List all resources, or create a new resource.

GET /api/t/?format=api&input=%22Protein+sequence%22
HTTP 200 OK
Allow: GET, POST, HEAD, OPTIONS
Content-Type: application/json
Vary: Accept

{
    "count": 378,
    "next": "?page=2",
    "previous": null,
    "list": [
        {
            "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",
                "authors": []
            },
            "validated": 0,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": "tool"
        },
        {
            "name": "PredGPI",
            "description": "Prediction system for GPI-anchored proteins.",
            "homepage": "https://busca.biocomp.unibo.it/predgpi",
            "biotoolsID": "predgpi",
            "biotoolsCURIE": "biotools:predgpi",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "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"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
                                "term": "Protein report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        }
                    ],
                    "note": "Prediction",
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3542",
                    "term": "Protein secondary structure"
                },
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
                }
            ],
            "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/predgpi",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/1471-2105-9-392",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "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"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Andrea Pierleoni",
                    "email": "andrea@biocomp.unibo.it",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Rita Casadio",
                    "email": "casadio@biocomp.unibo.it",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Pier Luigi Martelli",
                    "email": "pierluigi.martelli@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0274-5669",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Maintainer"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2015-01-22T11:31:41Z",
            "lastUpdate": "2024-03-20T09:53:11.051662Z",
            "editPermission": {
                "type": "group",
                "authors": [
                    "Nimna"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "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": [
                "1.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "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": [
                                {
                                    "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",
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0082",
                    "term": "Structure prediction"
                },
                {
                    "uri": "http://edamontology.org/topic_3542",
                    "term": "Protein secondary structure"
                }
            ],
            "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/dislocate/method",
                    "type": [
                        "General"
                    ],
                    "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)"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": "http://biocomp.unibo.it/savojard/",
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Developer",
                        "Maintainer"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2016-01-22T15:15:26Z",
            "lastUpdate": "2024-03-20T09:51:48.660876Z",
            "editPermission": {
                "type": "group",
                "authors": [
                    "ELIXIR-ITA-BOLOGNA",
                    "savo"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": "tool"
        },
        {
            "name": "SChloro",
            "description": "Prediction of protein sub-chloroplastinc localization.",
            "homepage": "https://busca.biocomp.unibo.it/schloro",
            "biotoolsID": "schloro",
            "biotoolsCURIE": "biotools:schloro",
            "version": [
                "1"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2955",
                                "term": "Sequence report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        }
                    ],
                    "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"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [],
            "license": "GPL-3.0",
            "collectionID": [
                "Bologna Biocomputing Group"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [
                {
                    "url": "https://github.com/BolognaBiocomp/schloro",
                    "type": "Source code",
                    "note": null,
                    "version": null
                },
                {
                    "url": "https://hub.docker.com/r/bolognabiocomp/schloro",
                    "type": "Container file",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://schloro.biocomp.unibo.it/sclpred/default/index",
                    "type": [
                        "General"
                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/BolognaBiocomp/schloro",
                    "type": [
                        "Command-line options"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btw656",
                    "pmid": "28172591",
                    "pmcid": "PMC5408801",
                    "type": [
                        "Primary"
                    ],
                    "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."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": "http://www.biocomp.unibo.it",
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Maintainer"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer",
                        "Primary contact",
                        "Maintainer"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2017-03-03T15:36:30Z",
            "lastUpdate": "2024-03-20T09:50:20.056264Z",
            "editPermission": {
                "type": "group",
                "authors": [
                    "ELIXIR-ITA-BOLOGNA",
                    "savo"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": "tool"
        },
        {
            "name": "DeepSig",
            "description": "Prediction of secretory signal peptides in protein sequences",
            "homepage": "https://busca.biocomp.unibo.it/deepsig/",
            "biotoolsID": "deepsig",
            "biotoolsCURIE": "biotools:deepsig",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0418",
                            "term": "Protein signal peptide detection"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3028",
                                "term": "Taxonomy"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
                                "term": "Protein report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application",
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3307",
                    "term": "Computational biology"
                },
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_0123",
                    "term": "Protein properties"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [],
            "license": "GPL-3.0",
            "collectionID": [
                "Bologna Biocomputing Group"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [
                {
                    "url": "https://github.com/BolognaBiocomp/deepsig",
                    "type": "Source code",
                    "note": null,
                    "version": "1.2.5"
                },
                {
                    "url": "https://hub.docker.com/r/bolognabiocomp/deepsig",
                    "type": "Container file",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://github.com/BolognaBiocomp/deepsig",
                    "type": [
                        "Command-line options"
                    ],
                    "note": null
                }
            ],
            "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,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer",
                        "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,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2018-05-28T14:50:09Z",
            "lastUpdate": "2024-03-20T09:34:02.284526Z",
            "editPermission": {
                "type": "group",
                "authors": [
                    "savo",
                    "ELIXIR-ITA-BOLOGNA"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": "tool"
        },
        {
            "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",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [
                {
                    "biotoolsID": "betaware-deep",
                    "type": "hasNewVersion"
                }
            ],
            "function": [
                {
                    "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"
                                }
                            ]
                        },
                        {
                            "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"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Python"
            ],
            "license": "GPL-3.0",
            "collectionID": [
                "Bologna Biocomputing Group"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [
                {
                    "url": "https://github.com/BolognaBiocomp/betaware",
                    "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": [
                        "Command-line options"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/bts728",
                    "pmid": "23297037",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "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"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": "http://biocomp.unibo.it/savojard/",
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Developer",
                        "Maintainer"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2016-01-22T15:07:25Z",
            "lastUpdate": "2024-03-20T09:21:29.642327Z",
            "editPermission": {
                "type": "group",
                "authors": [
                    "ELIXIR-ITA-BOLOGNA",
                    "savo"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": "tool"
        },
        {
            "name": "Proteinortho",
            "description": "Proteinortho is a tool to detect orthologous genes within different species",
            "homepage": "https://gitlab.com/paulklemm_PHD/proteinortho",
            "biotoolsID": "proteinortho",
            "biotoolsCURIE": "biotools:proteinortho",
            "version": [
                "6.3.1"
            ],
            "otherID": [],
            "relation": [
                {
                    "biotoolsID": "Diamond",
                    "type": "uses"
                },
                {
                    "biotoolsID": "BLAST",
                    "type": "uses"
                }
            ],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0291",
                            "term": "Sequence clustering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2403",
                            "term": "Sequence analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3475",
                                    "term": "TSV"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2332",
                                    "term": "XML"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": "proteinortho input/*.faa"
                }
            ],
            "toolType": [
                "Command-line tool",
                "Workflow"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0797",
                    "term": "Comparative genomics"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Mac",
                "Windows"
            ],
            "language": [
                "Perl",
                "C++",
                "Python"
            ],
            "license": "GPL-2.0",
            "collectionID": [],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://gitlab.com/paulklemm_PHD/proteinortho",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                },
                {
                    "url": "https://gitlab.com/paulklemm_PHD/proteinortho/-/issues?sort=created_date&state=opened",
                    "type": [
                        "Issue tracker"
                    ],
                    "note": null
                },
                {
                    "url": "https://toolshed.g2.bx.psu.edu/repository?repository_id=584d8accff31aefe",
                    "type": [
                        "Galaxy service"
                    ],
                    "note": null
                }
            ],
            "download": [
                {
                    "url": "https://gitlab.com/paulklemm_PHD/proteinortho/-/archive/master/proteinortho-master.zip",
                    "type": "Source code",
                    "note": "Download and unpack, compile with `make all`",
                    "version": "latest"
                },
                {
                    "url": "https://packages.debian.org/unstable/proteinortho",
                    "type": "Downloads page",
                    "note": "Installation with dpkg (root privileges are required)",
                    "version": null
                },
                {
                    "url": "https://anaconda.org/bioconda/proteinortho",
                    "type": "Downloads page",
                    "note": "conda install proteinortho",
                    "version": null
                },
                {
                    "url": "https://formulae.brew.sh/formula/proteinortho",
                    "type": "Downloads page",
                    "note": "brew install proteinortho",
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://gitlab.com/paulklemm_PHD/proteinortho/-/releases",
                    "type": [
                        "Release notes"
                    ],
                    "note": null
                },
                {
                    "url": "https://gitlab.com/paulklemm_PHD/proteinortho/-/wikis/home",
                    "type": [
                        "FAQ"
                    ],
                    "note": null
                },
                {
                    "url": "https://gitlab.com/paulklemm_PHD/proteinortho",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.3389/fbinf.2023.1322477",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "version 6",
                    "note": "For the version 6 of proteinortho",
                    "metadata": {
                        "title": "Proteinortho6: pseudo-reciprocal best alignment heuristic for graph-based detection of (co-)orthologs",
                        "abstract": "Proteinortho is a widely used tool to predict (co)-orthologous groups of genes for any set of species. It finds application in comparative and functional genomics, phylogenomics, and evolutionary reconstructions. With a rapidly increasing number of available genomes, the demand for large-scale predictions is also growing. In this contribution, we evaluate and implement major algorithmic improvements that significantly enhance the speed of the analysis without reducing precision. Graph-based detection of (co-)orthologs is typically based on a reciprocal best alignment heuristic that requires an all vs. all comparison of proteins from all species under study. The initial identification of similar proteins is accelerated by introducing an alternative search tool along with a revised search strategy—the pseudo-reciprocal best alignment heuristic—that reduces the number of required sequence comparisons by one-half. The clustering algorithm was reworked to efficiently decompose very large clusters and accelerate processing. Proteinortho6 reduces the overall processing time by an order of magnitude compared to its predecessor while maintaining its small memory footprint and good predictive quality.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Klemm P."
                            },
                            {
                                "name": "Stadler P.F."
                            },
                            {
                                "name": "Lechner M."
                            }
                        ],
                        "journal": "Frontiers in Bioinformatics"
                    }
                },
                {
                    "doi": "10.1186/1471-2105-12-124",
                    "pmid": "21526987",
                    "pmcid": "PMC3114741",
                    "type": [],
                    "version": "version 4 to 5",
                    "note": "For version 4 to 5 of proteinortho",
                    "metadata": {
                        "title": "Proteinortho: Detection of (Co-)orthologs in large-scale analysis",
                        "abstract": "Background: Orthology analysis is an important part of data analysis in many areas of bioinformatics such as comparative genomics and molecular phylogenetics. The ever-increasing flood of sequence data, and hence the rapidly increasing number of genomes that can be compared simultaneously, calls for efficient software tools as brute-force approaches with quadratic memory requirements become infeasible in practise. The rapid pace at which new data become available, furthermore, makes it desirable to compute genome-wide orthology relations for a given dataset rather than relying on relations listed in databases.Results: The program Proteinortho described here is a stand-alone tool that is geared towards large datasets and makes use of distributed computing techniques when run on multi-core hardware. It implements an extended version of the reciprocal best alignment heuristic. We apply Proteinortho to compute orthologous proteins in the complete set of all 717 eubacterial genomes available at NCBI at the beginning of 2009. We identified thirty proteins present in 99% of all bacterial proteomes.Conclusions: Proteinortho significantly reduces the required amount of memory for orthology analysis compared to existing tools, allowing such computations to be performed on off-the-shelf hardware. © 2011 Lechner et al; licensee BioMed Central Ltd.",
                        "date": "2011-04-28T00:00:00Z",
                        "citationCount": 799,
                        "authors": [
                            {
                                "name": "Lechner M."
                            },
                            {
                                "name": "Findeiss S."
                            },
                            {
                                "name": "Steiner L."
                            },
                            {
                                "name": "Marz M."
                            },
                            {
                                "name": "Stadler P.F."
                            },
                            {
                                "name": "Prohaska S.J."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1371/journal.pone.0105015",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": "The synteny extension PoFF (-syteny option)",
                    "metadata": {
                        "title": "Orthology detection combining clustering and synteny for very large datasets",
                        "abstract": "The elucidation of orthology relationships is an important step both in gene function prediction as well as towards understanding patterns of sequence evolution. Orthology assignments are usually derived directly from sequence similarities for large data because more exact approaches exhibit too high computational costs. Here we present PoFF, an extension for the standalone tool Proteinortho, which enhances orthology detection by combining clustering, sequence similarity, and synteny. In the course of this work, FFAdj-MCS, a heuristic that assesses pairwise gene order using adjacencies (a similarity measure related to the breakpoint distance) was adapted to support multiple linear chromosomes and extended to detect duplicated regions. PoFF largely reduces the number of false positives and enables more fine-grained predictions than purely similarity-based approaches. The extension maintains the low memory requirements and the efficient concurrency options of its basis Proteinortho, making the software applicable to very large datasets. © 2014 Lechner et al.",
                        "date": "2014-08-19T00:00:00Z",
                        "citationCount": 67,
                        "authors": [
                            {
                                "name": "Lechner M."
                            },
                            {
                                "name": "Hernandez-Rosales M."
                            },
                            {
                                "name": "Doerr D."
                            },
                            {
                                "name": "Wieseke N."
                            },
                            {
                                "name": "Thevenin A."
                            },
                            {
                                "name": "Stoye J."
                            },
                            {
                                "name": "Hartmann R.K."
                            },
                            {
                                "name": "Prohaska S.J."
                            },
                            {
                                "name": "Stadler P.F."
                            }
                        ],
                        "journal": "PLoS ONE"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Marcus Lechner",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Maintainer"
                    ],
                    "note": null
                },
                {
                    "name": "Paul Klemm",
                    "email": null,
                    "url": "https://gitlab.com/paulklemm",
                    "orcidid": "https://orcid.org/0000-0002-3609-5713",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Maintainer"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "klemmp",
            "additionDate": "2022-03-22T18:57:49.937151Z",
            "lastUpdate": "2024-03-13T22:17:22.229211Z",
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "TPpred 2.0",
            "description": "Mitochondrial targeting peptide prediction.",
            "homepage": "https://tppred2.biocomp.unibo.it",
            "biotoolsID": "tppred_2.0",
            "biotoolsCURIE": "biotools:tppred_2.0",
            "version": [
                "2.0"
            ],
            "otherID": [],
            "relation": [
                {
                    "biotoolsID": "tppred_1.0",
                    "type": "isNewVersionOf"
                },
                {
                    "biotoolsID": "tppred_3.0",
                    "type": "hasNewVersion"
                }
            ],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0253",
                            "term": "Sequence feature detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0422",
                            "term": "Protein cleavage site prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
                                "term": "Protein report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        }
                    ],
                    "note": "Prediction",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0160",
                    "term": "Sequence sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                },
                {
                    "uri": "http://edamontology.org/topic_0140",
                    "term": "Protein targeting and localisation"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Python"
            ],
            "license": "GPL-3.0",
            "collectionID": [
                "Bologna Biocomputing Group"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [
                {
                    "url": "http://biocomp.unibo.it/savojard/tppred2.tar.gz",
                    "type": "Source code",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://tppred3.biocomp.unibo.it/tppred3/default/help",
                    "type": [
                        "General"
                    ],
                    "note": null
                },
                {
                    "url": "https://tppred2.biocomp.unibo.it/tppred2/default/software",
                    "type": [
                        "Command-line options"
                    ],
                    "note": "Installation instructions"
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btu411",
                    "pmid": "24974200",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": null
                },
                {
                    "doi": "10.1093/bioinformatics/btt089",
                    "pmid": "23428638",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "The prediction of organelle-targeting peptides in eukaryotic proteins with Grammatical-Restrained Hidden Conditional Random Fields",
                        "abstract": "Motivation: Targeting peptides are the most important signal controlling the import of nuclear encoded proteins into mitochondria and plastids. In the lack of experimental information, their prediction is an essential step when proteomes are annotated for inferring both the localization and the sequence of mature proteins.Results: We developed TPpred a new predictor of organelle-targeting peptides based on Grammatical-Restrained Hidden Conditional Random Fields. TPpred is trained on a non-redundant dataset of proteins where the presence of a target peptide was experimentally validated, comprising 297 sequences. When tested on the 297 positive and some other 8010 negative examples, TPpred outperformed available methods in both accuracy and Matthews correlation index (96% and 0.58, respectively). Given its very low-false-positive rate (3.0%), TPpred is, therefore, well suited for large-scale analyses at the proteome level. We predicted that from ∼4 to 9% of the sequences of human, Arabidopsis thaliana and yeast proteomes contain targeting peptides and are, therefore, likely to be localized in mitochondria and plastids. TPpred predictions correlate to a good extent with the experimental annotation of the subcellular localization, when available. TPpred was also trained and tested to predict the cleavage site of the organelle-targeting peptide: on this task, the average error of TPpred on mitochondrial and plastidic proteins is 7 and 15 residues, respectively. This value is lower than the error reported by other methods currently available. © 2013 The Author.",
                        "date": "2013-04-15T00:00:00Z",
                        "citationCount": 14,
                        "authors": [
                            {
                                "name": "Indio V."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "Rita Casadio",
                    "email": "rita.casadio@unibo.it",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Piero Fariselli",
                    "email": "piero.fariselli@unito.it",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2016-01-22T15:53:12Z",
            "lastUpdate": "2024-03-11T16:57:44.699185Z",
            "editPermission": {
                "type": "group",
                "authors": [
                    "ELIXIR-ITA-BOLOGNA",
                    "savo"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": "tool"
        },
        {
            "name": "MemLoci",
            "description": "Predictor for the subcellular localization of proteins associated or inserted in eukaryotes membranes.",
            "homepage": "https://mu2py.biocomp.unibo.it/memloci",
            "biotoolsID": "memloci",
            "biotoolsCURIE": "biotools:memloci",
            "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_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": "Prediction",
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0140",
                    "term": "Protein targeting and localisation"
                },
                {
                    "uri": "http://edamontology.org/topic_0820",
                    "term": "Membrane and lipoproteins"
                },
                {
                    "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://mu2py.biocomp.unibo.it/memloci/default/info",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btr108",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "MemLoci: Predicting subcellular localization of membrane proteins in eukaryotes",
                        "abstract": "Motivation: Subcellular localization is a key feature in the process of functional annotation of both globular and membrane proteins. In the absence of experimental data, protein localization is inferred on the basis of annotation transfer upon sequence similarity search. However, predictive tools are necessary when the localization of homologs is not known. This is so particularly for membrane proteins. Furthermore, most of the available predictors of subcellular localization are specifically trained on globular proteins and poorly perform on membrane proteins. Results: Here we develop MemLoci, a new support vector machinebased tool that discriminates three membrane protein localizations: plasma, internal and organelle membrane. When tested on an independent set, MemLoci outperforms existing methods, reaching an overall accuracy of 70% on predicting the location in the three membrane types, with a generalized correlation coefficient as high as 0.50. © The Author 2011. Published by Oxford University Press. All rights reserved.",
                        "date": "2011-05-01T00:00:00Z",
                        "citationCount": 47,
                        "authors": [
                            {
                                "name": "Pierleoni A."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Andrea Pierleoni",
                    "email": "andrea@biocomp.unibo.it",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Pier Luigi Martelli",
                    "email": "pierluigi.martelli@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0274-5669",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Maintainer"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2015-01-22T11:31:40Z",
            "lastUpdate": "2024-03-11T16:57:20.943436Z",
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "MemPype",
            "description": "Prediction of topology and subcellular localization of Eukaryotic membrane proteins.",
            "homepage": "https://mu2py.biocomp.unibo.it/mempype",
            "biotoolsID": "mempype",
            "biotoolsCURIE": "biotools:mempype",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0468",
                            "term": "Protein secondary structure prediction (helices)"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0418",
                            "term": "Protein signal peptide detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0422",
                            "term": "Protein cleavage site prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2489",
                            "term": "Protein subcellular localisation prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0896",
                                "term": "Protein report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                }
                            ]
                        }
                    ],
                    "note": "Prediction",
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0140",
                    "term": "Protein targeting and localisation"
                },
                {
                    "uri": "http://edamontology.org/topic_0820",
                    "term": "Membrane and lipoproteins"
                },
                {
                    "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": null,
            "elixirPlatform": [],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "https://mu2py.biocomp.unibo.it/mempype/default/help",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkr282",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "MemPype: A pipeline for the annotation of eukaryotic membrane proteins",
                        "abstract": "MemPype is a Python-based pipeline including previously published methods for the prediction of signal peptides (SPEP), glycophosphatidylinositol (GPI) anchors (PredGPI), all-alpha membrane topology (ENSEMBLE), and a recent method (MemLoci) that specifically discriminates the localization of eukaryotic membrane proteins in: 'cell membrane', 'internal membranes', 'organelle membranes'. MemLoci scores with accuracy of 70 and generalized correlation coefficient (GCC) of 0.50 on a rigorous homology-unbiased validation set and overpasses other predictors for subcellular localization. The annotation process is based both on inheritance through homology and computational methods. Each submitted protein first retrieves, when available, up to 25 similar proteins (with sequence identity ≥50 and alignment coverage ≥50 on both sequences). This helps the identification of membrane-associated proteins and detailed localization tags. Each protein is also filtered for the presence of a GPI anchor [0.8 false positive rate (FPR)]. A positive score of GPI anchor prediction labels the sequence as exposed to 'Cell surface'. Concomitantly the sequence is analysed for the presence of a signal peptide and classified with MemLoci into one of three discriminated classes. Finally the sequence is filtered for predicting its putative all-alpha protein membrane topology (FPR<1). The web server is available at: http://mu2py.biocomp.unibo.it/ mempype. © 2011 The Author(s).",
                        "date": "2011-07-01T00:00:00Z",
                        "citationCount": 27,
                        "authors": [
                            {
                                "name": "Pierleoni A."
                            },
                            {
                                "name": "Indio V."
                            },
                            {
                                "name": "Savojardo C."
                            },
                            {
                                "name": "Fariselli P."
                            },
                            {
                                "name": "Martelli P.L."
                            },
                            {
                                "name": "Casadio R."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-BOLOGNA",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Andrea Pierleoni",
                    "email": "andrea@biocomp.unibo.it",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Pier Luigi Martelli",
                    "email": "pierluigi.martelli@unibo.it",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Castrense Savojardo",
                    "email": "castrense.savojardo2@unibo.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7359-0633",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Maintainer"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "ELIXIR-ITA-BOLOGNA",
            "additionDate": "2015-01-22T11:31:40Z",
            "lastUpdate": "2024-03-11T16:56:43.617506Z",
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        }
    ]
}