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| Resource Name | Proper Citation | Abbreviations | Resource Type |
Description |
Keywords | Resource Relationships | ||||||||||||||
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MIPModDB Resource Report Resource Website 1+ mentions |
MIPModDB (RRID:SCR_006058) | MIPModDB | data or information resource, database | This is a database of comparative protein structure models of MIP (Major Intrinsic Protein) family of proteins. The nearly completed sets of MIPs have been identified from the completed genome sequence of organisms available at NCBI. The structural models of MIP proteins were created by defined protocol. The database aims to provide key information of MIPs in particular based on sequence as well as structures. This will further help to decipher the function of uncharacterized MIPs. For each MIP entry, this database contains information about the source, gene structure, sequence features, substitutions in the conserved NPA motifs, structural model, the residues forming the selectivity filter and channel radius profile. For selected set of MIPs, it is possible to derive structure-based sequence alignment and evolutionary relationship. Sequences and structures of selected MIPs can be downloaded from MIPModDB database. | major intrinsic protein, model, protein structure, structural model, sequence, structure, superfamily, genome sequence, amino acid sequence, motif, bio.tools |
is listed by: Debian is listed by: bio.tools has parent organization: Indian Institute of Technology Kanpur; Uttar Pradesh; India |
Government of India | PMID:22080560 | Free | nlx_151460, biotools:mipmoddb | https://bio.tools/mipmoddb | SCR_006058 | SciCrunch Registry | Major Intrinsic Protein superfamily Models, MIPModDB - Major Intrinsic Protein superfamily Models | 2026-10-10 12:44:11 | 6 | ||||
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WEGO - Web Gene Ontology Annotation Plot Resource Report Resource Website 100+ mentions |
WEGO - Web Gene Ontology Annotation Plot (RRID:SCR_005827) | WEGO | analysis service resource, data analysis service, production service resource, service resource | Web Gene Ontology Annotation Plot (WEGO) is a simple but useful tool for plotting Gene Ontology (GO) annotation results. Different from other commercial software for chart creating, WEGO is designed to deal with the directed acyclic graph (DAG) structure of GO to facilitate histogram creation of GO annotation results. WEGO has been widely used in many important biological research projects, such as the rice genome project and the silkworm genome project. It has become one of the useful tools for downstream gene annotation analysis, especially when performing comparative genomics tasks. Platform: Online tool | visualization, gene ontology, gene, annotation, comparative genomics, histogram, directed acyclic graph, genomics, genome, ontology or annotation visualization, bio.tools |
is listed by: Gene Ontology Tools is listed by: Debian is listed by: bio.tools is related to: Gene Ontology has parent organization: BGI; Shenzhen; China |
Zhejiang University ; Chinese Academy of Sciences ; Danish Basic Research Foundation ; Ministry of Science and Technology 2002AA104250; Ministry of Science and Technology CNGI-04-15-7A; National Natural Science Foundation of China 30399120; National Natural Science Foundation of China 90208019; National Natural Science Foundation of China 30200163; National Natural Science Foundation of China 90403130 |
PMID:16845012 | Free for academic use | biotools:wego, nlx_149334 | https://bio.tools/wego | SCR_005827 | SciCrunch Registry | BGI WEGO - Web Gene Ontology Annotation Plotting, Web Gene Ontology Annotation Plot | 2026-10-10 12:43:38 | 398 | ||||
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RecountDB Resource Report Resource Website |
RecountDB (RRID:SCR_006117) | RecountDB | data or information resource, database | THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. Database for corrected read counts and genome mapping on NCBI's Short Read Archive. The corrected count was done using RECOUNT and the mapping with LAST. We also provide information of reference genome to which we aligned the short reads. We focus on transcriptomic data, specifically TSS-Seq and RNA-Seq. Because this is the type of data for which sequence count correction is most important. Hence we do not include the genomic reads. The current version contains 2,265 entries from 45 organisms, with read lengths from 17 to 100bp. Via a searchable and browseable interface users can obtain corrected data in formats useful for transcriptomic analysis. We provide the data grouped according to the genome, type of studies and submitter in TAB , PSL and BAM format. They contain the mapping position and annotation of reads observed and corrected counts. | read count, genome mapping, short read, genome, transcript, bio.tools |
is listed by: Debian is listed by: bio.tools is related to: NCBI Sequence Read Archive (SRA) is related to: RECOUNT is related to: LAST has parent organization: National Institute of Advanced Industrial Science and Technology |
Japanese Ministry of Education Culture Sports Science and Technology MEXT 221S002 | PMID:22139942 | THIS RESOURCE IS NO LONGER IN SERVICE | nlx_151592, biotools:recountdb | https://bio.tools/recountdb | SCR_006117 | SciCrunch Registry | 2026-10-10 12:43:45 | 0 | |||||
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SNPsandGO Resource Report Resource Website 50+ mentions |
SNPsandGO (RRID:SCR_005788) | SNPs&GO | analysis service resource, data analysis service, production service resource, 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 | SciCrunch Registry | SNPs and GO | 2026-10-10 12:44:09 | 61 | ||||||
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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 | SciCrunch Registry | 2026-10-10 12:43:39 | 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 | SciCrunch Registry | Protein-Chemical Structural Interactions, ProtChemSI: protein-chemical interaction database, ProtChemSI - the database of protein-chemical structural interactions | 2026-10-10 12:43:35 | 3 | |||||
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Yeast Search for Transcriptional Regulators And Consensus Tracking Resource Report Resource Website 100+ mentions |
Yeast Search for Transcriptional Regulators And Consensus Tracking (RRID:SCR_006076) | YEASTRACT | data or information resource, database | A curated repository of more than 206000 regulatory associations between transcription factors (TF) and target genes in Saccharomyces cerevisiae, based on more than 1300 bibliographic references. It also includes the description of 326 specific DNA binding sites shared among 113 characterized TFs. Further information about each Yeast gene has been extracted from the Saccharomyces Genome Database (SGD). For each gene the associated Gene Ontology (GO) terms and their hierarchy in GO was obtained from the GO consortium. Currently, YEASTRACT maintains a total of 7130 terms from GO. The nucleotide sequences of the promoter and coding regions for Yeast genes were obtained from Regulatory Sequence Analysis Tools (RSAT). All the information in YEASTRACT is updated regularly to match the latest data from SGD, GO consortium, RSA Tools and recent literature on yeast regulatory networks. YEASTRACT includes DISCOVERER, a set of tools that can be used to identify complex motifs found to be over-represented in the promoter regions of co-regulated genes. DISCOVERER is based on the MUSA algorithm. These algorithms take as input a list of genes and identify over-represented motifs, which can then be compared with transcription factor binding sites described in the YEASTRACT database. | yeast, gene, regulatory association, transcription factor, target gene, genomic, transcription regulation, transcription, web service, bio.tools, FASEB list |
is listed by: OMICtools is listed by: bio.tools is listed by: Debian is related to: SGD is related to: Gene Ontology is related to: Regulatory Sequence Analysis Tools |
Fundacao para a Ciencia e a Tecnologia contract Pest-OE/EQB/LA0023/2011_research line: Systems and Synthetic Biology; Fundacao para a Ciencia e a Tecnologia ERA-IB/0002/2010; Fundacao para a Ciencia e a Tecnologia PTDC/EIA-EIA/111239/2009; Fundacao para a Ciencia e a Tecnologia PTDC/EIA-CCO/118522/2010 |
PMID:24170807 PMID:20972212 PMID:18032429 PMID:16381908 |
Free | nif-0000-03652, OMICS_00547, biotools:yeastract | https://bio.tools/yeastract | SCR_006076 | SciCrunch Registry | 2026-10-10 12:43:35 | 130 | |||||
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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 | SciCrunch Registry | HotRegion: a database of predicted hot spot clusters, HotRegion: A database of cooperative hot spots | 2026-10-10 12:43:35 | 5 | |||||
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glycomedb Resource Report Resource Website 10+ mentions |
glycomedb (RRID:SCR_005717) | GlycomeDB | data or information resource, database | GlycomeDB is a database of all known carbohydrate structures. This was achieved by crosslinking several other databases of carbohydrate structures by using the GlycoCT XML language specification. We have analyzed all of the existing public databases and defined a sequence format based on XML (GlycoCT) capable of storing all structural information of carbohydrate sequences. We have implemented a library of parsers for the interpretation of the different encoding schemes for carbohydrates. With this library we have translated the carbohydrate sequences of all freely available databases (CFG , KEGG, GLYCOSCIENCES.de, BCSDB and Carbbank) to GlycoCT, and created a new database (GlycomeDB) containing all structures and annotations. During the process of data integration we found multiple inconsistencies in the existing databases which were corrected in collaboration with the responsible curators. With the new database, GlycomeDB, it is possible to get an overview of all carbohydrate structures in the different databases and to crosslink common structures in the different databases. Scientists are now able to search for a particular structure in the meta database and get information about the occurrence of this structure in the five carbohydrate structure databases. | carbohydrate structure, carbohydrate, bio.tools |
is listed by: Debian is listed by: bio.tools has parent organization: German Cancer Research Center |
European Union FP6 ; DFG |
PMID:21045056 PMID:19759275 PMID:18803830 |
nlx_149174, r3d100011527, biotools:glycomedb | https://bio.tools/glycomedb, https://doi.org/10.17616/R34M07 | SCR_005717 | SciCrunch Registry | GlycomeDB - A carbohydrate structure metadatabase | 2026-10-10 12:44:09 | 25 | |||||
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TropGENE DB Resource Report Resource Website 1+ mentions |
TropGENE DB (RRID:SCR_005716) | data or information resource, database | A database that manages genetic and genomic information about tropical crops studied by Cirad. The database is organised into crop specific modules. Each module includes data on genetic ressources (agro-morphological data, parentages, allelic diversity), information on molecular markers, genetics maps, result of QTL analyses, data from physical mapping, sequences, genes, as well as corresponding references. GENE DB interface has been designed to allow quick consultations as well as complex queries. Nine modules are presently on line. | banana, cocoa, coconut, coffee, cotton, oil palm, rice, rubber tree, sugarcane, bio.tools |
is listed by: bio.tools is listed by: Debian |
biotools:TropGeneDB, nif-0000-03593 | http://tropgenedb.cirad.fr/, https://bio.tools/TropGeneDB | SCR_005716 | SciCrunch Registry | TropGENE DB | 2026-10-10 12:43:41 | 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 | SciCrunch Registry | Database Of MacroMolecular INteractiOns | 2026-10-10 12:43:43 | 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 | SciCrunch Registry | Characterized Protein Database | 2026-10-10 12:44:10 | 0 | |||||
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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 | SciCrunch Registry | Virus-Host Network | 2026-10-10 12:43:44 | 7 | |||||
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ADGO Resource Report Resource Website 1+ mentions |
ADGO (RRID:SCR_006343) | ADGO | analysis service resource, data analysis service, production service resource, service resource | A web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. It provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. It screens redundant composite annotations when generating and prioritizing them. It also incorporates union and subtracted sets as well as intersection sets. Users can upload their gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets. | microarray, gene, annotation, bio.tools |
is listed by: OMICtools is listed by: Debian is listed by: bio.tools |
PMID:21624890 | Acknowledgement requested | OMICS_02229, biotools:adgo | https://bio.tools/adgo | SCR_006343 | SciCrunch Registry | 2026-10-10 12:43:40 | 3 | ||||||
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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 | SciCrunch Registry | HMM-TM: Prediction of Transmembrane Alpha-Helical Proteins | 2026-10-10 12:43:39 | 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 | SciCrunch Registry | PRED-LIPO: Prediction of Lipoprotein and Secretory Signal Peptides in Gram-positive Bacteria with Hidden Markov Models | 2026-10-10 12:43:36 | 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 | SciCrunch Registry | PRED-SIGNAL - Prediction of Signal Peptides in Archaea with Hidden Markov Models | 2026-10-10 12:43:45 | 14 | ||||
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SILVA Resource Report Resource Website 10000+ mentions |
SILVA (RRID:SCR_006423) | data or information resource, database | High quality ribosomal RNA databases providing comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya). Supplementary services include a rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches. Alignment tool, SINA, is available for download as well as available for use online. | ribosomal rna, gene sequence, gene, sequence, alignment, taxonomy, 16s, 18s, 23s, 28s, phylogeny, probe, primer, alignment service, fish, arb, ribocon, geoblast, bio.tools |
is listed by: OMICtools is listed by: bio.tools is listed by: Debian is affiliated with: RNAcentral is related to: ARB project is related to: SINA is related to: European ribosomal RNA database has parent organization: German Collection of Microorganisms and Cell Cultures |
German Collection of Microorganisms and Cell Cultures | PMID:23193283 PMID:24293649 PMID:17947321 |
biotools:silva, OMICS_01514, nif-0000-03464, r3d100011323, rid_000103 | https://bio.tools/silva, https://doi.org/10.17616/R3FP60 | SCR_006423 | SciCrunch Registry | SILVA rRNA database, SILVA - high quality ribosomal RNA databases | 2026-10-10 12:44:12 | 15971 | ||||||
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ViralZone Resource Report Resource Website 100+ mentions |
ViralZone (RRID:SCR_006563) | ViralZone | data or information resource, database | ViralZone is a SIB Swiss Institute of Bioinformatics web-resource for all viral genus and families, providing general molecular and epidemiological information, along with virion and genome figures. Each virus or family page gives an easy access to UniProtKB/Swiss-Prot viral protein entries. ViralZone project is handled by the virus program of SwissProt group. Proteins popups were developed in collaboration with Prof. Christian von Mering and Andrea Franceschini, Bioinformatics Group , Institute of Molecular Life Sciences, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland, funded in part by the SIB Swiss Institute of bioinformatics. All pictures in ViralZone are copyright of the SIB Swiss Institute of Bioinformatics. | dna virus, rna virus, virus, dna, rna, genomic, proteomic, sequence, reference strain, image, virion, retro-transcribing virus, genome, bibliographic, bio.tools |
is listed by: Debian is listed by: bio.tools has parent organization: SIB Swiss Institute of Bioinformatics |
Swiss Institute of Bioinformatics | PMID:20947564 | biotools:viralzone, r3d100013314, nlx_144372 | https://bio.tools/viralzone, https://doi.org/10.17616/R31NJMRM | http://www.expasy.org/viralzone/ | SCR_006563 | SciCrunch Registry | Viral Zone | 2026-10-10 12:43:38 | 148 | ||||
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neuroelectro Resource Report Resource Website 10+ mentions |
neuroelectro (RRID:SCR_006274) | NeuroElectro | data or information resource, database | A database of elecrophysiological properties text-mined from the biomedical literature as a function of neuron type. Specifically, NeuroElectro seeks to extract information about the electrophysiological properties (e.g. resting membrane potentials and membrane time constants) of diverse neuron types from the existing literature and place it into a centralized database. There are 252 neurons currently available, with the naming convention established in NeuroLex. | electrophysiology, text mining, cellular neurophysiology, neuron, neuron electrophysiology, bio.tools |
is used by: NIF Data Federation is listed by: Debian is listed by: bio.tools is related to: NeuroLex has parent organization: Carnegie Mellon University; Pennsylvania; USA |
Open unspecified license | nlx_151885, BioTools:neuroelectro, biotools:neuroelectro, r3d100011798 | https://bio.tools/neuroelectro, https://bio.tools/neuroelectro, https://bio.tools/neuroelectro, https://doi.org/10.17616/R3BP7F | SCR_006274 | SciCrunch Registry | Neuro Electro, NeuroElectro: organizing information on cellular neurophysiology | 2026-10-10 12:43:46 | 27 |
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