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{ "count": 3901, "next": "?page=2", "previous": null, "list": [ { "name": "lorikeet", "description": "Tools for M. tuberculosis DNA fingerprinting (spoligotyping)", "homepage": "https://github.com/AbeelLab/lorikeet", "biotoolsID": "lorikeet", "biotoolsCURIE": "biotools:lorikeet", "version": [ "20" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2403", "term": "Sequence analysis" }, { "uri": "http://edamontology.org/operation_3196", "term": "Genotyping" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Bioinformatics portal" ], "topic": [ { "uri": "http://edamontology.org/topic_0625", "term": "Genotype and phenotype" } ], "operatingSystem": [], "language": [ "Java" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "community": null, "owner": "rlibouban", "additionDate": "2024-03-12T10:56:44.977314Z", "lastUpdate": "2024-03-12T10:56:44.980036Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "TPpred 2.0", "description": "Mitochondrial targeting peptide prediction.", "homepage": "https://tppred2.biocomp.unibo.it", "biotoolsID": "tppred_2.0", "biotoolsCURIE": "biotools:tppred_2.0", "version": [ "2.0" ], "otherID": [], "relation": [ { "biotoolsID": "tppred_1.0", "type": "isNewVersionOf" }, { "biotoolsID": "tppred_3.0", "type": "hasNewVersion" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2489", "term": "Protein subcellular localisation prediction" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" }, { "uri": "http://edamontology.org/operation_0253", "term": "Sequence feature detection" }, { "uri": "http://edamontology.org/operation_0422", "term": "Protein cleavage site prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2974", "term": "Protein sequence (raw)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0896", "term": "Protein report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": "Prediction", "cmd": null } ], "toolType": [ "Command-line tool", "Web application" ], "topic": [ { "uri": "http://edamontology.org/topic_0160", "term": "Sequence sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" }, { "uri": "http://edamontology.org/topic_0140", "term": "Protein targeting and localisation" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [ "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Italy" ], "elixirCommunity": [], "link": [], "download": [ { "url": "http://biocomp.unibo.it/savojard/tppred2.tar.gz", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "https://tppred3.biocomp.unibo.it/tppred3/default/help", "type": [ "General" ], "note": null }, { "url": "https://tppred2.biocomp.unibo.it/tppred2/default/software", "type": [ "Command-line options" ], "note": "Installation instructions" } ], "publication": [ { "doi": "10.1093/bioinformatics/btu411", "pmid": "24974200", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": null }, { "doi": "10.1093/bioinformatics/btt089", "pmid": "23428638", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields", "abstract": "Motivation: Targeting peptides are the most important signal controlling the import of nuclear encoded proteins into mitochondria and plastids. In the lack of experimental information, their prediction is an essential step when proteomes are annotated for inferring both the localization and the sequence of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting peptides based on Grammatical-Restrained Hidden Conditional Random Fields. TPpred is trained on a non-redundant dataset of proteins where the presence of a target peptide was experimentally validated, comprising 297 sequences. When tested on the 297 positive and some other 8010 negative examples, TPpred outperformed available methods in both accuracy and Matthews correlation index (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%), TPpred is, therefore, well suited for large-scale analyses at the proteome level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis thaliana and yeast proteomes contain targeting peptides and are, therefore, likely to be localized in mitochondria and plastids. TPpred predictions correlate to a good extent with the experimental annotation of the subcellular localization, when available. TPpred was also trained and tested to predict the cleavage site of the organelle-targeting peptide: on this task, the average error of TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively. This value is lower than the error reported by other methods currently available. © 2013 The Author.", "date": "2013-04-15T00:00:00Z", "citationCount": 14, "authors": [ { "name": "Indio V." }, { "name": "Martelli P.L." }, { "name": "Savojardo C." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": null, "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null }, { "name": "Rita Casadio", "email": "rita.casadio@unibo.it", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Piero Fariselli", "email": "piero.fariselli@unito.it", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": null, "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2016-01-22T15:53:12Z", "lastUpdate": "2024-03-11T16:57:44.699185Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-ITA-BOLOGNA", "savo" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "TPpred 3.0", "description": "Organelle-targeting peptide detection and cleavage-site prediction.", "homepage": "http://tppred3.biocomp.unibo.it", "biotoolsID": "tppred_3.0", "biotoolsCURIE": "biotools:tppred_3.0", "version": [ "3" ], "otherID": [], "relation": [ { "biotoolsID": "tppred_2.0", "type": "isNewVersionOf" }, { "biotoolsID": "tppred_1.0", "type": "isNewVersionOf" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2489", "term": "Protein subcellular localisation prediction" }, { "uri": "http://edamontology.org/operation_0422", "term": "Protein cleavage site prediction" }, { "uri": "http://edamontology.org/operation_3092", "term": "Protein feature detection" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2974", "term": "Protein sequence (raw)" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0896", "term": "Protein report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] } ], "note": "Predictio Protein sequence in FASTA format", "cmd": null } ], "toolType": [ "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_3510", "term": "Protein sites, features and motifs" }, { "uri": "http://edamontology.org/topic_0140", "term": "Protein targeting and localisation" }, { "uri": "http://edamontology.org/topic_0154", "term": "Small molecules" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": "GPL-3.0", "collectionID": [ "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Italy" ], "elixirCommunity": [], "link": [], "download": [ { "url": "https://github.com/BolognaBiocomp/tppred3", "type": "Source code", "note": null, "version": null }, { "url": "https://hub.docker.com/r/bolognabiocomp/tppred3", "type": "Container file", "note": null, "version": null } ], "documentation": [ { "url": "https://tppred3.biocomp.unibo.it/tppred3/default/help", "type": [ "General" ], "note": null }, { "url": "https://github.com/BolognaBiocomp/tppred3", "type": [ "Command-line options" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btv367", "pmid": "26079349", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins", "abstract": "Motivation: Molecular recognition of N-terminal targeting peptides is the most common mechanism controlling the import of nuclear-encoded proteins into mitochondria and chloroplasts. When experimental information is lacking, computational methods can annotate targeting peptides, and determine their cleavage sites for characterizing protein localization, function, and mature protein sequences. The problem of discriminating mitochondrial from chloroplastic propeptides is particularly relevant when annotating proteomes of photosynthetic Eukaryotes, endowed with both types of sequences. Results: Here, we introduce TPpred3, a computational method that given any Eukaryotic protein sequence performs three different tasks: (i) the detection of targeting peptides; (ii) their classification as mitochondrial or chloroplastic and (iii) the precise localization of the cleavage sites in an organelle-specific framework. Our implementation is based on our TPpred previously introduced. Here, we integrate a new N-to-1 Extreme Learning Machine specifically designed for the classification task (ii). For the last task, we introduce an organelle-specific Support Vector Machine that exploits sequence motifs retrieved with an extensive motif-discovery analysis of a large set of mitochondrial and chloroplastic proteins. We show that TPpred3 outperforms the state-of-the-art methods in all the three tasks.", "date": "2015-03-25T00:00:00Z", "citationCount": 38, "authors": [ { "name": "Savojardo C." }, { "name": "Martelli P.L." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } }, { "doi": "10.1093/bioinformatics/btu411", "pmid": "24974200", "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "TPpred2: improving the prediction of mitochondrial targeting peptide cleavage sites by exploiting sequence motifs", "abstract": "CONTACT: gigi@biocomp.unibo.it SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. SUMMARY: Targeting peptides are N-terminal sorting signals in proteins that promote their translocation to mitochondria through the interaction with different protein machineries. We recently developed TPpred, a machine learning-based method scoring among the best ones available to predict the presence of a targeting peptide into a protein sequence and its cleavage site. Here we introduce TPpred2 that improves TPpred performances in the task of identifying the cleavage site of the targeting peptides. TPpred2 is now available as a web interface and as a stand-alone version for users who can freely download and adopt it for processing large volumes of sequences. Availability and implementaion: TPpred2 is available both as web server and stand-alone version at http://tppred2.biocomp.unibo.it.", "date": "2014-10-15T00:00:00Z", "citationCount": 31, "authors": [ { "name": "Savojardo C." }, { "name": "Martelli P.L." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics (Oxford, England)" } }, { "doi": "10.1093/bioinformatics/btt089", "pmid": "23428638", "pmcid": null, "type": [ "Other" ], "version": null, "note": null, "metadata": { "title": "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields", "abstract": "Motivation: Targeting peptides are the most important signal controlling the import of nuclear encoded proteins into mitochondria and plastids. In the lack of experimental information, their prediction is an essential step when proteomes are annotated for inferring both the localization and the sequence of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting peptides based on Grammatical-Restrained Hidden Conditional Random Fields. TPpred is trained on a non-redundant dataset of proteins where the presence of a target peptide was experimentally validated, comprising 297 sequences. When tested on the 297 positive and some other 8010 negative examples, TPpred outperformed available methods in both accuracy and Matthews correlation index (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%), TPpred is, therefore, well suited for large-scale analyses at the proteome level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis thaliana and yeast proteomes contain targeting peptides and are, therefore, likely to be localized in mitochondria and plastids. TPpred predictions correlate to a good extent with the experimental annotation of the subcellular localization, when available. TPpred was also trained and tested to predict the cleavage site of the organelle-targeting peptide: on this task, the average error of TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively. This value is lower than the error reported by other methods currently available. © 2013 The Author.", "date": "2013-04-15T00:00:00Z", "citationCount": 14, "authors": [ { "name": "Indio V." }, { "name": "Martelli P.L." }, { "name": "Savojardo C." }, { "name": "Fariselli P." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } }, { "doi": "10.1007/978-1-0716-1262-0_28", "pmid": "34118055", "pmcid": null, "type": [ "Review", "Benchmarking study" ], "version": null, "note": null, "metadata": { "title": "Computer-Aided Prediction of Protein Mitochondrial Localization", "abstract": "Protein sequences, directly translated from genomic data, need functional and structural annotation. Together with molecular function and biological process, subcellular localization is an important feature necessary for understanding the protein role and the compartment where the mature protein is active. In the case of mitochondrial proteins, their precursor sequences translated by the ribosome machinery include specific patterns from which it is possible not only to recognize their final destination within the organelle but also which of the mitochondrial subcompartments the protein is intended for. Four compartments are routinely discriminated, including the inner and the outer membranes, the intermembrane space, and the matrix. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequence and to discriminate their final destination in the organelle. We benchmark two of our methods on the general task of recognizing human mitochondrial proteins endowed with an experimentally characterized targeting peptide (TPpred3) and predicting which submitochondrial compartment is the final destination (DeepMito). We describe how to adopt our web servers in order to discriminate which human proteins are endowed with mitochondrial targeting peptides, the position of cleavage sites, and which submitochondrial compartment are intended for. By this, we add some other 1788 human proteins to the 450 ones already manually annotated in UniProt with a mitochondrial targeting peptide, providing for each of them also the characterization of the suborganellar localization.", "date": "2021-01-01T00:00:00Z", "citationCount": 2, "authors": [ { "name": "Martelli P.L." }, { "name": "Savojardo C." }, { "name": "Fariselli P." }, { "name": "Tartari G." }, { "name": "Casadio R." } ], "journal": "Methods in Molecular Biology" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": "http://biocomp.unibo.it/savojard/", "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer" ], "note": null } ], "community": null, "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2016-01-22T15:50:02Z", "lastUpdate": "2024-03-11T16:48:08.106769Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-ITA-BOLOGNA", "savo" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "BCov", "description": "Prediction of β-sheet topology.", "homepage": "http://biocomp.unibo.it/savojard/bcov/index.html", "biotoolsID": "bcov", "biotoolsCURIE": "biotools:bcov", "version": [ "1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0476", "term": "Ab initio structure prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1460", "term": "Protein structure" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] }, { "data": { "uri": "http://edamontology.org/data_1384", "term": "Sequence alignment (protein)" }, "format": [ { "uri": "http://edamontology.org/format_1984", "term": "FASTA-aln" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0906", "term": "Protein interaction data" }, "format": [ { "uri": "http://edamontology.org/format_2330", "term": "Textual format" } ] } ], "note": "Prediction Predicted or real secondary structure of the input protein Protein residue contact map", "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0082", "term": "Structure prediction" } ], "operatingSystem": [ "Linux", "Mac" ], "language": [ "C" ], "license": "GPL-3.0", "collectionID": [ "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [ "Italy" ], "elixirCommunity": [ "3D-BioInfo" ], "link": [], "download": [ { "url": "http://biocomp.unibo.it/savojard/bcov/index.html", "type": "Source code", "note": null, "version": null } ], "documentation": [ { "url": "http://biocomp.unibo.it/savojard/bcov/index.html", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btt555", "pmid": "24064422", "pmcid": "PMC5994943", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "BCov: A method for predicting β-sheet topology using sparse inverse covariance estimation and integer programming", "abstract": "Motivation: Prediction of protein residue contacts, even at the coarsegrain level, can help in finding solutions to the protein structure prediction problem. Unlike α-helices that are locally stabilized, β-sheets result from pairwise hydrogen bonding of two or more disjoint regions of the protein backbone. The problem of predicting contacts among β-strands in proteins has been addressed by several supervised computational approaches. Recently, prediction of residue contacts based on correlated mutations has been greatly improved and finally allows the prediction of 3D structures of the proteins. Results: In this article, we describe BCov, which is the first unsuper vised method to predict the β-sheet topology starting from the protein sequence and its secondary structure. BCov takes advantage of the sparse inverse covariance estimation to define β-strand partner scores. Then an optimization based on integer programming is carried out to predict the β-sheet connectivity. When tested on the prediction of β-strand pairing, BCov scores with average values of Matthews Correlation Coefficient (MCC) and F1 equal to 0.56 and 0.61, respectively, on a non-redundant dataset of 916 protein chains known with atomic resolution. Our approach well compares with the state-of-the art methods trained so far for this specific task.", "date": "2013-12-15T00:00:00Z", "citationCount": 18, "authors": [ { "name": "Savojardo C." }, { "name": "Fariselli P." }, { "name": "Martelli P.L." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": "http://biocomp.unibo.it/savojard", "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null } ], "community": null, "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2016-01-22T15:51:37Z", "lastUpdate": "2024-03-11T16:47:48.087425Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-ITA-BOLOGNA", "savo" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "legsta", "description": "Performs in silico Legionella pneumophila sequence based typing", "homepage": "https://github.com/tseemann/legsta", "biotoolsID": "legsta", "biotoolsCURIE": "biotools:legsta", "version": [ "0.5.1", "0.3.7", "0.3.2", "0.2" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2403", "term": "Sequence analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Bioinformatics portal", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3305", "term": "Public health and epidemiology" } ], "operatingSystem": [], "language": [], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "community": null, "owner": "rlibouban", "additionDate": "2024-03-11T16:40:14.136794Z", "lastUpdate": "2024-03-11T16:40:14.139298Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "CoCoNat", "description": "CoCoNat is a novel deep-learning based method for predicting coiled-coil regions, register annotation and oligomenrization state. CoCoNat adopts a sequence encoding based on two state-of-the-art protein language models and a deep-learning architetcure to perform prediction.", "homepage": "https://coconat.biocomp.unibo.it", "biotoolsID": "coconat", "biotoolsCURIE": "biotools:coconat", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0268", "term": "Protein super-secondary structure prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2976", "term": "Protein sequence" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0896", "term": "Protein report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" }, { "uri": "http://edamontology.org/format_3464", "term": "JSON" }, { "uri": "http://edamontology.org/format_3475", "term": "TSV" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_2814", "term": "Protein structure analysis" }, { "uri": "http://edamontology.org/topic_0736", "term": "Protein folds and structural domains" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [ "Bologna Biocomputing Group" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Italy" ], "elixirCommunity": [], "link": [], "download": [ { "url": "https://github.com/BolognaBiocomp/coconat", "type": "Source code", "note": null, "version": "1.0" } ], "documentation": [ { "url": "https://github.com/BolognaBiocomp/coconat", "type": [ "Command-line options" ], "note": null }, { "url": "https://coconat.biocomp.unibo.it/help/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.1093/bioinformatics/btad495", "pmid": "37540220", "pmcid": "PMC10425188", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "CoCoNat: a novel method based on deep learning for coiled-coil prediction", "abstract": "Motivation: Coiled-coil domains (CCD) are widespread in all organisms and perform several crucial functions. Given their relevance, the computational detection of CCD is very important for protein functional annotation. State-of-the-art prediction methods include the precise identification of CCD boundaries, the annotation of the typical heptad repeat pattern along the coiled-coil helices as well as the prediction of the oligomerization state. Results: In this article, we describe CoCoNat, a novel method for predicting coiled-coil helix boundaries, residue-level register annotation, and oligomerization state. Our method encodes sequences with the combination of two state-of-the-art protein language models and implements a three-step deep learning procedure concatenated with a Grammatical-Restrained Hidden Conditional Random Field for CCD identification and refinement. A final neural network predicts the oligomerization state. When tested on a blind test set routinely adopted, CoCoNat obtains a performance superior to the current state-of-the-art both for residue-level and segment-level CCD. CoCoNat significantly outperforms the most recent state-of-the-art methods on register annotation and prediction of oligomerization states.", "date": "2023-08-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "Madeo G." }, { "name": "Savojardo C." }, { "name": "Manfredi M." }, { "name": "Martelli P.L." }, { "name": "Casadio R." } ], "journal": "Bioinformatics" } }, { "doi": "10.21769/BioProtoc.4935", "pmid": "38405078", "pmcid": "PMC10883893", "type": [ "Usage" ], "version": null, "note": null, "metadata": { "title": "CoCoNat: A Deep Learning–Based Tool for the Prediction of Coiled-coil Domains in Protein Sequences", "abstract": "Coiled-coil domains (CCDs) are structural motifs observed in proteins in all organisms that perform several crucial functions. The computational identification of CCD segments over a protein sequence is of great importance for its functional characterization. This task can essentially be divided into three separate steps: the detection of segment boundaries, the annotation of the heptad repeat pattern along the segment, and the classification of its oligomerization state. Several methods have been proposed over the years addressing one or more of these predictive steps. In this protocol, we illustrate how to make use of CoCoNat, a novel approach based on protein language models, to characterize CCDs. CoCoNat is, at its release (August 2023), the state of the art for CCD detection. The web server allows users to submit input protein sequences and visualize the predicted domains after a few minutes. Optionally, precomputed segments can be provided to the model, which will predict the oligomerization state for each of them. CoCoNat can be easily integrated into biological pipelines by downloading the standalone version, which provides a single executable script to produce the output.", "date": "2024-02-20T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Manfredi M." }, { "name": "Savojardo C." }, { "name": "Martelli P.L." }, { "name": "Casadio R." } ], "journal": "Bio-protocol" } } ], "credit": [ { "name": "ELIXIR-ITA-BOLOGNA", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null }, { "name": "Castrense Savojardo", "email": "castrense.savojardo2@unibo.it", "url": null, "orcidid": "https://orcid.org/0000-0002-7359-0633", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null }, { "name": "Pier Luigi Martelli", "email": "pierluigi.martelli@unibo.it", "url": null, "orcidid": "https://orcid.org/0000-0002-0274-5669", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "community": null, "owner": "ELIXIR-ITA-BOLOGNA", "additionDate": "2024-01-28T18:03:20.705547Z", "lastUpdate": "2024-03-11T16:30:11.896748Z", "editPermission": { "type": "group", "authors": [ "ELIXIR-ITA-BOLOGNA", "savo" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "krocus", "description": "Predict MLST directly from uncorrected long reads", "homepage": "https://github.com/quadram-institute-bioscience/krocus", "biotoolsID": "krocus", "biotoolsCURIE": "biotools:krocus", "version": [ "1.0.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3840", "term": "Multilocus sequence typing" }, { "uri": "http://edamontology.org/operation_3472", "term": "k-mer counting" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Bioinformatics portal", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3305", "term": "Public health and epidemiology" } ], "operatingSystem": [], "language": [ "Python", "Shell" ], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "community": null, "owner": "rlibouban", "additionDate": "2024-03-11T15:55:14.633015Z", "lastUpdate": "2024-03-11T15:55:14.636043Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "PlasFlow", "description": "PlasFlow is a set of scripts used for prediction of plasmid sequences in metagenomic contigs.", "homepage": "https://github.com/smaegol/PlasFlow", "biotoolsID": "plasflow", "biotoolsCURIE": "biotools:plasflow", "version": [ "1.1" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2403", "term": "Sequence analysis" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Bioinformatics portal", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3174", "term": "Metagenomics" } ], "operatingSystem": [], "language": [], "license": "GPL-3.0", "collectionID": [], "maturity": "Mature", "cost": null, "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "community": null, "owner": "rlibouban", "additionDate": "2024-03-11T13:25:13.344697Z", "lastUpdate": "2024-03-11T13:25:32.934504Z", "editPermission": { "type": "public", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "IPEV", "description": "Software tool to identify prokaryotic and eukaryotic virus-derived sequences in virome using deep learning. Used to calculate a set of scores that reflect the probability that input sequence fragments are prokaryotic and eukaryotic viral sequences.", "homepage": "https://github.com/basehc/IPEV", "biotoolsID": "IPEV", "biotoolsCURIE": "biotools:IPEV", "version": [ "V4" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3460", "term": "Taxonomic classification" } ], "input": [], "output": [], "note": null, "cmd": "python run.py example.fasta" } ], "toolType": [ "Bioinformatics portal" ], "topic": [], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://scicrunch.org/resolver/SCR_023702", "type": [ "Other" ], "note": null } ], "download": [ { "url": "https://github.com/basehc/IPEV", "type": "Downloads page", "note": null, "version": "V4" } ], "documentation": [ { "url": "https://github.com/basehc/IPEV/blob/main/README.md", "type": [ "User manual" ], "note": null } ], "publication": [], "credit": [], "community": null, "owner": "yinhengchuang", "additionDate": "2024-03-09T05:32:55.552498Z", "lastUpdate": "2024-03-09T06:02:32.054907Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "NeuroDevSim", "description": "NeuroDevSim is a Neural Development Simulator for Linux of MacOS environments. It is a parallel and phenomenological computational framework to grow large numbers of neuronal morphologies (and resultant microcircuits) simultaneously according to growth-rules expressed in terms of interactions with the environment and internal variables:.", "homepage": "https://github.com/CNS-OIST/NeuroDevSim", "biotoolsID": "neurodevsim", "biotoolsCURIE": "biotools:neurodevsim", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3450", "term": "Neurite measurement" }, { "uri": "http://edamontology.org/operation_2426", "term": "Modelling and simulation" }, { "uri": "http://edamontology.org/operation_3431", "term": "Deposition" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Library" ], "topic": [ { "uri": "http://edamontology.org/topic_0749", "term": "Transcription factors and regulatory sites" }, { "uri": "http://edamontology.org/topic_3334", "term": "Neurology" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [ "Python" ], "license": "GPL-3.0", "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://cns-oist.github.io/NeuroDevSim/index.html", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.3389/FNINF.2023.1212384", "pmid": "37547492", "pmcid": "PMC10400717", "type": [], "version": null, "note": null, "metadata": { "title": "Efficient simulation of neural development using shared memory parallelization", "abstract": "The Neural Development Simulator, NeuroDevSim, is a Python module that simulates the most important aspects of brain development: morphological growth, migration, and pruning. It uses an agent-based modeling approach inherited from the NeuroMaC software. Each cycle has agents called fronts execute model-specific code. In the case of a growing dendritic or axonal front, this will be a choice between extension, branching, or growth termination. Somatic fronts can migrate to new positions and any front can be retracted to prune parts of neurons. Collision detection prevents new or migrating fronts from overlapping with existing ones. NeuroDevSim is a multi-core program that uses an innovative shared memory approach to achieve parallel processing without messaging. We demonstrate linear strong parallel scaling up to 96 cores for large models and have run these successfully on 128 cores. Most of the shared memory parallelism is achieved without memory locking. Instead, cores have only write privileges to private sections of arrays, while being able to read the entire shared array. Memory conflicts are avoided by a coding rule that allows only active fronts to use methods that need writing access. The exception is collision detection, which is needed to avoid the growth of physically overlapping structures. For collision detection, a memory-locking mechanism was necessary to control access to grid points that register the location of nearby fronts. A custom approach using a serialized lock broker was able to manage both read and write locking. NeuroDevSim allows easy modeling of most aspects of neural development for models simulating a few complex or thousands of simple neurons or a mixture of both. Code available at: https://github.com/CNS-OIST/NeuroDevSim.", "date": "2023-01-01T00:00:00Z", "citationCount": 1, "authors": [ { "name": "De Schutter E." } ], "journal": "Frontiers in Neuroinformatics" } } ], "credit": [ { "name": "Erik De Schutter", "email": "erik@oist.jp", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "community": null, "owner": "Pub2Tools", "additionDate": "2024-03-08T09:37:00.265738Z", "lastUpdate": "2024-03-08T09:37:00.268356Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" } ] }