biophysical methods. * their collection in a new type of public database. * their exploitation and use for in silico simulations of protein-interaction networks. The innovations generated in IPP will provide the basis for an efficient analysis and systems modeling of fundamental biological processes in health and disease. It will develop novel technology, including a high-end mass spectrometer with extremely large dynamic range, high-density peptide arrays, and improved visualization technology for light and electron microscopy. Additionally, the novel technologies will be validated with selected model systems of high relevance to medicine and biotechnology. Extensive bioinformatics support is a key element in the project to cope with the massive increase in experimental data on protein interactions obtained using the novel technologies. In particular, the efficient integration of disparate data sets represents a key challenge in proteomics and functional genomics. Therefore, the consortium includes the creator of the only European protein-interactions database, MINT. The multi-disciplinary efforts required in the scientific program of IPP are organized into four sub-projects (SP): * SP1: Tools for interaction analysis - SP1 is dedicated to the development of innovative proteomics technology to map protein-interaction networks and their cellular topology for the interaction analyses in SP2 and SP3. * SP2: Identification of interaction partners for protein domains - SP2 will generate (high throughput) data for important protein-protein interactions defined by bioinformatics and biomedical interest and by SP3, utilizing technology developed in SP1. * SP3: Functional analysis of interactions - SP3 focuses on the validation of technologies and tools developed in SP1. It will perform functional analyses of protein-interactions in medically and biochemically relevant prokaryotic and eukaryotic (mammalian) model systems. * SP4: Interactome database and modelling - SP4 provides the required bioinformatics infrastructure for the project, comprising the improvement of the public MINT database for the collection and dissemination of the interactome data; modelling and simulation of protein-interaction networks characterised in SP2 and SP3; and the dissemination of the technology developments to the scientific community.

Proper citation: Interaction Proteome Project (RRID:SCR_008043) Copy   

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http://fmf.igh.cnrs.fr/ISSAID/infevers

Registry for Familial Mediterranean Fever (FMF) and hereditary inflammatory disorders mutations. As of 2014, it includes twenty genes including: MEFV, MVK, TNFRSF1A, NLRP3, NOD2, PSTPIP1, LPIN2 and NLRP7, and contains over 1338 sequence variants. Confidential data, simple and complex alleles are accepted. For each gene, a menu offers: 1) a tabular list of the variants that can be sorted by several parameters; 2) a gene graph providing a schematic representation of the variants along the gene; 3) statistical analysis of the data according to the phenotype, alteration type, and location of the mutation in the gene; 4) the cDNA and gDNA sequences of each gene, showing the nucleotide changes along the sequence, with a color-based code highlighting the gene domains, the first ATG, and the termination codon; and 5) a download menu making all tables and figures available for the users, which, except for the gene graphs, are all automatically generated and updated upon submission of the variants. The entire database was curated to comply with the HUGO Gene Nomenclature Committee (HGNC) and HGVS nomenclature guidelines, and wherever necessary, an informative note was provided.

Proper citation: INFEVERS (RRID:SCR_007738) Copy   

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http://www.gudmap.org/Resources/Ontologies.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. A high-resolution ontology has been developed by members of the GUDMAP consortium to describe the subcompartments of the developing murine genitourinary tract. This ontology incorporates what can be defined histologically and begins to encompass other structures and cell types already identified at the molecular level. The GUDMAP ontology encompasses Theiler stage (TS) 17-27 of development as well as the sexually mature adult. It has been written as a partonomic, text-based, hierarchical ontology that, for the embryological stages, has been developed as a high-resolution expansion of the existing Edinburgh Mouse Atlas Project (EMAP) ontology. It also includes group terms for well-characterized structural and/or functional units comprising several sub-structures, such as the nephron and juxtaglomerular complex. Each term has been assigned a unique identification number. Synonyms have been used to improve the success of query searching and maintain wherever possible existing EMAP terms relating to this organ system.

Proper citation: GUDMAP Ontology (RRID:SCR_002637) Copy   

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http://www.geneontology.org/

Computable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.

Proper citation: Gene Ontology (RRID:SCR_002811) Copy   

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http://www.imexconsortium.org/

Interaction database from international collaboration between major public interaction data providers who share curation effort and develop set of curation rules when capturing data from both directly deposited interaction data or from publications in peer reviewed journals. Performs complete curation of all protein-protein interactions experimentally demonstrated within publication and makes them available in single search interface on common website. Provides data in standards compliant download formats. IMEx partners produce their own separate resources, which range from all encompassing molecular interaction databases, such as are maintained by IntAct, MINT and DIP, organism-centric resources such as BioGrid or MPIDB or biological domain centric, such as MatrixDB. They have committed to making records available, via PSICQUIC webservice, which have been curated to IMEx rules and are available to users as single, non-redundant set of curated publications which can be searched at the IMEx website. Data is made available in standards-compliant tab-deliminated and XML formats, enabling to visualize data using wide range of tools. Consortium is open to participation of additional partners and encourages deposition of data, prior to publication, and will supply unique accession numbers which may be referenced within final article. Submitters may send their data directly to any of member databases using variety of formats, but should conform to guidelines as to minimum information required to describe data.

Proper citation: IMEx - The International Molecular Exchange Consortium (RRID:SCR_002805) Copy   

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http://funsimmat.bioinf.mpi-inf.mpg.de

FunSimMat is a comprehensive resource of semantic and functional similarity values. It allows ranking disease candidate proteins for OMIM diseases and searching for functional similarity values for proteins (extracted from UniProt), and protein families (Pfam, SMART). FunSimMat provides several different semantic and functional similarity measures for each protein pair using the Gene Ontology annotation from UniProtKB and the Gene Ontology Annotation project at EBI (GOA). There are several search options available: Disease candidate prioritization: * Rank candidate proteins using any OMIM disease entry * Compare a list of proteins to any OMIM disease entry * Compare all human proteins to any OMIM disease entry Functional similarity: * Compare one protein / protein family to a list of proteins / protein families * Compare a list of GO terms to a list of proteins / protein families Semantic similarity: * For a list of GO terms, FunSimMat performs an all-against-all comparison and displays the semantic similarity values. FunSimMat provides an XML-RPC interface for performing automatic queries and processing of the results as well as a RestLike Interface. Platform: Online tool

Proper citation: FunSimMat (RRID:SCR_002729) Copy   

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http://www.genepaint.org

Digital atlas of gene expression patterns in developing and adult mouse. Several reference atlases are also available through this site. Expression patterns are determined by non-radioactive in situ hybridization on serial tissue sections. Sections are available from several developmental ages: E10.5, E14.5 (whole embryos), E15.5, P7 and P56 (brains only). To retrieve expression patterns, search by gene name, site of expression, GenBank accession number or sequence homology. For viewing expression patterns, GenePaint.org features virtual microscope tool that enables zooming into images down to cellular resolution.

Proper citation: GenePaint (RRID:SCR_003015) Copy   

•   Source: SciCrunch Registry

http://www.mged.org/Workgroups/MAGE/mage-ml.html

A language / data exchange format designed to describe and communicate information about microarray based experiments that is based on XML and can describe microarray designs, microarray manufacturing information, microarray experiment setup and execution information, gene expression data and data analysis results. MAGE-ML has been automatically derived from Microarray Gene Expression Object Model (MAGE-OM), which is developed and described using the Unified Modelling Language (UML) -- a standard language for describing object models. Descriptions using UML have an advantage over direct XML document type definitions (DTDs), in many respects. First they use graphical representation depicting the relationships between different entities in a way which is much easier to follow than DTDs. Second, the UML diagrams are primarily meant for humans, while DTDs are meant for computers. Therefore MAGE-OM should be considered as the primary model, and MAGE-ML will be explained by providing simplified fragments of MAGE-OM, rather then XML DTD or XML Schema. (from the description by Ugis Sarkans) The field of gene expression experiments has several distinct technologies that a standard must include. These include single vs. dual channel experiments, cDNA vs. oligonucleotides. Because of these different technologies and different types of gene expression experiments, it is not expected that all aspects of the standard will be used by all organizations. Given the massive amount of data associated with a single set of experiments, it is felt that Extensible Markup Language (XML) is the best way to describe the data. The use of a Document Type Definition (DTD) allows a well-defined tag set, a vocabulary, to describe the domain of gene expression experiments. It also has the virtue of compressing very well so that files in an XML format compress to ten percent of their original size. XML is now widely accepted as a data exchange format across multiple platforms.

Proper citation: MicroArray and Gene Expression Markup Language (RRID:SCR_003023) Copy   

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

International functional genomics data collection generated from microarray or next-generation sequencing (NGS) platforms. Repository of functional genomics data supporting publications. Provides genes expression data for reuse to the research community where they can be queried and downloaded. Integrated with the Gene Expression Atlas and the sequence databases at the European Bioinformatics Institute. Contains a subset of curated and re-annotated Archive data which can be queried for individual gene expression under different biological conditions across experiments. Data collected to MIAME and MINSEQE standards. Data are submitted by users or are imported directly from the NCBI Gene Expression Omnibus.

Proper citation: ArrayExpress (RRID:SCR_002964) Copy   

•   Source: SciCrunch Registry

http://briansimulator.org/

Software Python package for simulating spiking neural networks. Useful for neuroscientific modelling at systems level, and for teaching computational neuroscience. Intuitive and efficient neural simulator.

Proper citation: Brian Simulator (RRID:SCR_002998) Copy   

•   Source: SciCrunch Registry

http://www.mugen-noe.org/database/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 5, 2023. MUGEN Mouse Database (MMdb) is a virtual and fully searchable repository of murine models of immune processes and immunological diseases. MMdb is being developed within the context of the MUGEN network of Excellence, a consortium of 21 leading research institutes and universities, and currently holds all mutant mouse models that were developed within the consortium. Its primary aim is to enable information exchange between participating institutions on mouse strain characteristics and availability. More importantly, it aims to create a mouse-centric international forum on modelling of immunological diseases and pave the way to systems biology of the mouse by correlating various genotypic and phenotypic characteristics. The basic categorization of models is based on three major research application categories: * Model of Human Disease * Model of Immune Processes * Transgenic Tool Mutant strains carry detailed information on affected gene(s), mutant alleles and genetic background (DNA origin, targeted, host and backcrossing background). Each gene/transgene index also includes IDs and direct links to Ensembl (EBI��s genome browser), ArrayExpress (providing expression profiles), Eurexpress II (for embryonic expression patterns) and NCBI��s Entrez Gene database. Phenotypic description is standardized and hierarchically structured, based on MGI��s mammalian phenotypic ontology terms, but also includes relevant images and references. Since version 2.1.0 MMdb is also utilizing PATO. Availability (in the form of live mice, cryopreserved embryos or sperm, as well as ES cells) is clearly indicated, along with handling and genotyping details (in the form of documents or hyperlinks) and all relevant contact information (including EMMA and JAX hyperlinks where available).

Proper citation: MUGEN Mouse Database (RRID:SCR_003243) Copy   

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https://protein.mpiib-berlin.mpg.de/cgi-bin/pdbs/2d-page/extern/index.cgi

The Proteome 2D-PAGE Database system for microbial research is a curated database for storing and investigating proteomics data. Software tools are available and for data submission, please contact the Database Curator. Established at the Max Plank Institution for Infection Biology, this system contains four interconnected databases: i.) 2D-PAGE Database: Two dimensional electrophoresis (2-DE) and mass spectrometry of diverse microorganisms and other organisms. This database currently contains 4971 identified spots and 1228 mass peaklists in 44 reference maps representing experiments from 24 different organisms and strains. The data were submitted by 84 Submitters from 24 Institutes and 12 nations. It also contains various software tools that are important in formatting and analyzing gels and mass peaks; software include: *TopSpot: Scanning the gel, editing the spots and saving the information *Fragmentation: Fragmentation of the gel image into sections *MS-Screener: Perl script to compare the similarity of MALDI-PMF peaklists *MS-Screener update: MS-Screener can be used to compare mass spectra (MALDI-MS(/MS) as well as ESI-MS/MS spectra) on the basis of their peak lists (.dta, .pkm, .pkt, or .txt files), to recalibrate mass spectra, to determine and eliminate exogenous contaminant peaks, and to create matrices for cluster analyses. *GelCali: Online calibration of the Mr- and pI-axis of 2-DE gels with mathematical regression methods ii.)Isotope Coded Affinity Tag (ICAT)-LC/MS database: Isotope Coded Affinity Tag (ICAT)-LC/MS data for Mycobacterium tuberculosis strain BCG versus H37Rv. iii.) FUNC_CLASS database: Functional classification of diverse microorganism. This database also integrates genomic, proteomic, and metabolic data. iv.) DIFF database: Presentation of differently regulated proteins obtained by comparative proteomic experiments using computerized gel image analysis.

Proper citation: Proteome 2D-PAGE Database (RRID:SCR_001678) Copy   

•   Source: SciCrunch Registry

http://videolectures.net/

Award-winning free and open access educational video lectures repository. The lectures are given by distinguished scholars and scientists at the most important and prominent events like conferences, summer schools, workshops and science promotional events from many fields of Science. The portal is aimed at promoting science, exchanging ideas and fostering knowledge sharing by providing high quality didactic contents not only to the scientific community but also to the general public. All lectures, accompanying documents, information and links are systematically selected and classified through the editorial process taking into account also users' comments.

Proper citation: VideoLectures.NET (RRID:SCR_001972) Copy   

•   Source: SciCrunch Registry

http://mips.gsf.de/genre/proj/yeast/index.jsp

The MIPS Comprehensive Yeast Genome Database (CYGD) aims to present information on the molecular structure and functional network of the entirely sequenced, well-studied model eukaryote, the budding yeast Saccharomyces cerevisiae. In addition, the data of various projects on related yeasts are used for comparative analysis.

Proper citation: CYGD - Comprehensive Yeast Genome Database (RRID:SCR_002289) Copy   

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http://www.gene-regulation.com/pub/databases.html#transpath

Database on eukaryotic transcription factors, their experimentally-proven binding sites, consensus binding sequences (positional weight matrices) and regulated genes. Its broad compilation of binding sites allows the derivation of positional weight matrices. It can either be used as an encyclopedia, for both specific and general information on signal transduction, or can serve as a network analyzer. Cross-references to important sequence and signature databases such as EMBL/GenBank UniProt/Swiss-Prot InterPro or Ensembl EntrezGene RefSeq are provided. The database is equipped with the tools for data visualization and analysis. It has three modules: the first one is the data, which have been manually extracted, mostly from the primary literature; the second is PathwayBuilder, which provides several different types of network visualization and hence facilitates understanding; the third is ArrayAnalyzer, which is particularly suited to gene expression array interpretation, and is able to identify key molecules within signalling networks (potential drug targets). These key molecules could be responsible for the coordinated regulation of downstream events. Manual data extraction focuses on direct reactions between signalling molecules and the experimental evidence for them, including species of genes/proteins used in individual experiments, experimental systems, materials and methods. This combination of materials and methods is used in TRANSPATH to assign a quality value to each experimentally proven reaction, which reflects the probability that this reaction would happen under physiological conditions. Another important feature in TRANSPATH is the inclusion of transcription factor-gene relations, which are transferred from TRANSFAC, a database focused on transcription regulation and transcription factors. Since interactions between molecules are mainly direct, this allows a complete and stepwise pathway reconstruction from ligands to regulated genes.

Proper citation: TRANSPATH (RRID:SCR_005640) Copy   

•   Source: SciCrunch Registry