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                        "title": "SignalP 4.0: Discriminating signal peptides from transmembrane regions",
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                                "name": "Nielsen H."
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                        "journal": "Nature Methods"
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                            "term": "Protein cleavage site prediction"
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                        "title": "Prediction of twin-arginine signal peptides",
                        "abstract": "Background: Proteins carrying twin-arginine (Tat) signal peptides are exported into the periplasmic compartment or extracellular environment independently of the classical Sec-dependent translocation pathway. To complement other methods for classical signal peptide prediction we here present a publicly available method, TatP, for prediction of bacterial Tat signal peptides. Results: We have retrieved sequence data for Tat substrates in order to train a computational method for discrimination of Sec and Tat signal peptides. The TatP method is able to positively classify 91% of 35 known Tat signal peptides and 84% of the annotated cleavage sites of these Tat signal peptides were correctly predicted. This method generates far less false positive predictions on various datasets than using simple pattern matching. Moreover, on the same datasets TatP generates less false positive predictions than a complementary rule based prediction method. Conclusion: The method developed here is able to discriminate Tat signal peptides from cytoplasmic proteins carrying a similar motif, as well as from Sec signal peptides, with high accuracy. The method allows filtering of input sequences based on Perl syntax regular expressions, whereas hydrophobicity discrimination of Tat- and Sec-signal peptides is carried out by an artificial neural network. A potential cleavage site of the predicted Tat signal peptide is also reported. The TatP prediction server is available as a public web server at http://www.cbs.dtu.dk/ services/TatP/. © 2005 Bendtsen et al; licensee BioMed Central Ltd.",
                        "date": "2005-07-02T00:00:00Z",
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                            {
                                "name": "Widdick D."
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                            {
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                            {
                                "name": "Brunak S."
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            "description": "Prediction of substrates of N-acetyltransferase A (NatA).",
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                    "note": "predicts substrates of N-acetyltransferase A (NatA)",
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                    "metadata": {
                        "title": "NetAcet: Prediction of N-terminal acetylation sites",
                        "abstract": "Summary: We present here a neural network based method for prediction of N-terminal acetylation - by far the most abundant post-translational modification in eukaryotes. The method was developed on a yeast dataset for N-acetyltransferase A (NatA) acetylation, which is the type of N-acetylation for which most examples are known and for which orthologs have been found in several eukaryotes. We obtain correlation coefficients close to 0.7 on yeast data and a sensitivity up to 74% on mammalian data, suggesting that the method is valid for eukaryotic NatA orthologs. © The Author 2004. Published by Oxford University Press. All rights reserved.",
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                        "authors": [
                            {
                                "name": "Kiemer L."
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                            {
                                "name": "Bendtsen J.D."
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                            {
                                "name": "Blom N."
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                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
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                        "title": "Feature-based prediction of non-classical and leaderless protein secretion",
                        "abstract": "We present a sequence-based method, SecretomeP, for the prediction of mammalian secretory proteins targeted to the non-classical secretory pathway, i.e. proteins without an N-terminal signal peptide. So far only a limited number of proteins have been shown experimentally to enter the non-classical secretory pathway. These are mainly fibroblast growth factors, interleukins and galectins found in the extracellular matrix. We have discovered that certain pathway-independent features are shared among secreted proteins. The method presented here is also capable of predicting (signal peptide-containing) secretory proteins where only the mature part of the protein has been annotated or cases where the signal peptide remains uncleaved. By scanning the entire human proteome we identified new proteins potentially undergoing non-classical secretion. Predictions can be made at http://www.cbs.dtu.dk/services/SecretomeP.",
                        "date": "2004-04-01T00:00:00Z",
                        "citationCount": 863,
                        "authors": [
                            {
                                "name": "Bendtsen J.D."
                            },
                            {
                                "name": "Jensen L.J."
                            },
                            {
                                "name": "Blom N."
                            },
                            {
                                "name": "Von Heijne G."
                            },
                            {
                                "name": "Brunak S."
                            }
                        ],
                        "journal": "Protein Engineering, Design and Selection"
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                    "metadata": {
                        "title": "Non-classical protein secretion in bacteria",
                        "abstract": "Background: We present an overview of bacterial non-classical secretion and a prediction method for identification of proteins following signal peptide independent secretion pathways. We have compiled a list of proteins found extracellularly despite the absence of a signal peptide. Some of these proteins also have known roles in the cytoplasm, which means they could be so-called \"moon-lightning\" proteins having more than one function. Results: A thorough literature search was conducted to compile a list of currently known bacterial non-classically secreted proteins. Pattern finding methods were applied to the sequences in order to identify putative signal sequences or motifs responsible for their secretion. We have found no signal or motif characteristic to any majority of the proteins in the compiled list of non-classically secreted proteins, and conclude that these proteins, indeed, seem to be secreted in a novel fashion. However, we also show that the apparently non-classically secreted proteins are still distinguished from cellular proteins by properties such as amino acid composition, secondary structure and disordered regions. Specifically, prediction of disorder reveals that bacterial secretory proteins are more structurally disordered than their cytoplasmic counterparts. Finally, artificial neural networks were used to construct protein feature based methods for identification of non-classically secreted proteins in both Gram-positive and Gram-negative bacteria. Conclusion: We present a publicly available prediction method capable of discriminating between this group of proteins and other proteins, thus allowing for the identification of novel non-classically secreted proteins. We suggest candidates for non-classically secreted proteins in Escherichia coli and Bacillus subtilis. The prediction method is available online. © 2005 Bendtsen et al; licensee BioMed Central Ltd.",
                        "date": "2005-10-07T00:00:00Z",
                        "citationCount": 454,
                        "authors": [
                            {
                                "name": "Bendtsen J.D."
                            },
                            {
                                "name": "Kiemer L."
                            },
                            {
                                "name": "Fausboll A."
                            },
                            {
                                "name": "Brunak S."
                            }
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                        "journal": "BMC Microbiology"
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                        }
                    ],
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                            "data": {
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                                "term": "Protein features"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
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                        }
                    ],
                    "note": "Predicts the subcellular location of eukaryotic proteins",
                    "cmd": null
                }
            ],
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                "Web application",
                "Web service"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                }
            ],
            "operatingSystem": [
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            ],
            "language": [],
            "license": "Other",
            "collectionID": [],
            "maturity": "Emerging",
            "cost": "Free of charge (with restrictions)",
            "accessibility": null,
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            "link": [
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                    "type": [
                        "Software catalogue"
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                    "note": null
                }
            ],
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            "documentation": [
                {
                    "url": "http://www.cbs.dtu.dk/services/TargetP/instructions.php",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1006/jmbi.2000.3903",
                    "pmid": "10891285",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Predicting subcellular localization of proteins based on their N-terminal amino acid sequence",
                        "abstract": "A neural network-based tool, TargetP, for large-scale subcellular location prediction of newly identified proteins has been developed. Using N-terminal sequence information only, it discriminates between proteins destined for the mitochondrion, the chloroplast, the secretory pathway, and 'other' localizations with a success rate of 85% (plant) or 90% (non-plant) on redundancy-reduced test sets. From a TargetP analysis of the recently sequenced Arabidopsis thaliana chromosomes 2 and 4 and the Ensembl Homo sapiens protein set, we estimate that 10% of all plant proteins are mitochondrial and 14% chloroplastic, and that the abundance of secretory proteins, in both Arabidopsis and Homo, is around 10%. TargetP also predicts cleavage sites with levels of correctly predicted sites ranging from approximately 40% to 50% (chloroplastic and mitochondrial presequences) to above 70% (secretory signal peptides). TargetP is available as a web-server at http://www.cbs.dtu.dk/services/TargetP/. (C) 2000 Academic Press.",
                        "date": "2000-07-21T00:00:00Z",
                        "citationCount": 3470,
                        "authors": [
                            {
                                "name": "Emanuelsson O."
                            },
                            {
                                "name": "Nielsen H."
                            },
                            {
                                "name": "Brunak S."
                            },
                            {
                                "name": "Von Heijne G."
                            }
                        ],
                        "journal": "Journal of Molecular Biology"
                    }
                }
            ],
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                    "email": null,
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                    "typeEntity": "Institute",
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                },
                {
                    "name": "Henrik Nielsen",
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        {
            "name": "ChloroP",
            "description": "Prediction of presence of chloroplast transit peptides and their cleavage sites in plant proteins.",
            "homepage": "http://cbs.dtu.dk/services/ChloroP/",
            "biotoolsID": "chlorop",
            "biotoolsCURIE": "biotools:chlorop",
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0422",
                            "term": "Protein cleavage site prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2044",
                                "term": "Sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": "predicts the presence of chloroplast transit peptides (cTP)",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                }
            ],
            "operatingSystem": [
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                    ],
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                }
            ],
            "download": [
                {
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                    "type": "Source code",
                    "note": null,
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                },
                {
                    "url": "http://www.cbs.dtu.dk/services/doc/chlorop-1.1.readme",
                    "type": "Binaries",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "http://www.cbs.dtu.dk/services/ChloroP",
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                    ],
                    "note": null
                },
                {
                    "url": "http://www.cbs.dtu.dk/services/ChloroP/pages/instr.php",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1110/ps.8.5.978",
                    "pmid": "10338008",
                    "pmcid": "PMC2144330",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites",
                        "abstract": "We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross- validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level is well above that of the publicly available chloroplast localization predictor PSORT. Cleavage sites are predicted using a scoring matrix derived by an automatic motif-finding algorithm. Approximately 60% of the known cleavage sites in our sequence collection were predicted to within ±2 residues from the cleavage sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. The ChloroP predictor is available as a web- server at http://www.cbs.dtu.dk/services/ChloroP/.",
                        "date": "1999-01-01T00:00:00Z",
                        "citationCount": 1467,
                        "authors": [
                            {
                                "name": "Emanuelsson O."
                            },
                            {
                                "name": "Nielsen H."
                            },
                            {
                                "name": "Von Heijne G."
                            }
                        ],
                        "journal": "Protein Science"
                    }
                },
                {
                    "doi": "10.1110/ps.8.5.978",
                    "pmid": "10338008",
                    "pmcid": "PMC2144330",
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "ChloroP, a neural network-based method for predicting chloroplast transit peptides and their cleavage sites",
                        "abstract": "We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross- validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level is well above that of the publicly available chloroplast localization predictor PSORT. Cleavage sites are predicted using a scoring matrix derived by an automatic motif-finding algorithm. Approximately 60% of the known cleavage sites in our sequence collection were predicted to within ±2 residues from the cleavage sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. The ChloroP predictor is available as a web- server at http://www.cbs.dtu.dk/services/ChloroP/.",
                        "date": "1999-01-01T00:00:00Z",
                        "citationCount": 1467,
                        "authors": [
                            {
                                "name": "Emanuelsson O."
                            },
                            {
                                "name": "Nielsen H."
                            },
                            {
                                "name": "Von Heijne G."
                            }
                        ],
                        "journal": "Protein Science"
                    }
                }
            ],
            "credit": [
                {
                    "name": "CBS",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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                    "typeEntity": "Institute",
                    "typeRole": [
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                    ],
                    "note": null
                },
                {
                    "name": null,
                    "email": null,
                    "url": "http://www.bioinformatics.dtu.dk/english/Service/Contact",
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                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
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                    ],
                    "note": null
                },
                {
                    "name": "Henrik Nielsen",
                    "email": "hnielsen@bioinformatics.dtu.dk",
                    "url": null,
                    "orcidid": "http://orcid.org/0000-0002-9412-9643",
                    "gridid": null,
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            ],
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            "owner": "CBS",
            "additionDate": "2015-09-12T12:50:16Z",
            "lastUpdate": "2018-12-14T09:40:07Z",
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            },
            "validated": 1,
            "homepage_status": 0,
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        },
        {
            "name": "DeepLoc",
            "description": "Prediction of eukaryotic protein subcellular localization using deep learning.",
            "homepage": "http://www.cbs.dtu.dk/services/DeepLoc/",
            "biotoolsID": "deeploc",
            "biotoolsCURIE": "biotools:deeploc",
            "version": [
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            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
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                            "uri": "http://edamontology.org/operation_2489",
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                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1233",
                                "term": "Sequence set (protein)"
                            },
                            "format": [
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                                    "term": "FASTA"
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                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2884",
                                "term": "Plot"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3603",
                                    "term": "PNG"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web service"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
                }
            ],
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                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [],
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            "collectionID": [],
            "maturity": "Mature",
            "cost": null,
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            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "http://www.cbs.dtu.dk/services/DeepLoc/instructions.php",
                    "type": [
                        "User manual"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btx431",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "DeepLoc: prediction of protein subcellular localization using deep learning",
                        "abstract": "© The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.comMotivation: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of them, predictions rely on annotation of homologues from knowledge databases. For novel proteins where no annotated homologues exist, and for predicting the effects of sequence variants, it is desirable to have methods for predicting protein properties from sequence information only.Results: Here, we present a prediction algorithm using deep neural networks to predict protein subcellular localization relying only on sequence information. At its core, the prediction model uses a recurrent neural network that processes the entire protein sequence and an attention mechanism identifying protein regions important for the subcellular localization. The model was trained and tested on a protein dataset extracted from one of the latest UniProt releases, in which experimentally annotated proteins follow more stringent criteria than previously. We demonstrate that our model achieves a good accuracy (78% for 10 categories; 92% for membrane-bound or soluble), outperforming current state-of-the-art algorithms, including those relying on homology information.Availability and implementation: The method is available as a web server at http://www.cbs.dtu.dk/services/DeepLoc. Example code is available at https://github.com/JJAlmagro/subcellular_localization. The dataset is available at http://www.cbs.dtu.dk/services/DeepLoc/data.php.Contact: jjalma@dtu.dk.",
                        "date": "2017-11-01T00:00:00Z",
                        "citationCount": 311,
                        "authors": [
                            {
                                "name": "Almagro Armenteros J.J."
                            },
                            {
                                "name": "Sonderby C.K."
                            },
                            {
                                "name": "Sonderby S.K."
                            },
                            {
                                "name": "Nielsen H."
                            },
                            {
                                "name": "Winther O."
                            }
                        ],
                        "journal": "Bioinformatics (Oxford, England)"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Henrik Nielsen",
                    "email": "hnielsen@cbs.dtu.dk",
                    "url": null,
                    "orcidid": null,
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                        "Primary contact"
                    ],
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                }
            ],
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            "owner": "hnielsen@cbs.dtu.dk",
            "additionDate": "2017-08-25T13:20:00Z",
            "lastUpdate": "2018-12-10T12:58:53Z",
            "editPermission": {
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            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "LocTree3",
            "description": "Prediction of protein subcellular localization in 18 classes for eukaryota, 6 for bacteria and 3 for archaea.",
            "homepage": "https://rostlab.org/services/loctree3/",
            "biotoolsID": "loctree3",
            "biotoolsCURIE": "biotools:loctree3",
            "version": [
                "1.0.8"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2886",
                                "term": "Protein sequence record"
                            },
                            "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_3475",
                                    "term": "TSV"
                                }
                            ]
                        }
                    ],
                    "note": "Prediction of protein sucellular localization in 18 classes for eukaryota, 6 classes for bacteria and 3 for archaea using homology searches (PSI-BLAST) and machine learning (SVM) User can provide one or more sequences in FASTA format The prediction output contains: prediction score, from 1 (weak prediction) to 100 (strong prediction); one of 18 localization classes for eukaryota, 6 for bacteria and 3 for archaea; GO identifier; GO term; prediction source (PSI-BLAST or SVM)",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
                },
                {
                    "uri": "http://edamontology.org/topic_2229",
                    "term": "Cell biology"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
            "language": [
                "PHP",
                "Java",
                "Perl",
                "JavaScript"
            ],
            "license": "GPL-3.0",
            "collectionID": [
                "Rostlab tools",
                "PredictProtein"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": null,
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://rostlab.org/owiki/index.php/Packages",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                }
            ],
            "download": [
                {
                    "url": "https://rostlab.org/owiki/index.php/Packages",
                    "type": "Source code",
                    "note": null,
                    "version": null
                },
                {
                    "url": "https://rostlab.org/owiki/index.php/Packages",
                    "type": "Binaries",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://rostlab.org/owiki/index.php/Loctree3",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gku396",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "LocTree3 prediction of localization",
                        "abstract": "The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3. © 2014 The Author(s).",
                        "date": "2014-07-01T00:00:00Z",
                        "citationCount": 158,
                        "authors": [
                            {
                                "name": "Goldberg T."
                            },
                            {
                                "name": "Hecht M."
                            },
                            {
                                "name": "Hamp T."
                            },
                            {
                                "name": "Karl T."
                            },
                            {
                                "name": "Yachdav G."
                            },
                            {
                                "name": "Ahmed N."
                            },
                            {
                                "name": "Altermann U."
                            },
                            {
                                "name": "Angerer P."
                            },
                            {
                                "name": "Ansorge S."
                            },
                            {
                                "name": "Balasz K."
                            },
                            {
                                "name": "Bernhofer M."
                            },
                            {
                                "name": "Betz A."
                            },
                            {
                                "name": "Cizmadija L."
                            },
                            {
                                "name": "Do K.T."
                            },
                            {
                                "name": "Gerke J."
                            },
                            {
                                "name": "Greil R."
                            },
                            {
                                "name": "Joerdens V."
                            },
                            {
                                "name": "Hastreiter M."
                            },
                            {
                                "name": "Hembach K."
                            },
                            {
                                "name": "Herzog M."
                            },
                            {
                                "name": "Kalemanov M."
                            },
                            {
                                "name": "Kluge M."
                            },
                            {
                                "name": "Meier A."
                            },
                            {
                                "name": "Nasir H."
                            },
                            {
                                "name": "Neumaier U."
                            },
                            {
                                "name": "Prade V."
                            },
                            {
                                "name": "Reeb J."
                            },
                            {
                                "name": "Sorokoumov A."
                            },
                            {
                                "name": "Troshani I."
                            },
                            {
                                "name": "Vorberg S."
                            },
                            {
                                "name": "Waldraff S."
                            },
                            {
                                "name": "Zierer J."
                            },
                            {
                                "name": "Nielsen H."
                            },
                            {
                                "name": "Rost B."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/bts390",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
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                        "title": "LocTree2 predicts localization for all domains of life",
                        "abstract": "Motivation: Subcellular localization is one aspect of protein function. Despite advances in high-throughput imaging, localization maps remain incomplete. Several methods accurately predict localization, but many challenges remain to be tackled. Results: In this study, we introduced a framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data. © The Author(s) 2012. Published by Oxford University Press.",
                        "date": "2012-09-01T00:00:00Z",
                        "citationCount": 75,
                        "authors": [
                            {
                                "name": "Goldberg T."
                            },
                            {
                                "name": "Hamp T."
                            },
                            {
                                "name": "Rost B."
                            }
                        ],
                        "journal": "Bioinformatics"
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            "credit": [
                {
                    "name": "Maximilian Hecht",
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                    "name": "Guy Yachdav",
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                    "name": "Timothy Karl",
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                {
                    "name": "Ernst Ludwig Ehrlich Studienwerk",
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        {
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                            "data": {
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                                "term": "Protein features"
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                        "title": "Protein annotation in the era of personal genomics",
                        "abstract": "Protein annotation provides a condensed and systematic view on the function of individual proteins. It has traditionally dealt with sorting proteins into functional categories, which for example has proven to be successful for the comparison of different species. However, if we are to understand the differences between many individuals of the same species. - humans in particular. - the focus needs be on the functional impact of individual residue variation. To fulfil the promises of personal genomics, we need to start asking not only what is in a genome but also how millions of small differences between individual genomes affect protein function and in turn human health. © 2010 Elsevier Ltd.",
                        "date": "2010-06-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Blicher T."
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                            {
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                            },
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                            },
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                                "name": "Jensen L.J."
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                                "name": "Brunak S."
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                        "journal": "Current Opinion in Structural Biology"
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