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| Resource Name | Proper Citation | Abbreviations | Resource Type |
Description |
Keywords | Resource Relationships | |||||||||||||
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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, analysis 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 | 2026-02-17 10:01:01 | 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, analysis 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 | 2026-02-17 10:00:47 | 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, analysis 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 | 2026-02-17 10:01:01 | 14 | ||||
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PRED-CLASS Resource Report Resource Website |
PRED-CLASS (RRID:SCR_006216) | PRED-CLASS | data analysis service, production service resource, analysis 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 | 2026-02-17 10:01:05 | 0 | ||||||
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CoPreTHi Resource Report Resource Website |
CoPreTHi (RRID:SCR_006217) | CoPreTHi | data analysis service, production service resource, analysis 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 | 2026-02-17 10:00:53 | 0 | ||||||
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SNPsandGO Resource Report Resource Website 50+ mentions |
SNPsandGO (RRID:SCR_005788) | SNPs&GO | data analysis service, production service resource, analysis 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 | SNPs and GO | 2026-02-17 10:00:39 | 58 | ||||||
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ConBBPRED Resource Report Resource Website 1+ mentions |
ConBBPRED (RRID:SCR_006194) | ConBBPRED | data analysis service, production service resource, analysis 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 | 2026-02-17 10:00:47 | 3 | ||||||
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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, analysis service resource, 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 | 2026-02-17 10:00:57 | 1 | ||||||
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GORetriever Resource Report Resource Website 10+ mentions |
GORetriever (RRID:SCR_005633) | GORetriever | data analysis service, production service resource, analysis service resource, 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 | 2026-02-17 10:00:47 | 13 | |||||
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ProteInOn Resource Report Resource Website 1+ mentions |
ProteInOn (RRID:SCR_005740) | data analysis service, production service resource, analysis 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 | 2026-02-17 10:00:38 | 2 | ||||||||
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Protein Knots Resource Report Resource Website 1+ mentions |
Protein Knots (RRID:SCR_008353) | data analysis service, production service resource, analysis service resource, service resource | The knot server allows the user to check PDB entries or uploaded structures for knots and to visualize them. The size of a knot is determined by deleting amino acids from both ends. This procedure is, however, not perfect and the resulting size should only be treated as a guideline. Mathematically, knots are only well defined in closed (circular) loops. However, both the N- and C-termini of open proteins are typically located close to the surface of the protein and can be connected unambiguously: We reduce the protein to its backbone and draw two lines outward starting at the termini in the direction of the connection line between the center of mass of the backbone and the respective ends. The two lines are joined by a big loop, and the structure is topologically classified by the determination of its Alexander polynomial. To determine an estimate for the size of the knotted core, we successively delete amino acids from the N-terminus until the protein becomes unknotted. The procedure is repeated at the C-terminus starting with the last N-terminal deletion structure that contained the original knot. For each deletion, the outward-pointing line through the new termini is parallel to the respective lines computed for the full structure. Unfortunately, the size of a knot is not always precisely determined by this procedure, so reported sizes should therefore only be treated as approximate. Sponsors: Knots is funded by MIT. | amino acid, backbone, core, c-terminus, knot, n-terminus, protein, protein databank, protein folding, size, structural model, structure | has parent organization: Massachusetts Institute of Technology; Massachusetts; USA; | nif-0000-25217 | SCR_008353 | Knots | 2026-02-17 10:01:07 | 3 | |||||||||
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NECTAR Resource Report Resource Website 10+ mentions |
NECTAR (RRID:SCR_007757) | NECTAR | data or information resource, database, service resource | A database and web application to annotate disease-related and functionally important amino acids in human proteins., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025. | annotation, protein, disease, amino acid, gene, function | is listed by: OMICtools | PMID:24297257 | THIS RESOURCE IS NO LONGER IN SERVICE | OMICS_00276 | SCR_007757 | Non-synonymous Enriched Coding muTation Archive | 2026-02-17 10:01:16 | 36 | ||||||
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ProbeExplorer Resource Report Resource Website |
ProbeExplorer (RRID:SCR_007116) | ProbeExplorer | data analysis service, production service resource, analysis 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 | 2026-02-17 10:01:01 | 0 | |||||||
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SVM based method for predicting beta hairpin structures in proteins Resource Report Resource Website 1+ mentions |
SVM based method for predicting beta hairpin structures in proteins (RRID:SCR_008349) | data analysis service, production service resource, analysis service resource, service resource | Bhairpred server is based on machine learning technique SVM using single sequence information, evolutionary profile, predicted and observed secondary structure (as obtained using Psipred and DSSP), predicted and observed accessibility values (as obtainned from Netasa and DSSP). The methods were trained and tested on dataset of 2880 proteins and their performance was evaluated on dataset of 534 proteins used by Thornton (PNAS, 2002). Best prediction results were obtained with hybrid approach that combined prediction results from evolutionary profile, predicted secondary structure and accessibility. | evolutionary, information, protein, protein structure prediction, secondary, sequence, single, svm, technique, bio.tools |
is listed by: bio.tools is listed by: Debian has parent organization: Institute of Microbial Technology; Chandigarh; India |
Institute of Microbial Technology | nif-0000-25213, biotools:bhairpred | https://bio.tools/bhairpred | SCR_008349 | BhairPred | 2026-02-17 10:01:13 | 2 | |||||||
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FunSpec Resource Report Resource Website 50+ mentions |
FunSpec (RRID:SCR_006952) | FunSpec | data analysis service, production service resource, analysis 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 | 2026-02-17 10:01:11 | 87 | ||||||
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GraphRBF Resource Report Resource Website |
GraphRBF (RRID:SCR_025652) | simulation software, source code, software application, software resource | Software tool as protein-protein/nucleic acid interaction site prediction model built by enhanced graph neural networks and prioritized radial basis function neural networks. Protein-protein and protein-nucleic acid binding site prediction via interpretable hierarchical geometric deep learning. | protein, nucleic acids, interactions, python, binding site prediction, | Free, Available for download, Freely available | SCR_025652 | 2026-02-17 10:05:06 | 0 | |||||||||||
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TBLASTN Resource Report Resource Website 5000+ mentions |
TBLASTN (RRID:SCR_011822) | TBLASTN | data analysis service, production service resource, analysis service resource, service resource | Tool to search translated nucleotide databases using a protein query. | protein |
is listed by: OMICtools is listed by: SoftCite has parent organization: NCBI |
OMICS_00999 | SCR_011822 | Translated BLAST: tblastn | 2026-02-17 10:01:57 | 5523 | ||||||||
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mCSM Resource Report Resource Website 50+ mentions |
mCSM (RRID:SCR_010776) | mCSM | data analysis service, production service resource, analysis service resource, service resource | Data analysis service to the study of missense mutations which relies on graph-based signatures. | mutation, protein, protein stability, protein-protein, protein-dna, data set |
is listed by: OMICtools has parent organization: University of Cambridge; Cambridge; United Kingdom |
PMID:24281696 | OMICS_00133 | SCR_010776 | mCSM: predicting the effect of mutations in proteins using graph-based signatures | 2026-02-17 10:01:52 | 78 | |||||||
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BaCelLo Resource Report Resource Website 10+ mentions |
BaCelLo (RRID:SCR_011965) | BaCelLo | data analysis service, production service resource, analysis service resource, service resource | A predictor for the subcellular localization of proteins in eukaryotes that is based on a decision tree of several support vector machines (SVMs). It classifies up to four localizations for Fungi and Metazoan proteins and five localizations for Plant ones. BaCelLo's predictions are balanced among different classes and all the localizations are considered as equiprobable. | protein, data set, proteome |
is listed by: OMICtools is listed by: SoftCite has parent organization: University of Bologna; Bologna; Italy |
PMID:16873501 | OMICS_01616 | SCR_011965 | 2026-02-17 10:01:59 | 45 | ||||||||
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HSLPred Resource Report Resource Website |
HSLPred (RRID:SCR_011972) | HSLPred | data analysis service, production service resource, analysis service resource, service resource | A support vector machine (SVM)-based method for the prediction of 4 major subcellular localization (cytoplasm, mitochondrial, nuclear and plasma membrane) of human proteins using various features such as i) amino acid composition, ii) dipeptide composition and iii) evolutionary information of proteins. | subcellular localization, protein, support vector machine, bio.tools |
is listed by: OMICtools is listed by: Debian is listed by: bio.tools has parent organization: Institute of Microbial Technology; Chandigarh; India |
PMID:15647269 | Acknowledgement requested | biotools:hslpred, OMICS_01622 | https://bio.tools/hslpred | SCR_011972 | HSLPred - A SVM-based Method for Subcellular Localization of Human Proteins | 2026-02-17 10:02:06 | 0 |
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