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| Resource Name | Proper Citation | Abbreviations | Resource Type |
Description |
Keywords | Resource Relationships | |||||||||||||
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DOMMINO - Database Of MacroMolecular INteractiOns Resource Report Resource Website 1+ mentions |
DOMMINO - Database Of MacroMolecular INteractiOns (RRID:SCR_005958) | DOMMINO | data or information resource, database | DOMMINO is a comprehensive structural database on macromolecular interactions. As of June, 2011, it contains more than 407,000 binary interactions. The distinctive features of DOMMINO are: # Automated updates: DOMMINO is fully automated and is designed to update itself on a weekly basis, one day after a PDB weekly update. Thus, the community will be able to study macromolecular interactions almost immediately after they are released by PDB. # Coverage of non-domain mediated interactions: In addition to domain-domain and domain-peptide interactions the database characterizes the interaction between domains and unstructured protein regions that are not parts of a domain, such as inter-domain linkers and N- and C-termini. The interactions that involve the latter unstructured parts of proteins have been included to the database for the first time providing additional ~186,000 interactions (~45% of the total number of interactions, as of June, 2011). # Coverage of new structural domains: DOMMINO employs one of the most accurate structural classifications of proteins, SCOP. In addition to the existing SCOP-annotated domains, we employ a state-of-the-art machine learning approach to classify newer protein structures into existing SCOP families. With the progress of structural genomics, we do not expect a significant growth of the number of structurally novel folds or protein families and therefore our method allows covering almost all new protein structures. In total, using this predictive approach has allowed us to add more than 261,000 new interactions, almost twice as many as existing SCOP-annotated interactions. # The web-interface is designed to give the user a possibility of a flexible search as well as the capability to study macromolecular interactions in a PDB structure at the interaction network level and at the individual interface level. The web interface of the DOMMINO database includes a comprehensive list of help topics linked to the specific actions. In addition, we have designed a step-by-step tutorial that covers all aspects of working with the data from DOMMINO using the web interface. | macromolecular interaction, macromolecule, structural domain, non-domain mediated interaction, protein, domain, peptide, interaction, protein-protein interaction, protein-peptide interaction, protein-dna interactions, protein-rna interactions, rna-rna interactions, rna-dna interactions, interface structure, bio.tools |
is listed by: Debian is listed by: bio.tools is related to: Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) is related to: SCOP: Structural Classification of Proteins has parent organization: University of Missouri; Missouri; USA |
NSF DBI-0845196 | PMID:22135305 | biotools:dommino, nlx_151316 | http://orion.rnet.missouri.edu/~nz953/DOMMINO/, https://bio.tools/dommino | SCR_005958 | Database Of MacroMolecular INteractiOns | 2026-09-12 01:01:40 | 1 | |||||
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CharProtDB: Characterized Protein Database Resource Report Resource Website |
CharProtDB: Characterized Protein Database (RRID:SCR_005872) | CharProtDB | data or information resource, database | The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated. | protein, annotation, functional annotation, taxonomic classification, literature, gene ontology, evidence code, enzyme commission, transport classification, protein sequence, bio.tools |
is listed by: Debian is listed by: bio.tools is related to: Gene Ontology has parent organization: J. Craig Venter Institute |
NHGRI R01 HG004881; NIAID contract HHSN266200100038C |
PMID:22140108 | biotools:charprotdb, nlx_149421 | https://bio.tools/charprotdb | SCR_005872 | Characterized Protein Database | 2026-09-12 01:01:40 | 0 | |||||
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ProteInOn Resource Report Resource Website 1+ mentions |
ProteInOn (RRID:SCR_005740) | analysis service resource, data analysis service, production service resource, service resource | ProteInOn calculates semantic similarity between GO terms or proteins annotated with GO terms. It also calculates term enrichment of protein sets, by applying a term representativity score, and gives additional information on protein interactions. The query compute protein semantic similarity returns the semantic similarity scores between all proteins entered, in matrix format. The option Measure allows users to choose one of several semantic similarity measures: Resnik, Lin, or Jiang & Conrath's measures with or without the DCA approach, plus the graph-based simUI and simGIC measures. These measures are listed by order of performance as evaluated with protein sequence similarity. The option GO type allows users to choose one of the aspects of GO: molecular function, biological process and cellular component. The option Ignore IEA limits the query to non-electronic annotations, excluding evidence types: IEA, NAS, ND, NR. | protein, ontology, gene ontology, annotation, statistical analysis, term enrichment, protein interaction, semantic similarity, other analysis |
is listed by: Gene Ontology Tools is related to: Gene Ontology is related to: FuSSiMeG: Functional Semantic Similarity Measure between Gene-Products has parent organization: University of Lisbon; Lisbon; Portugal |
Free for academic use | nlx_149206 | SCR_005740 | Protein Interactions Ontology, ProteInOn - Protein Interactions and Ontology, Protein Interactions and Ontology | 2026-09-12 01:01:39 | 2 | ||||||||
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VirHostNet: Virus-Host Network Resource Report Resource Website 1+ mentions |
VirHostNet: Virus-Host Network (RRID:SCR_005978) | VirHostNet | data or information resource, database | Public knowledge base specialized in the management and analysis of integrated virus-virus, virus-host and host-host interaction networks coupled to their functional annotations. It contains high quality and up-to-date information gathered and curated from public databases (VirusMint, Intact, HIV-1 database). It allows users to search by host gene, host/viral protein, gene ontology function, KEGG pathway, Interpro domain, and publication information. It also allows users to browse viral taxonomy. | interaction, protein, virus, protein-protein interaction, protein interaction, infectious disease, antiviral drug design, proteome, interactome, molecular function, cellular pathway, protein domain, virus-virus, virus-host, bio.tools |
is listed by: OMICtools is listed by: Debian is listed by: bio.tools is related to: Gene Ontology is related to: VirusMINT is related to: IntAct is related to: HIV-1 Human Protein Interaction Database is related to: PSICQUIC Registry has parent organization: Claude Bernard University Lyon 1; Lyon; France |
PMID:18984613 | Acknowledgement requested, Public | nif-0000-03634, OMICS_01910, biotools:virhostnet | https://bio.tools/virhostnet | SCR_005978 | Virus-Host Network | 2026-09-12 01:01:40 | 7 | |||||
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AMYL-PRED Resource Report Resource Website |
AMYL-PRED (RRID:SCR_006185) | AMYL-PRED | analysis service resource, data analysis service, production service resource, service resource | A web tool using the consensus prediction method for identifying possible amyloidogenic regions in protein sequences. This tool uses an assortment of different methods that have been found or specifically developed to predict features related to the formation of amyloid fibrils. The consensus of these methods is defined as the the hit overlap of at least two out of five methods and it is the primary output of the program. However, the individual predictions of these methods are also made available in the form of a text file, maintained on the server for 1 (one) day. Consequently, the tool predicts probable amyloidogenic determinants for a given amino acid sequence of a peptide or protein. | amyloidogenic region, protein sequence, prediction, amyloid, amino acid sequence, peptide, protein, amyloid fibril | has parent organization: University of Athens Biophysics and Bioinformatics Laboratory | Free for academic use, Non-academic users should contact Prof. S.J. Hamodrakas (shamodr at biol.uoa.gr). | nlx_151730 | SCR_006185 | AMYL-PRED: A Consensus Method for Amyloid Propensity Prediction | 2026-09-12 01:01:41 | 0 | |||||||
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HMM-TM Resource Report Resource Website 1+ mentions |
HMM-TM (RRID:SCR_006186) | HMM-TM | analysis service resource, data analysis service, production service resource, service resource | A web tool using the Hidden Markov Model method for the topology prediction of alpha-helical membrane proteins that incorporates experimentally derived topological information. Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM''s classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications. | hidden markov model, topology, prediction, alpha-helical membrane protein, protein, transmembrane, transmembrane alpha-helical protein, bio.tools |
is listed by: Debian is listed by: bio.tools has parent organization: University of Athens Biophysics and Bioinformatics Laboratory |
PMID:16597327 | Free for academic use | nlx_151731, biotools:hmm-tm | https://bio.tools/hmm-tm | SCR_006186 | HMM-TM: Prediction of Transmembrane Alpha-Helical Proteins | 2026-09-12 01:01:41 | 7 | |||||
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PRED-LIPO Resource Report Resource Website 10+ mentions |
PRED-LIPO (RRID:SCR_006187) | PRED-LIPO | analysis service resource, data analysis service, production service resource, service resource | A web tool using the Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides. | hidden markov model, lipoprotein signal peptide, gram-positive bacteria, lipoprotein, prediction, peptide, protein, signal peptide, bio.tools |
is listed by: Debian is listed by: bio.tools has parent organization: University of Athens Biophysics and Bioinformatics Laboratory |
National Scholarships Foundation of Greece | PMID:19367716 | Free | nlx_151732, biotools:pred-lipo | https://bio.tools/pred-lipo | SCR_006187 | PRED-LIPO: Prediction of Lipoprotein and Secretory Signal Peptides in Gram-positive Bacteria with Hidden Markov Models | 2026-09-12 01:01:41 | 17 | ||||
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PRED-SIGNAL Resource Report Resource Website 10+ mentions |
PRED-SIGNAL (RRID:SCR_006181) | PRED-SIGNAL | analysis service resource, data analysis service, production service resource, service resource | A web tool for prediction of signal peptides in archaea. Computational prediction of signal peptides (SPs) and their cleavage sites is of great importance in computational biology; however, currently there is no available method capable of predicting reliably the SPs of archaea, due to the limited amount of experimentally verified proteins with SPs. We performed an extensive literature search in order to identify archaeal proteins having experimentally verified SP and managed to find 69 such proteins, the largest number ever reported. A detailed analysis of these sequences revealed some unique features of the SPs of archaea, such as the unique amino acid composition of the hydrophobic region with a higher than expected occurrence of isoleucine, and a cleavage site resembling more the sequences of gram-positives with almost equal amounts of alanine and valine at the position-3 before the cleavage site and a dominant alanine at position-1, followed in abundance by serine and glycine. Using these proteins as a training set, we trained a hidden Markov model method that predicts the presence of the SPs and their cleavage sites and also discriminates such proteins from cytoplasmic and transmembrane ones. | signal peptide, prediction, protein, bio.tools |
is listed by: Debian is listed by: bio.tools has parent organization: University of Athens Biophysics and Bioinformatics Laboratory |
State Scholarships Foundation of Greece | PMID:18988691 | Free for academic use | biotools:pred-signal, nlx_151728 | https://bio.tools/pred-signal | SCR_006181 | PRED-SIGNAL - Prediction of Signal Peptides in Archaea with Hidden Markov Models | 2026-09-12 01:01:41 | 14 | ||||
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Tuberculosis Database Resource Report Resource Website 50+ mentions |
Tuberculosis Database (RRID:SCR_006619) | TBDB | data or information resource, database | Database providing integrated access to genome sequence, expression data and literature curation for Tuberculosis (TB) that houses genome assemblies for numerous strains of Mycobacterium tuberculosis (MTB) as well assemblies for over 20 strains related to MTB and useful for comparative analysis. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives, including over 3000 MTB microarrays, 95 RT-PCR datasets, 2700 microarrays for human and mouse TB related experiments, and 260 arrays for Streptomyces coelicolor. (July 2010) To enable wide use of these data, TBDB provides a suite of tools for searching, browsing, analyzing, and downloading the data. | genomic, protein, blast, genome, gene, systems biology, gene expression, microarray, comparative analysis, regulatory network, metabolic network, epitope, expression profile, rt-pcr, gene regulation, genome browser, FASEB list |
is listed by: re3data.org is related to: SMD is related to: BioCyc has parent organization: Broad Institute has parent organization: Stanford University School of Medicine; California; USA |
Tuberculosis | Bill and Melinda Gates Foundation | PMID:20488753 PMID:18835847 |
Acknowledgement requested, Public, (Published data) | nif-0000-03537, r3d100010930 | https://doi.org/10.17616/R39G8F | SCR_006619 | TB Database, TBDatabase | 2026-09-12 01:01:43 | 64 | |||
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PRED-CLASS Resource Report Resource Website |
PRED-CLASS (RRID:SCR_006216) | PRED-CLASS | analysis service resource, data analysis service, production service resource, service resource | A system of cascading neural networks that classifies any protein, given its amino acid sequence alone, into one of four possible classes: membrane, globular, fibrous, mixed. | classification, protein, fibrous, globular, protein class, membrane, sequence, algorithm, protein classification, neural network, transmembrane, genome annotation, genome-wide analysis |
is related to: DAM-Bio has parent organization: University of Athens Biophysics and Bioinformatics Laboratory |
European Union ERBFMRXCT960019 | PMID:11455609 | nlx_151762 | SCR_006216 | PRED-CLASS - Classification of proteins into one of four possible classes | 2026-09-12 01:01:41 | 0 | ||||||
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CoPreTHi Resource Report Resource Website |
CoPreTHi (RRID:SCR_006217) | CoPreTHi | analysis service resource, data analysis service, production service resource, service resource | A Java based web application, which combines the results of methods that predict the location of transmembrane segments in protein sequences into a joint prediction histogram. Clearly, the joint prediction algorithm, produces superior quality results than individual prediction schemes. | java, predict, transmembrane, region, protein, histogram, algorithm, joint prediction | has parent organization: University of Athens Biophysics and Bioinformatics Laboratory | PMID:11471236 | Free | nlx_151763 | SCR_006217 | CoPreTHi - A Java-program which Combines the results of several methods (available throught the Internet ) that Predict Transmembrane regions in proteins in a joint prediction Histogram | 2026-09-12 01:01:41 | 0 | ||||||
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DroID - Drosophila Interactions Database Resource Report Resource Website 10+ mentions |
DroID - Drosophila Interactions Database (RRID:SCR_006634) | DroID | data or information resource, database | A gene and protein interactions database designed specifically for the model organism Drosophila including protein-protein, transcription factor-gene, microRNA-gene, and genetic interactions. For advanced searches and dynamic graphing capabilities the IM Browser and a DroID Cytoscape plugin are available. | interaction, gene, protein, protein interaction, annotation, transcription factor, rna, protein-protein interaction, interactome, gene expression, phenotype, interolog, ortholog |
is listed by: OMICtools is related to: Cytoscape has parent organization: Wayne State University School of Medicine; Michigan; USA |
PMID:21036869 PMID:18840285 |
Free, Public, Acknowledgement requested | nif-0000-02767, OMICS_01908 | SCR_006634 | DroID - The Drosophila Interactions Database | 2026-09-12 01:01:43 | 37 | ||||||
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ConBBPRED Resource Report Resource Website 1+ mentions |
ConBBPRED (RRID:SCR_006194) | ConBBPRED | analysis service resource, data analysis service, production service resource, service resource | A web tool for the Consensus Prediction of TransMembrane Beta-Barrel Proteins. Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. | predict, topology, beta-barrel outer membrane protein, outer membrane protein, protein, consensus prediction, gram-negative bacteria, transmembrane, beta-barrel protein | has parent organization: University of Athens Biophysics and Bioinformatics Laboratory | Greek Ministry of National Education and Religious Affairs | PMID:15647112 | Free, Non-commercial | nlx_151740 | SCR_006194 | 2026-09-12 01:01:41 | 3 | ||||||
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BioGPS: The Gene Portal Hub Resource Report Resource Website 500+ mentions |
BioGPS: The Gene Portal Hub (RRID:SCR_006433) | BioGPS | data or information resource, database | An extensible and customizable gene annotation portal that emphasizes community extensibility and user customizability. It is a complete resource for learning about gene and protein function. Community extensibility reflects a belief that any BioGPS user should be able to add new content to BioGPS using the simple plugin interface, completely independently of the core developer team. User customizability recognizes that not all users are interested in the same set of gene annotation data, so the gene report layouts enable each user to define the information that is most relevant to them. Currently, BioGPS supports eight species: Human (Homo sapiens), Mouse (Mus musculus), Rat (Rattus norvegicus), Fruitfly (Drosophila melanogaster), Nematode (Caenorhabditis elegans), Zebrafish (Danio rerio), Thale-cress (Arabidopsis thaliana), Frog (Xenopus tropicalis), and Pig (Sus scrofa). BioGPS presents data in an ortholog-centric format, which allows users to display mouse plugins next to human ones. Our data for defining orthologs comes from NCBI's HomoloGene database. | gene, ortholog, plug-in, report, literature, genetics, expression, reagent, protein, pathway, snp, genomics, gene annotation, function, FASEB list |
is listed by: Biositemaps is related to: bioDBcore is related to: aGEM has parent organization: Scripps Research Institute |
Novartis Research Foundation ; NIGMS R01GM083924 |
PMID:19919682 | Free, The community can contribute to this resource | r3d100012402, nif-0000-10168 | http://biogps.gnf.org/, https://doi.org/10.17616/R33J20 | SCR_006433 | 2026-09-12 01:01:42 | 814 | |||||
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Scansite Resource Report Resource Website 100+ mentions |
Scansite (RRID:SCR_007026) | data or information resource, database | Scansite searches for motifs within proteins that are likely to be phosphorylated by specific protein kinases or bind to domains such as SH2 domains, 14-3-3 domains or PDZ domains. The Motifscanner program utilizes an entropy approach that assesses the probability of a site matching the motif using the selectivity values and sums the logs of the probability values for each amino acid in the candidate sequence. The program then indicates the percentile ranking of the candidate motif in respect to all potential motifs in proteins of a protein database. When available, percentile scores of some confirmed phosphorylation sites for the kinase of interests or confirmed binding sites of the domain of interest are provided for comparison with the scores of the candidate motifs. | binding, kinase, phosphorylate, protein, bio.tools, FASEB list |
is listed by: bio.tools is listed by: Debian |
biotools:scansite, nif-0000-20914 | https://bio.tools/scansite | SCR_007026 | Scansite | 2026-09-12 01:01:45 | 302 | ||||||||
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HIstome: The Histone Infobase Resource Report Resource Website 1+ mentions |
HIstome: The Histone Infobase (RRID:SCR_006972) | HIstome | data or information resource, database | Database of human histone variants, sites of their post-translational modifications and various histone modifying enzymes. The database covers 5 types of histones, 8 types of their post-translational modifications and 13 classes of modifying enzymes. Many data fields are hyperlinked to other databases (e.g. UnprotKB/Swiss-Prot, HGNC, OMIM, Unigene etc.). Additionally, this database also provides sequences of promoter regions (-700 TSS +300) for all gene entries. These sequences were extracted from the UCSC genome browser. Sites of post-translational modifications of histones were manually searched from PubMed listed literature. Current version contains information for about ~50 histone proteins and ~150 histone modifying enzymes. HIstome is a combined effort of researchers from two institutions, Advanced Center for Treatment, Research and Education in Cancer (ACTREC), Navi Mumbai and Center of Excellence in Epigenetics (CoEE), Indian Institute of Science Education and Research (IISER), Pune. | histone, protein, enzyme, modifying enzyme, post-translational modification, variant, promoter region, gene, epigenetic regulation, india, bio.tools |
is listed by: re3data.org is listed by: Debian is listed by: bio.tools has parent organization: ACTREC - Advanced Centre for Treatment Research and Education in Cancer |
Cancer | ACTREuropean Union - Advanced Centre for Treatment Research and Education in Cancer ; Government of India |
PMID:22140112 | Free, Public, Acknowledgement requested | biotools:histome, r3d100010977, nlx_151419 | http://www.actrec.gov.in/histome/, https://bio.tools/histome, https://doi.org/10.17616/R3RD0R | http://www.histome.net/ | SCR_006972 | 2026-09-12 01:01:45 | 1 | |||
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HPRD - Human Protein Reference Database Resource Report Resource Website 1000+ mentions |
HPRD - Human Protein Reference Database (RRID:SCR_007027) | HPRD | data or information resource, database | Database that represents a centralized platform to visually depict and integrate information pertaining to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome. All the information in HPRD has been manually extracted from the literature by expert biologists who read, interpret and analyze the published data. | protein, disease, network, post-translational, proteome, protein binding, protein s, protein c, pathway, protein-protein interaction, protein expression, subcellular localization, phosphorylation motif, signaling pathway, protein sequence, blast, molecule, domain, motif, post-translational modification, protein isoform, FASEB list |
is used by: Mutation Annotation and Genomic Interpretation is used by: Pathway Analysis Tool for Integration and Knowledge Acquisition is used by: GEMINI is listed by: re3data.org is related to: Human Proteinpedia is related to: MatrixDB is related to: Interaction Reference Index is related to: Pathway Commons is related to: ConsensusPathDB is related to: Gene Ontology is related to: Agile Protein Interactomes DataServer has parent organization: Johns Hopkins University; Maryland; USA has parent organization: Institute of Bioinformatics; Bangalore; India |
PMID:18988627 PMID:16381900 PMID:14525934 |
Acknowledgement requested, Free, Non-commercial, Commercial requires license | nif-0000-00137, r3d100010978 | https://doi.org/10.17616/R3MK9N | SCR_007027 | Human Protein Reference Database | 2026-09-12 01:01:45 | 1311 | |||||
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ProbeExplorer Resource Report Resource Website |
ProbeExplorer (RRID:SCR_007116) | ProbeExplorer | analysis service resource, data analysis service, production service resource, service resource | Probe Explorer is an open access web-based bioinformatics application designed to show the association between microarray oligonucleotide probes and transcripts in the genomic context, but flexible enough to serve as a simplified genome and transcriptome browser. Coordinates and sequences of the genomic entities (loci, exons, transcripts), including vector graphics outputs, are provided for fifteen metazoa organisms and two yeasts. Alignment tools are used to built the associations between Affymetrix microarrays probe sequences and the transcriptomes (for human, mouse, rat and yeasts). Search by keywords is available and user searches and alignments on the genomes can also be done using any DNA or protein sequence query. Platform: Online tool | bioinformatics, microarray, oligonucleotide probe, transcript, genomic, genome, transcriptome, alignment, affymetrix, probe sequence, dna, protein, sequence, statistical analysis |
is listed by: Gene Ontology Tools is related to: Gene Ontology has parent organization: University of Salamanca; Salamanca; Spain |
Open unspecified license - Free for academic use | nlx_149275 | SCR_007116 | Probe Explorer | 2026-09-12 01:01:45 | 0 | |||||||
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Therapeutic Target Database Resource Report Resource Website 50+ mentions |
Therapeutic Target Database (RRID:SCR_006892) | TTD | data or information resource, database | A database to provide information about the known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and the corresponding drugs/ligands directed at each of these targets. Also included in this database are links to relevant databases that contain information about the function, sequence, 3D structure, ligand binding properties, enzyme nomenclature and related literatures of each target.This database currently contains 1535 targets and 2107 drugs/ligands. Queries can be submitted by entering or selecting the required information in any one or combination of the five fields in the form. User can specify full name or any part of the name in a text field, or choose one item from an selection field. | therapeutic, protein, nucleic acid, disease, pathway, drug, ligand, target, FASEB list |
is listed by: OMICtools is related to: ConsensusPathDB has parent organization: National University of Singapore; Singapore; Singapore |
nif-0000-03596, OMICS_01593 | http://bidd.nus.edu.sg/group/cjttd/ | SCR_006892 | 2026-09-12 01:01:44 | 73 | ||||||||
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FunSpec Resource Report Resource Website 50+ mentions |
FunSpec (RRID:SCR_006952) | FunSpec | analysis service resource, data analysis service, production service resource, service resource | FunSpec is a web-based tool for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations, including GO terms. FunSpec (an acronym for Functional Specification) inputs a list of yeast gene names, and outputs a summary of functional classes, cellular localizations, protein complexes, etc. that are enriched in the list. The classes and categories evaluated were downloaded from the MIPS Database and the GO Database . In addition, many published datasets have been compiled to evaluate enrichment against. Hypertext links to the publications are given. The p-values, calculated using the hypergeometric distribution, represent the probability that the intersection of given list with any given functional category occurs by chance. The Bonferroni-correction divides the p-value threshold, that would be deemed significant for an individual test, by the number of tests conducted and thus accounts for spurious significance due to multiple testing over the categories of a database. After the Bonferroni correction, only those categories are displayed for which the chance probability of enrichment is lower than: p-value/#CD where #CD is the number of categories in the selected database. Without the Bonferroni Correction, all categories are displayed for which the same probability of enrichment is lower than: p-value threshold in an individual test Note that many genes are contained in many categories, especially in the MIPS database (which are hierarchical) and that this can create biases for which FunSpec currently makes no compensation. Also the databases are treated as independent from one another, which is really not the case, and each is searched seperately, which may not be optimal for statistical calculations. Nonetheless, we find it useful for sifting through the results of clustering analysis, TAP pulldowns, etc. Platform: Online tool | gene, protein, annotation, gene ontology, gene expression, clustering, prediction, statistical analysis, functional class, cellular localization, protein complex, yeast, FASEB list |
is listed by: Gene Ontology Tools is related to: Gene Ontology is related to: CYGD - Comprehensive Yeast Genome Database has parent organization: University of Toronto; Ontario; Canada |
Genome Canada ; CIHR ; University of Toronto Connaught Foundation |
PMID:12431279 | nlx_149246 | SCR_006952 | Functional Specification | 2026-09-12 01:01:44 | 91 |
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We recommend that you click next to the search bar to check some helpful tips on searches and refine your search firstly. Alternatively, please register your tool with the SciCrunch Registry by adding a little information to a web form, logging in will enable users to create a provisional RRID, but it not required to submit.
Welcome to the NIF Resources search. From here you can search through a compilation of resources used by NIF and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that NIF has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on NIF then you can log in from here to get additional features in NIF such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
If you are logged into NIF you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the facets that you can filter the data by.
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
