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{
    "count": 851,
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    "list": [
        {
            "name": "pangolin",
            "description": "Phylogenetic Assignment of Named Global Outbreak LINeages - software package for assigning SARS-CoV-2 genome sequences to global lineages",
            "homepage": "https://cov-lineages.org/resources/pangolin.html",
            "biotoolsID": "pangolin_cov-lineages",
            "biotoolsCURIE": "biotools:pangolin_cov-lineages",
            "version": [
                "v4.0 -  v4.3.1"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0499",
                            "term": "Tree-based sequence alignment"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3225",
                            "term": "Variant classification"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3494",
                                "term": "DNA sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1872",
                                "term": "Taxonomic classification"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3751",
                                    "term": "DSV"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0781",
                    "term": "Virology"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Mac"
            ],
            "language": [],
            "license": "GPL-3.0",
            "collectionID": [
                "COVID-19"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [
                "Tools"
            ],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/cov-lineages/pangolin",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/ve/veab064",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Assignment of epidemiological lineages in an emerging pandemic using the pangolin tool",
                        "abstract": "The response of the global virus genomics community to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has been unprecedented, with significant advances made towards the ‘real-time’ generation and sharing of SARS-CoV-2 genomic data. The rapid growth in virus genome data production has necessitated the development of new analytical methods that can deal with orders of magnitude of more genomes than previously available. Here, we present and describe Phylogenetic Assignment of Named Global Outbreak Lineages (pangolin), a computational tool that has been developed to assign the most likely lineage to a given SARSCoV-2 genome sequence according to the Pango dynamic lineage nomenclature scheme. To date, nearly two million virus genomes have been submitted to the web-application implementation of pangolin, which has facilitated the SARS-CoV-2 genomic epidemiology and provided researchers with access to actionable information about the pandemic’s transmission lineages.",
                        "date": "2021-01-01T00:00:00Z",
                        "citationCount": 528,
                        "authors": [
                            {
                                "name": "O'Toole A."
                            },
                            {
                                "name": "Scher E."
                            },
                            {
                                "name": "Underwood A."
                            },
                            {
                                "name": "Jackson B."
                            },
                            {
                                "name": "Hill V."
                            },
                            {
                                "name": "McCrone J.T."
                            },
                            {
                                "name": "Colquhoun R."
                            },
                            {
                                "name": "Ruis C."
                            },
                            {
                                "name": "Abu-Dahab K."
                            },
                            {
                                "name": "Taylor B."
                            },
                            {
                                "name": "Yeats C."
                            },
                            {
                                "name": "du Plessis L."
                            },
                            {
                                "name": "Maloney D."
                            },
                            {
                                "name": "Medd N."
                            },
                            {
                                "name": "Attwood S.W."
                            },
                            {
                                "name": "Aanensen D.M."
                            },
                            {
                                "name": "Holmes E.C."
                            },
                            {
                                "name": "Pybus O.G."
                            },
                            {
                                "name": "Rambaut A."
                            }
                        ],
                        "journal": "Virus Evolution"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Áine O'Toole",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "wm75",
            "additionDate": "2024-03-27T12:31:52.492448Z",
            "lastUpdate": "2024-03-27T12:45:41.622372Z",
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            },
            "validated": 0,
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            "confidence_flag": null
        },
        {
            "name": "physher",
            "description": "A multi-algorithmic framework for phylogenetic inference",
            "homepage": "https://github.com/4ment/physher",
            "biotoolsID": "physher",
            "biotoolsCURIE": "biotools:physher",
            "version": [
                "2.0.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0547",
                            "term": "Phylogenetic inference (maximum likelihood and Bayesian methods)"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1383",
                                "term": "Nucleic acid sequence alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0872",
                                "term": "Phylogenetic tree"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1910",
                                    "term": "newick"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3293",
                    "term": "Phylogenetics"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux"
            ],
            "language": [
                "C",
                "C++"
            ],
            "license": "GPL-2.0",
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1186/s12862-014-0163-6",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Novel non-parametric models to estimate evolutionary rates and divergence times from heterochronous sequence data",
                        "abstract": "Background: Early methods for estimating divergence times from gene sequence data relied on the assumption of a molecular clock. More sophisticated methods were created to model rate variation and used auto-correlation of rates, local clocks, or the so called \"uncorrelated relaxed clock\" where substitution rates are assumed to be drawn from a parametric distribution. In the case of Bayesian inference methods the impact of the prior on branching times is not clearly understood, and if the amount of data is limited the posterior could be strongly influenced by the prior. Results: We develop a maximum likelihood method - Physher - that uses local or discrete clocks to estimate evolutionary rates and divergence times from heterochronous sequence data. Using two empirical data sets we show that our discrete clock estimates are similar to those obtained by other methods, and that Physher outperformed some methods in the estimation of the root age of an influenza virus data set. A simulation analysis suggests that Physher can outperform a Bayesian method when the real topology contains two long branches below the root node, even when evolution is strongly clock-like. Conclusions: These results suggest it is advisable to use a variety of methods to estimate evolutionary rates and divergence times from heterochronous sequence data. Physher and the associated data sets used here are available online at. © 2014 Fourment and Holmes; licensee BioMed Central Ltd.",
                        "date": "2014-07-24T00:00:00Z",
                        "citationCount": 18,
                        "authors": [
                            {
                                "name": "Fourment M."
                            },
                            {
                                "name": "Holmes E.C."
                            }
                        ],
                        "journal": "BMC Evolutionary Biology"
                    }
                },
                {
                    "doi": "10.1093/sysbio/syz046",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "19 Dubious Ways to Compute the Marginal Likelihood of a Phylogenetic Tree Topology",
                        "abstract": "The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here, we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real data sets under the JC69 model. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators.",
                        "date": "2020-03-01T00:00:00Z",
                        "citationCount": 28,
                        "authors": [
                            {
                                "name": "Fourment M."
                            },
                            {
                                "name": "Magee A.F."
                            },
                            {
                                "name": "Whidden C."
                            },
                            {
                                "name": "Bilge A."
                            },
                            {
                                "name": "Matsen F.A."
                            },
                            {
                                "name": "Minin V.N."
                            }
                        ],
                        "journal": "Systematic Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Mathieu Fourment",
                    "email": "mathieu.fourment@uts.edu.au",
                    "url": "https://github.com/4ment",
                    "orcidid": "https://orcid.org/0000-0001-8153-9822",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Developer",
                        "Maintainer",
                        "Support"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "4ment",
            "additionDate": "2024-03-26T23:53:30.900328Z",
            "lastUpdate": "2024-03-26T23:54:56.150640Z",
            "editPermission": {
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                "authors": []
            },
            "validated": 0,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "SingleM",
            "description": "Novelty-inclusive microbial community profiling of shotgun metagenomes",
            "homepage": "https://wwood.github.io/singlem/",
            "biotoolsID": "singlem",
            "biotoolsCURIE": "biotools:singlem",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3460",
                            "term": "Taxonomic classification"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3494",
                                "term": "DNA sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1930",
                                    "term": "FASTQ"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3028",
                                "term": "Taxonomy"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3752",
                                    "term": "CSV"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
                }
            ],
            "operatingSystem": [
                "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://wwood.github.io/singlem/",
                    "type": [
                        "General",
                        "Command-line options",
                        "Installation instructions"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1101/2024.01.30.578060",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": null
                }
            ],
            "credit": [],
            "community": null,
            "owner": "benjwoodcroft",
            "additionDate": "2024-03-23T21:36:23.370696Z",
            "lastUpdate": "2024-03-23T21:46:51.247440Z",
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            "validated": 0,
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            "confidence_flag": null
        },
        {
            "name": "NetAllergen",
            "description": "NetAllergen-1.0 is a predictive model based on the random forest algorithm. It incorporates novel MHC class II presentation propensity features to improve the allergenicity prediction.",
            "homepage": "https://services.healthtech.dtu.dk/services/NetAllergen-1.0/",
            "biotoolsID": "netallergen",
            "biotoolsCURIE": "biotools:netallergen",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0252",
                            "term": "Peptide immunogenicity prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0452",
                            "term": "Indel detection"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2830",
                    "term": "Immunoproteins and antigens"
                },
                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [],
            "license": null,
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/BIOADV/VBAD151",
                    "pmid": "37901344",
                    "pmcid": "PMC10603389",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "NetAllergen, a random forest model integrating MHC-II presentation propensity for improved allergenicity prediction",
                        "abstract": "Motivation: Allergy is a pathological immune reaction towards innocuous protein antigens. Although only a narrow fraction of plant or animal proteins induce allergy, atopic disorders affect millions of children and adults and cost billions in healthcare systems worldwide. In silico predictors can aid in the development of more innocuous food sources. Previous allergenicity predictors used sequence similarity, common structural domains, and amino acid physicochemical features. However, these predictors strongly rely on sequence similarity to known allergens and fail to predict protein allergenicity accurately when similarity diminishes. Results: To overcome these limitations, we collected allergens from AllergenOnline, a curated database of IgE-inducing allergens, carefully removed allergen redundancy with a novel protein partitioning pipeline, and developed a new allergen prediction method, introducing MHC presentation propensity as a novel feature. NetAllergen outperformed a sequence similarity-based BLAST baseline approach, and previous allergenicity predictor AlgPred 2 when similarity to known allergens is limited.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Li Y."
                            },
                            {
                                "name": "Sackett P.W."
                            },
                            {
                                "name": "Nielsen M."
                            },
                            {
                                "name": "Barra C."
                            }
                        ],
                        "journal": "Bioinformatics Advances"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Carolina Barra",
                    "email": "carolet@dtu.dk",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-6836-4906",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Yuchen Li",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                }
            ],
            "community": null,
            "owner": "Pub2Tools",
            "additionDate": "2024-03-21T19:07:31.093691Z",
            "lastUpdate": "2024-03-21T19:07:31.096391Z",
            "editPermission": {
                "type": "public",
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            },
            "validated": 0,
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            "confidence_flag": "tool"
        },
        {
            "name": "BaCelLo",
            "description": "Predictor for the subcellular localization of proteins in eukaryotes.",
            "homepage": "https://busca.biocomp.unibo.it/bacello",
            "biotoolsID": "bacello",
            "biotoolsCURIE": "biotools:bacello",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0849",
                                "term": "Sequence record"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        }
                    ],
                    "note": "Prediction for the subcellular localization",
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0140",
                    "term": "Protein targeting and localisation"
                },
                {
                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
                },
                {
                    "uri": "http://edamontology.org/topic_0780",
                    "term": "Plants"
                },
                {
                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [],
            "license": null,
            "collectionID": [
                "Bologna Biocomputing Group"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "https://busca.biocomp.unibo.it/bacello",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btl222",
                    "pmid": "16873501",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BaCelLo: A balanced subcellular localization predictor",
                        "abstract": "Motivation. The knowledge of the subcellular localization of a protein is fundamental for elucidating its function. It is difficult to determine the subcellular location for eukaryotic cells with experimental high-throughput procedures. Computational procedures are then needed for annotating the subcellular location of proteins in large scale genomic projects. Results. BaCelLo is a predictor for five classes of subcellular localization (secretory pathway, cytoplasm, nucleus, mitochondrion and chloroplast) and it is based on different SVMs organized in a decision tree. The system exploits the information derived from the residue sequence and from the evolutionary information contained in alignment profiles. It analyzes the whole sequence composition and the compositions of both the N- and C-termini. The training set is curated in order to avoid redundancy. For the first time a balancing procedure is introduced in order to mitigate the effect of biased training sets. Three kingdom-specific predictors are implemented: for animals, plants and fungi, respectively. When distributing the proteins from animals and fungi into four classes, accuracy of BaCelLo reach 74% and 76%, respectively; a score of 67% is obtained when proteins from plants are distributed into five classes. BaCelLo outperforms the other presently available methods for the same task and gives more balanced accuracy and coverage values for each class. We also predict the subcellular localization of five whole proteomes, Homo sapiens, Mus musculus, Caenorhabditis elegans, Saccharomyces cerevisiae and Arabidopsis thaliana, comparing the protein content in each different compartment. © 2006 Oxford University Press.",
                        "date": "2006-07-15T00:00:00Z",
                        "citationCount": 290,
                        "authors": [
                            {
                                "name": "Pierleoni A."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
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                {
                    "name": "Pier Luigi Martelli",
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                    "name": "Castrense Savojardo",
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3351",
                            "term": "Molecular surface analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                            ]
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
                                "term": "Protein report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
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                    "note": "Prediction",
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            ],
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3542",
                    "term": "Protein secondary structure"
                },
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
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            "documentation": [
                {
                    "url": "https://busca.biocomp.unibo.it/predgpi",
                    "type": [
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                    "note": null
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            ],
            "publication": [
                {
                    "doi": "10.1186/1471-2105-9-392",
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PredGPI: A GPI-anchor predictor",
                        "abstract": "Background: Several eukaryotic proteins associated to the extracellular leaflet of the plasma membrane carry a Glycosylphosphatidylinositol (GPI) anchor, which is linked to the C-terminal residue after a proteolytic cleavage occurring at the so called ω-site. Computational methods were developed to discriminate proteins that undergo this post-translational modification starting from their aminoacidic sequences. However more accurate methods are needed for a reliable annotation of whole proteomes. Results: Here we present PredGPI, a prediction method that, by coupling a Hidden Markov Model (HMM) and a Support Vector Machine (SVM), is able to efficiently predict both the presence of the GPI-anchor and the position of the ω-site. PredGPI is trained on a non-redundant dataset of experimentally characterized GPI-anchored proteins whose annotation was carefully checked in the literature. Conclusion: PredGPI outperforms all the other previously described methods and is able to correctly replicate the results of previously published high-throughput experiments. PredGPI reaches a lower rate of false positive predictions with respect to other available methods and it is therefore a costless, rapid and accurate method for screening whole proteomes. © 2008 Pierleoni et al; licensee BioMed Central Ltd.",
                        "date": "2008-09-23T00:00:00Z",
                        "citationCount": 463,
                        "authors": [
                            {
                                "name": "Pierleoni A."
                            },
                            {
                                "name": "Martelli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
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                },
                {
                    "name": "Andrea Pierleoni",
                    "email": "andrea@biocomp.unibo.it",
                    "url": null,
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                    "fundrefid": null,
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                    "note": null
                },
                {
                    "name": "Rita Casadio",
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                    "url": null,
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                },
                {
                    "name": "Pier Luigi Martelli",
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                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
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        {
            "name": "DisLocate",
            "description": "Prediction of cysteine connectivity patterns in a protein chain.",
            "homepage": "https://busca.biocomp.unibo.it/dislocate",
            "biotoolsID": "dislocate",
            "biotoolsCURIE": "biotools:dislocate",
            "version": [
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_1830",
                            "term": "Free cysteine detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0267",
                            "term": "Protein secondary structure prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_1829",
                            "term": "Cysteine bridge detection"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
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                                    "term": "FASTA"
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
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                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
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                    ],
                    "note": "Prediction",
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            "topic": [
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                    "uri": "http://edamontology.org/topic_0082",
                    "term": "Structure prediction"
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                {
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                    "term": "Protein secondary structure"
                }
            ],
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            "documentation": [
                {
                    "url": "https://busca.biocomp.unibo.it/dislocate/method",
                    "type": [
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                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btr387",
                    "pmid": "21715467",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Improving the prediction of disulfide bonds in Eukaryotes with machine learning methods and protein subcellular localization",
                        "abstract": "Motivation: Disulfide bonds stabilize protein structures and play relevant roles in their functions. Their formation requires an oxidizing environment and their stability is consequently depending on the redox ambient potential, which may differ according to the subcellular compartment. Several methods are available to predict cysteine-bonding state and connectivity patterns. However, none of them takes into consideration the relevance of protein subcellular localization. Results: Here we develop DISLOCATE, a two-step method based on machine learning models for predicting both the bonding state and the connectivity patterns of cysteine residues in a protein chain. We find that the inclusion of protein subcellular localization improves the performance of these predictive steps by 3 and 2 percentage points, respectively. When compared with previously developed methods for predicting disulfide bonds from sequence, DISLOCATE improves the overall performance by more than 10 percentage points. © The Author 2011. Published by Oxford University Press. All rights reserved.",
                        "date": "2011-08-01T00:00:00Z",
                        "citationCount": 36,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Alhamdoosh M."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Pierleoni A."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1186/1471-2105-14-S1-S10",
                    "pmid": "23368835",
                    "pmcid": "PMC3548674",
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Prediction of disulfide connectivity in proteins with machine-learning methods and correlated mutations",
                        "abstract": "Background: Recently, information derived by correlated mutations in proteins has regained relevance for predicting protein contacts. This is due to new forms of mutual information analysis that have been proven to be more suitable to highlight direct coupling between pairs of residues in protein structures and to the large number of protein chains that are currently available for statistical validation. It was previously discussed that disulfide bond topology in proteins is also constrained by correlated mutations.Results: In this paper we exploit information derived from a corrected mutual information analysis and from the inverse of the covariance matrix to address the problem of the prediction of the topology of disulfide bonds in Eukaryotes. Recently, we have shown that Support Vector Regression (SVR) can improve the prediction for the disulfide connectivity patterns. Here we show that the inclusion of the correlated mutation information increases of 5 percentage points the SVR performance (from 54% to 59%). When this approach is used in combination with a method previously developed by us and scoring at the state of art in predicting both location and topology of disulfide bonds in Eukaryotes (DisLocate), the per-protein accuracy is 38%, 2 percentage points higher than that previously obtained.Conclusions: In this paper we show that the inclusion of information derived from correlated mutations can improve the performance of the state of the art methods for predicting disulfide connectivity patterns in Eukaryotic proteins. Our analysis also provides support to the notion that improving methods to extract evolutionary information from multiple sequence alignments greatly contributes to the scoring performance of predictors suited to detect relevant features from protein chains. © 2013 Savojardo et al.; licensee BioMed Central Ltd.",
                        "date": "2013-01-14T00:00:00Z",
                        "citationCount": 10,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1007/978-3-642-21946-7_8",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Prediction of the bonding state of cysteine residues in proteins with machine-learning methods",
                        "abstract": "In this paper we evaluate the performance of machine learning methods in the task of predicting the bonding state of cysteines starting from protein sequences. This task is the first step for the identification of disulfide bonds in proteins. We score the performance of three different approaches: 1) Hidden Support Vector Machines (HSVMs) which integrate the SVM predictions with a Hidden Markov Model; 2) SVM-HMMs which discriminatively train models that are isomorphic to a kth-order hidden Markov model; 3) Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs) that we recently introduced. We evaluate two different encoding schemes based on sequence profile and position specific scoring matrix (PSSM) as computed with the PSI-BLAST program and we show that when the evolutionary information is encoded with PSSM all the methods perform better than with sequence profile. Among the different methods it appears that GRHCRFs perform slightly better than the others achieving a per protein accuracy of 87% with a Matthews correlation coefficient (C) of 0.73. Finally, we investigate the difference between disulfide bonding state predictions in Eukaryotes and Prokaryotes. Our analysis shows that the per-protein accuracy in Prokaryotic proteins is higher than that in Eukaryotes (0.88 vs 0.83). However, given the paucity of bonded cysteines in Prokaryotes as compared to Eukaryotes the Matthews correlation coefficient is drastically reduced (0.48 vs 0.80). © 2011 Springer-Verlag Berlin Heidelberg.",
                        "date": "2011-08-19T00:00:00Z",
                        "citationCount": 5,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Shukla P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"
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                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": "http://biocomp.unibo.it/savojard/",
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        },
        {
            "name": "SChloro",
            "description": "Prediction of protein sub-chloroplastinc localization.",
            "homepage": "https://busca.biocomp.unibo.it/schloro",
            "biotoolsID": "schloro",
            "biotoolsCURIE": "biotools:schloro",
            "version": [
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            "function": [
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
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                                {
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2955",
                                "term": "Sequence report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
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                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application",
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0140",
                    "term": "Protein targeting and localisation"
                },
                {
                    "uri": "http://edamontology.org/topic_0780",
                    "term": "Plant biology"
                }
            ],
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                "Mac"
            ],
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            ],
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            "download": [
                {
                    "url": "https://github.com/BolognaBiocomp/schloro",
                    "type": "Source code",
                    "note": null,
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                },
                {
                    "url": "https://hub.docker.com/r/bolognabiocomp/schloro",
                    "type": "Container file",
                    "note": null,
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                }
            ],
            "documentation": [
                {
                    "url": "https://schloro.biocomp.unibo.it/sclpred/default/index",
                    "type": [
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                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/BolognaBiocomp/schloro",
                    "type": [
                        "Command-line options"
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                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btw656",
                    "pmid": "28172591",
                    "pmcid": "PMC5408801",
                    "type": [
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "SChloro: Directing Viridiplantae proteins to six chloroplastic sub-compartments",
                        "abstract": "Motivation: Chloroplasts are organelles found in plants and involved in several important cell processes. Similarly to other compartments in the cell, chloroplasts have an internal structure comprising several sub-compartments, where different proteins are targeted to perform their functions. Given the relation between protein function and localization, the availability of effective computational tools to predict protein sub-organelle localizations is crucial for large-scale functional studies. Results: In this paper we present SChloro, a novel machine-learning approach to predict protein sub-chloroplastic localization, based on targeting signal detection and membrane protein information. The proposed approach performs multi-label predictions discriminating six chloroplastic sub-compartments that include inner membrane, outer membrane, stroma, thylakoid lumen, plastoglobule and thylakoid membrane. In comparative benchmarks, the proposed method outperforms current state-of-the-art methods in both single- and multi-compartment predictions, with an overall multi-label accuracy of 74%. The results demonstrate the relevance of the approach that is eligible as a good candidate for integration into more general large-scale annotation pipelines of protein subcellular localization.",
                        "date": "2017-01-01T00:00:00Z",
                        "citationCount": 18,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
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                        "journal": "Bioinformatics"
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                },
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                    "name": "Castrense Savojardo",
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        },
        {
            "name": "DeepSig",
            "description": "Prediction of secretory signal peptides in protein sequences",
            "homepage": "https://busca.biocomp.unibo.it/deepsig/",
            "biotoolsID": "deepsig",
            "biotoolsCURIE": "biotools:deepsig",
            "version": [
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            "otherID": [],
            "relation": [],
            "function": [
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_0418",
                            "term": "Protein signal peptide detection"
                        }
                    ],
                    "input": [
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                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
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                            "data": {
                                "uri": "http://edamontology.org/data_3028",
                                "term": "Taxonomy"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
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                            ]
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                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
                                "term": "Protein report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
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            "topic": [
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                    "uri": "http://edamontology.org/topic_3307",
                    "term": "Computational biology"
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                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
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                    "term": "Protein properties"
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            "download": [
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                {
                    "url": "https://hub.docker.com/r/bolognabiocomp/deepsig",
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                }
            ],
            "documentation": [
                {
                    "url": "https://github.com/BolognaBiocomp/deepsig",
                    "type": [
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            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btx818",
                    "pmid": "29280997",
                    "pmcid": "PMC5946842",
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "DeepSig: Deep learning improves signal peptide detection in proteins",
                        "abstract": "Motivation The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. Availability and implementation DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website.",
                        "date": "2018-05-15T00:00:00Z",
                        "citationCount": 77,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": "http://biocomp.unibo.it",
                    "orcidid": null,
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                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
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                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
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                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
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                    "typeRole": [
                        "Developer",
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Pier Luigi Martelli",
                    "email": "pierluigi.martelli@unibo.it",
                    "url": "http://biocomp.unibo.it",
                    "orcidid": "https://orcid.org/0000-0002-0274-5669",
                    "gridid": null,
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                    "fundrefid": null,
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                    "typeRole": [
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                }
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        {
            "name": "BetAware",
            "description": "A software package for the analysis of TransMembrane β-barrel proteins.",
            "homepage": "https://busca.biocomp.unibo.it/betaware",
            "biotoolsID": "betaware",
            "biotoolsCURIE": "biotools:betaware",
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            "relation": [
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                    "biotoolsID": "betaware-deep",
                    "type": "hasNewVersion"
                }
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0267",
                            "term": "Protein secondary structure prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0269",
                            "term": "Transmembrane protein prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0889",
                                "term": "Structural profile"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": "Prediction",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0082",
                    "term": "Structure prediction"
                },
                {
                    "uri": "http://edamontology.org/topic_3542",
                    "term": "Protein secondary structure"
                }
            ],
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                "Python"
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            "download": [
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                    "type": "Source code",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://busca.biocomp.unibo.it/betaware/method/",
                    "type": [
                        "Citation instructions"
                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/BolognaBiocomp/betaware",
                    "type": [
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                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/bts728",
                    "pmid": "23297037",
                    "pmcid": null,
                    "type": [
                        "Primary"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BETAWARE: A machine-learning tool to detect and predict transmembrane beta-barrel proteins in prokaryotes",
                        "abstract": "The annotation of membrane proteins in proteomes is an important problem of Computational Biology, especially after the development of high-throughput techniques that allow fast and efficient genome sequencing. Among membrane proteins, transmembrane β-barrels (TMBBs) are poorly represented in the database of protein structures (PDB) and difficult to identify with experimental approaches. They are, however, extremely important, playing key roles in several cell functions and bacterial pathogenicity. TMBBs are included in the lipid bilayer with a β-barrel structure and are presently found in the outer membranes of Gram-negative bacteria, mitochondria and chloroplasts. Recently, we developed two top-performing methods based on machine-learning approaches to tackle both the detection of TMBBs in sets of proteins and the prediction of their topology. Here, we present our BETAWARE program that includes both approaches and can run as a standalone program on a linux-based computer to easily address in-home massive protein annotation or filtering.Availability and implementation: http://www.biocomp.unibo.it/ ∼savojard/betawarecl. © 2013 The Author.",
                        "date": "2013-02-15T00:00:00Z",
                        "citationCount": 38,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btr549",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Improving the detection of transmembrane β-barrel chains with N-to-1 extreme learning machines",
                        "abstract": "Motivation: Transmembrane β-barrels (TMBBs) are extremely important proteins that play key roles in several cell functions. They cross the lipid bilayer with β-barrel structures. TMBBs are presently found in the outer membranes of Gram-negative bacteria and of mitochondria and chloroplasts. Loop exposure outside the bacterial cell membranes makes TMBBs important targets for vaccine or drug therapies. In genomes, they are not highly represented and are difficult to identify with experimental approaches. Several computational methods have been developed to discriminate TMBBs from other types of proteins. However, the best performing approaches have a high fraction of false positive predictions.Results: In this article, we introduce a new machine learning approach for TMBB detection based on N-to-1 Extreme Learning Machines that significantly outperforms previous methods achieving a Matthews correlation coefficient of 0.82, a probability of correct prediction of 0.92 and a sensitivity of 0.73. © The Author 2011. Published by Oxford University Press. All rights reserved.",
                        "date": "2011-11-01T00:00:00Z",
                        "citationCount": 20,
                        "authors": [
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1186/1748-7188-4-13",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Grammatical-restrained hidden conditional random fields for bioinformatics applications",
                        "abstract": "Background: Discriminative models are designed to naturally address classification tasks. However, some applications require the inclusion of grammar rules, and in these cases generative models, such as Hidden Markov Models (HMMs) and Stochastic Grammars, are routinely applied. Results: We introduce Grammatical-Restrained Hidden Conditional Random Fields (GRHCRFs) as an extension of Hidden Conditional Random Fields (HCRFs). GRHCRFs while preserving the discriminative character of HCRFs, can assign labels in agreement with the production rules of a defined grammar. The main GRHCRF novelty is the possibility of including in HCRFs prior knowledge of the problem by means of a defined grammar. Our current implementation allows regular grammar rules. We test our GRHCRF on a typical biosequence labeling problem: the prediction of the topology of Prokaryotic outer-membrane proteins. Conclusion: We show that in a typical biosequence labeling problem the GRHCRF performs better than CRF models of the same complexity, indicating that GRHCRFs can be useful tools for biosequence analysis applications. Availability: GRHCRF software is available under GPLv3 licence at the website. http://www.biocomp.unibo.it/~savojard/biocrf-0.9.tar.gz. © 2009 Fariselli et al; licensee BioMed Central Ltd.",
                        "date": "2009-10-22T00:00:00Z",
                        "citationCount": 18,
                        "authors": [
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Algorithms for Molecular Biology"
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                },
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                    "name": "Castrense Savojardo",
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                    "url": "http://biocomp.unibo.it/savojard/",
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