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| Resource Name | Proper Citation | Abbreviations | Resource Type |
Description |
Keywords | Resource Relationships | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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Oncotator Resource Report Resource Website 100+ mentions |
Oncotator (RRID:SCR_005183) | Oncotator | data analysis service, production service resource, service resource, analysis service resource | A tool for annotating human genomic point mutations and indels with data relevant to cancer researchers. Genomic Annotations, Protein Annotations, and Cancer Annotations are aggregated from many resources. A standalone version of Oncotator is being developed. | annotate, genomic, point mutation, indel, mutation, genome, protein, variant |
is listed by: OMICtools has parent organization: Broad Institute |
Cancer | OMICS_00178 | SCR_005183 | 2026-08-05 10:44:11 | 215 | ||||||||
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YMDB - Yeast Metabolome Database Resource Report Resource Website 10+ mentions |
YMDB - Yeast Metabolome Database (RRID:SCR_005890) | YMDB | data or information resource, database | A manually curated database of small molecule metabolites found in or produced by Saccharomyces cerevisiae (also known as Baker's yeast and Brewer's yeast). This database covers metabolites described in textbooks, scientific journals, metabolic reconstructions and other electronic databases. YMDB contains metabolites arising from normal S. cerevisiae metabolism under defined laboratory conditions as well as metabolites generated by S. cerevisiae when used in baking and in the production of wines, beers and spirits. YMDB currently contains 2027 small molecules with 857 associated enzymes and 138 associated transporters. Each small molecule has 48 data fields describing the metabolite, its chemical properties and links to spectral and chemical databases. Each enzyme/transporter is linked to its associated metabolites and has 30 data fields describing both the gene and corresponding protein. Users may search through the YMDB using a variety of database-specific tools. The simple text query supports general text queries of the textual component of the database. By selecting either metabolites or proteins in the search for field it is possible to restrict the search and the returned results to only those data associated with metabolites or with proteins. Clicking on the Browse button generates a tabular synopsis of YMDB's content. This browser view allows users to casually scroll through the database or re-sort its contents. Clicking on a given MetaboCard button brings up the full data content for the corresponding metabolite. A complete explanation of all the YMDB fields and sources is available. Under the Search link users will find a number of search options listed in a pull-down menu. The Chem Query option allows users to draw (using MarvinSketch applet or a ChemSketch applet) or to type (SMILES string) a chemical compound and to search the YMDB for chemicals similar or identical to the query compound. The Advanced Search option supports a more sophisticated text search of the text portion of YMDB. The Sequence Search button allows users to conduct BLASTP (protein) sequence searches of all sequences contained in YMDB. Both single and multiple sequence (i.e. whole proteome) BLAST queries are supported. YMDB also supports a Data Extractor option that allows specific data fields or combinations of data fields to be searched and/or extracted. Spectral searches of YMDB's reference compound NMR and MS spectral data are also supported through its MS, MS/MS, GC/MS and NMR Spectra Search links. Users may download YMDB's complete textual data, chemical structures and sequence data by clicking on the Download button. | small molecule, metabolite, enzyme, transporter, gene, protein, chemical property, reaction, pathway, class, protein, proteome, blastp, nmr, ms spectra, chemical structure | has parent organization: University of Alberta; Alberta; Canada | Canadian Institutes of Health Research | PMID:22064855 | Freely available - Explicit permission / acknowledgment of the source material and original publication is required for commercial purposes. We ask users who download significant portions of the database cite the YMDB paper in any resulting publications. | nlx_151612, r3d100012733 | https://doi.org/10.17616/R3Z51K | SCR_005890 | Yeast Metabolome Database (YMDB), Yeast Metabolome Database | 2026-08-05 10:44:22 | 14 | ||||
|
Unified Human Interactome Resource Report Resource Website 10+ mentions |
Unified Human Interactome (RRID:SCR_005805) | UniHI | data or information resource, database | A database of human molecular interaction networks that integrates human protein-protein and transcriptional regulatory interactions from 15 distinct resources and aims to give direct and easy access to the integrated data set and to enable users to perform network-based investigations. The database includes tools (i) to search for molecular interaction partners of query genes or proteins in the integrated dataset, (ii) to inspect the origin, evidence and functional annotation of retrieved proteins and interactions, (iii) to visualize and adjust the resulting interaction network, (iv) to filter interactions based on method of derivation, evidence and type of experiment as well as based on gene expression data or gene lists and (v) to analyze the functional composition of interaction networks. | molecular interaction network, interactome, protein, protein interaction network, protein interaction, pathway, function, visualization, protein-protein interaction, transcriptional regulatory interaction, network |
is listed by: OMICtools has parent organization: University of Algarve; Faro; Portugal |
PMID:24214987 PMID:22218860 PMID:18984619 PMID:17158159 |
Public, Non-commercial | OMICS_01911, nif-0000-03609 | http://www.mdc-berlin.de/unihi | SCR_005805 | 2026-08-05 10:44:21 | 19 | ||||||
|
SNPsandGO Resource Report Resource Website 50+ mentions |
SNPsandGO (RRID:SCR_005788) | SNPs&GO | data analysis service, production service resource, service resource, analysis service resource | A server for the prediction of single point protein mutations likely to be involved in the insurgence of diseases in humans. | prediction, protein, mutation, disease, single nucleotide polymorphism, bio.tools |
is used by: HmtVar is listed by: OMICtools is listed by: Debian is listed by: bio.tools is related to: Gene Ontology has parent organization: University of Bologna; Bologna; Italy |
PMID:19514061 | biotools:snps_go, OMICS_02219 | https://bio.tools/snps_go | SCR_005788 | SNPs and GO | 2026-08-05 10:44:20 | 58 | ||||||
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IT-GOM: Integrated Tool for IC-based GO Semantic Similarity Measures Resource Report Resource Website 1+ mentions |
IT-GOM: Integrated Tool for IC-based GO Semantic Similarity Measures (RRID:SCR_005815) | IT-GOM | data analysis service, production service resource, service resource, analysis service resource | The Integrated Tool for IC-based GO Semantic Similarity Measures (IT-GOM) integrates the currently known GO semantic similarity measures into a single tool. It provides the information content (IC) of GO terms, semantic similarity between GO terms and GO-based protein functional similarity scores. The specificity of GO terms and the similarity of biological content between GO terms or proteins are transformed into numeric values for protein analyses at the functional level. The integration of the different measures enables users to choose the measure best suited to their application and to compare results between different semantic similarity measures. Platform: Online tool | semantic similarity, gene ontology, protein, functional similarity, function, annotation, topology |
is listed by: Gene Ontology Tools is related to: Gene Ontology is related to: UniProt is related to: GOA has parent organization: University of Cape Town; Western Cape; South Africa |
National Bioinformatics Network in South Africa ; University of Cape Town; Western Cape; South Africa ; Computational Biology research group at the Institute of Infectious Disease and Molecular Medicine |
Open unspecified license - Free for academic use | nlx_149310 | SCR_005815 | Integrated Tool for IC-based GO Semantic Similarity Measures (IT-GOM), Integrated Tool for IC-based GO Semantic Similarity Measures | 2026-08-05 10:44:20 | 1 | ||||||
|
HotRegion - A Database of Cooperative Hotspots Resource Report Resource Website 1+ mentions |
HotRegion - A Database of Cooperative Hotspots (RRID:SCR_006022) | HotRegion | data or information resource, database | Hot spots are energetically important residues at protein interfaces and they are not randomly distributed across the interface but rather clustered. These clustered hot spots form hot regions. Hot regions are important for the stability of protein complexes, as well as providing specificity to binding sites. HotRegion provides the hot region information of the interfaces by using predicted hot spot residues, and structural properties of these interface residues such as pair potentials of interface residues, accessible surface area (ASA) and relative ASA values of interface residues of both monomer and complex forms of proteins. Also, the 3D visualization of the interface and interactions among hot spot residues are provided. The number of interfaces in the database is 147909 and still growing. | residue, chain, complex, monomer, pair potential, hotspot, hotregion, accessible surface area, protein, bio.tools |
is listed by: Debian is listed by: bio.tools has parent organization: Koc University; Istanbul; Turkey |
Turkish Academy of Sciences ; TUBITAK 109T343; TUBITAK 109E207 |
PMID:22080558 | nlx_151420, biotools:hotregion | https://bio.tools/hotregion | SCR_006022 | HotRegion: a database of predicted hot spot clusters, HotRegion: A database of cooperative hot spots | 2026-08-05 10:44:22 | 5 | |||||
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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 | 2026-08-05 10:44:24 | 1 | |||||
|
GORetriever Resource Report Resource Website 10+ mentions |
GORetriever (RRID:SCR_005633) | GORetriever | data analysis service, production service resource, service resource, analysis service resource | GORetriever is used to find all of the GO annotations corresponding to a list of user-supplied protein identifiers. GORetriever produces a list of proteins and their annotations and a separate list of entries with no GO annotation. Platform: Online tool | gene, annotation, protein, ontology or annotation search engine |
is listed by: Gene Ontology Tools is related to: Gene Ontology has parent organization: AgBase |
USDA ; Mississippi State University; Mississippi; USA ; MSU Office of Research ; MSU Bagley College of Engineering ; MSU College of College of Veterinary Medicine ; MSU Life Science and Biotechnology Institute |
PMID:17135208 PMID:16961921 |
Free for academic use | nlx_149140 | SCR_005633 | AgBase GORetriever | 2026-08-05 10:44:17 | 13 | |||||
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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 | 2026-08-05 10:44:21 | 0 | |||||
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ProteInOn Resource Report Resource Website 1+ mentions |
ProteInOn (RRID:SCR_005740) | data analysis service, production service resource, service resource, analysis 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 | 2026-08-05 10:44:20 | 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 | 2026-08-05 10:44:21 | 6 | |||||
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AMYL-PRED Resource Report Resource Website |
AMYL-PRED (RRID:SCR_006185) | AMYL-PRED | data analysis service, production service resource, service resource, analysis 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 | 2026-08-05 10:44:26 | 0 | |||||||
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HMM-TM Resource Report Resource Website 1+ mentions |
HMM-TM (RRID:SCR_006186) | HMM-TM | data analysis service, production service resource, service resource, analysis 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 | 2026-08-05 10:44:27 | 6 | |||||
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PRED-LIPO Resource Report Resource Website 10+ mentions |
PRED-LIPO (RRID:SCR_006187) | PRED-LIPO | data analysis service, production service resource, service resource, analysis 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 | 2026-08-05 10:44:23 | 17 | ||||
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PRED-SIGNAL Resource Report Resource Website 10+ mentions |
PRED-SIGNAL (RRID:SCR_006181) | PRED-SIGNAL | data analysis service, production service resource, service resource, analysis 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 | 2026-08-05 10:44:23 | 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 | 2026-08-05 10:44:29 | 64 | |||
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PRED-CLASS Resource Report Resource Website |
PRED-CLASS (RRID:SCR_006216) | PRED-CLASS | data analysis service, production service resource, service resource, analysis 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 | 2026-08-05 10:44:24 | 0 | ||||||
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CoPreTHi Resource Report Resource Website |
CoPreTHi (RRID:SCR_006217) | CoPreTHi | data analysis service, production service resource, service resource, analysis 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 | 2026-08-05 10:44:26 | 0 | ||||||
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ProRepeat Resource Report Resource Website 1+ mentions |
ProRepeat (RRID:SCR_006113) | ProRepeat | data or information resource, database | ProRepeat is an integrated curated repository and analysis platform for in-depth research on the biological characteristics of amino acid tandem repeats. ProRepeat collects repeats from all proteins included in the UniProt knowledgebase, together with 85 completely sequenced eukaryotic proteomes contained within the RefSeq collection. It contains non-redundant perfect tandem repeats, approximate tandem repeats and simple, low-complexity sequences, covering the majority of the amino acid tandem repeat patterns found in proteins. The ProRepeat web interface allows querying the repeat database using repeat characteristics like repeat unit and length, number of repetitions of the repeat unit and position of the repeat in the protein. Users can also search for repeats by the characteristics of repeat containing proteins, such as entry ID, protein description, sequence length, gene name and taxon. ProRepeat offers powerful analysis tools for finding biological interesting properties of repeats, such as the strong position bias of leucine repeats in the N-terminus of eukaryotic protein sequences, the differences of repeat abundance among proteomes, the functional classification of repeat containing proteins and GC content constrains of repeats' corresponding codons. | amino acid, tandem, repeat, protein, sequence, nucleotide sequence, repeat fragment, protein repeat, proteome, sequence length, gene, taxon, bio.tools |
is listed by: Debian is listed by: bio.tools is related to: UniProtKB is related to: RefSeq has parent organization: Wageningen University and Research Centre; Gelderland; Netherlands |
PMID:22102581 | nlx_151587, biotools:prorepeat | https://bio.tools/prorepeat | SCR_006113 | 2026-08-05 10:44:22 | 1 | |||||||
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ProtChemSI Resource Report Resource Website 1+ mentions |
ProtChemSI (RRID:SCR_006115) | ProtChemSI | data or information resource, database | The database of protein-chemical structural interactions includes all existing 3D structures of complexes of proteins with low molecular weight ligands. When one considers the proteins and chemical vertices of a graph, all these interactions form a network. Biological networks are powerful tools for predicting undocumented relationships between molecules. The underlying principle is that existing interactions between molecules can be used to predict new interactions. For pairs of proteins sharing a common ligand, we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other. The current version includes data from the Protein Data Bank as of August 2011. The database is updated monthly. | protein, chemical, 3d structure, biological network, interaction, ligand, prediction, fasta, fasta sequence, smiles string, complex, bio.tools |
is listed by: bio.tools is listed by: Debian is related to: Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) has parent organization: Heidelberg University; Baden-Wurttemberg; Germany |
PMID:21573205 | Acknowledgement requested | nlx_151590, biotools:protchemsi | https://bio.tools/protchemsi | SCR_006115 | Protein-Chemical Structural Interactions, ProtChemSI: protein-chemical interaction database, ProtChemSI - the database of protein-chemical structural interactions | 2026-08-05 10:44:25 | 3 |
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