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GET /api/t/?input=%22Protein%20sequence%22
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            "name": "align-cli",
            "description": "align-cli is a tool to assist manual inspection of mass spectra using alignments, isobaric sequences and other information from mass spectrometry.",
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_0292",
                            "term": "Sequence alignment"
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                    ],
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                        {
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                            "data": {
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            "name": "PyMut",
            "description": "PyMut is a Python3 module that fills the gap between machine learning and bioinformatics, providing methods that help in the prediction of pathology in protein mutations.",
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_3661",
                            "term": "SNP annotation"
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                {
                    "url": "https://mmb.irbbarcelona.org/PMut/static/PyMut.tar.gz",
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                    "url": "https://mmb.irbbarcelona.org/PMut/help",
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                    "doi": "10.1093/nar/gkx313",
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                    "pmcid": "PMC5793831",
                    "type": [
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                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PMut: A web-based tool for the annotation of pathological variants on proteins, 2017 update",
                        "abstract": "We present here a full update of the PMut predictor, active since 2005 and with a large acceptance in the field of predicting Mendelian pathological mutations. PMut internal engine has been renewed, and converted into a fully featured standalone training and prediction engine that not only powers PMut web portal, but that can generate custom predictors with alternative training sets or validation schemas. PMut Web portal allows the user to perform pathology predictions, to access a complete repository of pre-calculated predictions, and to generate and validate new predictors. The default predictor performs with good quality scores (MCC values of 0.61 on 10-fold cross validation, and 0.42 on a blind test with SwissVar 2016 mutations).",
                        "date": "2017-07-03T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Lopez-Ferrando V."
                            },
                            {
                                "name": "Gazzo A."
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                            {
                                "name": "De La Cruz X."
                            },
                            {
                                "name": "Orozco M."
                            },
                            {
                                "name": "Gelpi J.L."
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                        ],
                        "journal": "Nucleic Acids Research"
                    }
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                                    "uri": "http://edamontology.org/format_3752",
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                    "note": "Predicts the host genus of a phage given its receptor-binding protein sequences",
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                    "term": "Machine learning"
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                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
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                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
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                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
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                    "term": "Computational biology"
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                    "term": "Bioinformatics"
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                    "uri": "http://edamontology.org/topic_0781",
                    "term": "Virology"
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                {
                    "doi": "10.1371/journal.pone.0289030",
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                    "pmcid": "PMC10365317",
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                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Protein embeddings improve phage-host interaction prediction",
                        "abstract": "With the growing interest in using phages to combat antimicrobial resistance, computational methods for predicting phage-host interactions have been explored to help shortlist candidate phages. Most existing models consider entire proteomes and rely on manual feature engineering, which poses difficulty in selecting the most informative sequence properties to serve as input to the model. In this paper, we framed phage-host interaction prediction as a multiclass classification problem that takes as input the embeddings of a phage’s receptor-binding proteins, which are known to be the key machinery for host recognition, and predicts the host genus. We explored different protein language models to automatically encode these protein sequences into dense embeddings without the need for additional alignment or structural information. We show that the use of embeddings of receptor-binding proteins presents improvements over handcrafted genomic and protein sequence features. The highest performance was obtained using the transformer-based protein language model ProtT5, resulting in a 3% to 4% increase in weighted F1 and recall scores across different prediction confidence thresholds, compared to using selected handcrafted sequence features.",
                        "date": "2023-07-01T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Gonzales M.E.M."
                            },
                            {
                                "name": "Ureta J.C."
                            },
                            {
                                "name": "Shrestha A.M.S."
                            }
                        ],
                        "journal": "PLoS ONE"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Mark Edward M. Gonzales",
                    "email": "gonzales.markedward@gmail.com",
                    "url": "https://github.com/memgonzales",
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                    "note": "Research Assistant, Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines"
                },
                {
                    "name": "Jennifer C. Ureta",
                    "email": "jennifer.ureta@gmail.com",
                    "url": "https://scholar.google.com/citations?user=v0Tf_u4AAAAJ&hl=en",
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                    "note": "Faculty, Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines"
                },
                {
                    "name": "Anish M.S. Shrestha",
                    "email": "anish.shrestha@dlsu.edu.ph",
                    "url": "https://a-transposable-element.com/",
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                    "note": "Head, Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, Philippines"
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        },
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            "name": "RADAR (EBI)",
            "description": "Identify gapped approximate repeats and complex repeat architectures involving many different types of repeats.",
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                            "term": "Repeat sequence analysis"
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                    ],
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            "toolType": [
                "Web application",
                "Web service"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0157",
                    "term": "Sequence composition, complexity and repeats"
                }
            ],
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                "Linux",
                "Windows",
                "Mac"
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            ],
            "download": [
                {
                    "url": "https://sourceforge.net/projects/repeatradar/",
                    "type": "Downloads page",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "http://www.ebi.ac.uk/about/terms-of-use",
                    "type": [
                        "Terms of use"
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                {
                    "url": "https://www.ebi.ac.uk/jdispatcher/help",
                    "type": [
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                    "note": null
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                {
                    "url": "https://sourceforge.net/projects/repeatradar/",
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            "publication": [
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                    "doi": "10.1002/1097-0134(20001101)41:2<224::aid-prot70>3.0.co;2-z",
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                },
                {
                    "doi": "10.1093/nar/gkae241",
                    "pmid": "38597606",
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                    "version": null,
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                },
                {
                    "doi": "10.1093/nar/gkac240",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
                        "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.",
                        "date": "2022-07-05T00:00:00Z",
                        "citationCount": 867,
                        "authors": [
                            {
                                "name": "Madeira F."
                            },
                            {
                                "name": "Pearce M."
                            },
                            {
                                "name": "Tivey A.R.N."
                            },
                            {
                                "name": "Basutkar P."
                            },
                            {
                                "name": "Lee J."
                            },
                            {
                                "name": "Edbali O."
                            },
                            {
                                "name": "Madhusoodanan N."
                            },
                            {
                                "name": "Kolesnikov A."
                            },
                            {
                                "name": "Lopez R."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Andreas Heger",
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                            "uri": "http://edamontology.org/operation_0238",
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2762",
                                "term": "Sequence signature report"
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            ],
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                "Web service"
            ],
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                {
                    "uri": "http://edamontology.org/topic_0160",
                    "term": "Sequence sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
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            ],
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                    "metadata": {
                        "title": "Finding flexible patterns in unaligned protein sequences",
                        "abstract": "We present a new method for the identification of conserved patterns in a set of unaligned related protein sequences. It is able to discover patterns of a quite general form, allowing for both ambiguous positions and for variable length wildcard regions. It allows the user to define a class of patterns (e.g., the degree of ambiguity allowed and the length and number of gaps), and the method is then guaranteed to find the conserved patterns in this class scoring highest according to a significance measure defined. Identified patterns may be refined using one of two new algorithms. We present a new (nonstatistical) significance measure for flexible patterns. The method is shown to recover known motifs for PROSITE families and is also applied to some recently described families from the literature. Copyright © 1995 The Protein Society",
                        "date": "1995-01-01T00:00:00Z",
                        "citationCount": 245,
                        "authors": [
                            {
                                "name": "Jonassen I."
                            },
                            {
                                "name": "Collins J.F."
                            },
                            {
                                "name": "Higgins D.G."
                            }
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                        "journal": "Protein Science"
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                {
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                    "pmid": "38597606",
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                        "Other"
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                    "metadata": {
                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
                        "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.",
                        "date": "2022-07-05T00:00:00Z",
                        "citationCount": 867,
                        "authors": [
                            {
                                "name": "Madeira F."
                            },
                            {
                                "name": "Pearce M."
                            },
                            {
                                "name": "Tivey A.R.N."
                            },
                            {
                                "name": "Basutkar P."
                            },
                            {
                                "name": "Lee J."
                            },
                            {
                                "name": "Edbali O."
                            },
                            {
                                "name": "Madhusoodanan N."
                            },
                            {
                                "name": "Kolesnikov A."
                            },
                            {
                                "name": "Lopez R."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Inge Jonassen",
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                    "name": "University of Bergen",
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            "description": "Predict transmembrane topology and signal peptides from the amino acid sequence of a protein.",
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                        {
                            "uri": "http://edamontology.org/operation_0418",
                            "term": "Protein signal peptide detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0269",
                            "term": "Transmembrane protein prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
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                            "data": {
                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
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                "Web service"
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                {
                    "uri": "http://edamontology.org/topic_0160",
                    "term": "Sequence sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
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                {
                    "uri": "http://edamontology.org/topic_0820",
                    "term": "Membrane and lipoproteins"
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                {
                    "uri": "http://edamontology.org/topic_0736",
                    "term": "Protein folds and structural domains"
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                {
                    "doi": "10.1016/j.jmb.2004.03.016",
                    "pmid": null,
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                        "Primary"
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                    "version": null,
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                    "metadata": {
                        "title": "A combined transmembrane topology and signal peptide prediction method",
                        "abstract": "An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at http://phobius.cgb.ki.se/ as well as at http://phobius.binf.ku.dk/ © 2004 Elsevier Ltd. All rights reserved.",
                        "date": "2004-05-14T00:00:00Z",
                        "citationCount": 1772,
                        "authors": [
                            {
                                "name": "Kall L."
                            },
                            {
                                "name": "Krogh A."
                            },
                            {
                                "name": "Sonnhammer E.L.L."
                            }
                        ],
                        "journal": "Journal of Molecular Biology"
                    }
                },
                {
                    "doi": "10.1093/nar/gkae241",
                    "pmid": "38597606",
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": null
                },
                {
                    "doi": "10.1093/nar/gkac240",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
                        "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.",
                        "date": "2022-07-05T00:00:00Z",
                        "citationCount": 867,
                        "authors": [
                            {
                                "name": "Madeira F."
                            },
                            {
                                "name": "Pearce M."
                            },
                            {
                                "name": "Tivey A.R.N."
                            },
                            {
                                "name": "Basutkar P."
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                            {
                                "name": "Lee J."
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                            {
                                "name": "Edbali O."
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                            {
                                "name": "Madhusoodanan N."
                            },
                            {
                                "name": "Kolesnikov A."
                            },
                            {
                                "name": "Lopez R."
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                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
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                    "name": "Erik Sonnhammer",
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        },
        {
            "name": "PfamScan (EBI)",
            "description": "PfamScan is used to search a FASTA sequence against a library of Pfam HMM.",
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            "biotoolsID": "pfamscan_ebi",
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2478",
                            "term": "Nucleic acid sequence analysis"
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": []
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0858",
                                "term": "Sequence signature matches"
                            },
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                        }
                    ],
                    "note": null,
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                }
            ],
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                "Web application",
                "Web service"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0623",
                    "term": "Gene and protein families"
                }
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                },
                {
                    "url": "https://www.ebi.ac.uk/jdispatcher/help",
                    "type": [
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                {
                    "url": "http://xfam.org/",
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            "publication": [
                {
                    "doi": "10.1186/1471-2105-8-298",
                    "pmid": null,
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                    "type": [
                        "Primary"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Predicting active site residue annotations in the Pfam database",
                        "abstract": "Background: Approximately 5% of Pfam families are enzymatic, but only a small fraction of the sequences within these families (<0.5%) have had the residues responsible for catalysis determined. To increase the active site annotations in the Pfam database, we have developed a strict set of rules, chosen to reduce the rate of false positives, which enable the transfer of experimentally determined active site residue data to other sequences within the same Pfam family. Description: We have created a large database of predicted active site residues. On comparing our active site predictions to those found in UniProtKB, Catalytic Site Atlas, PROSITE and MEROPS we find that we make many novel predictions. On investigating the small subset of predictions made by these databases that are not predicted by us, we found these sequences did not meet our strict criteria for prediction. We assessed the sensitivity and specificity of our methodology and estimate that only 3% of our predicted sequences are false positives. Conclusion: We have predicted 606110 active site residues, of which 94% are not found in UniProtKB, and have increased the active site annotations in Pfam by more than 200 fold. Although implemented for Pfam, the tool we have developed for transferring the data can be applied to any alignment with associated experimental active site data and is available for download. Our active site predictions are re-calculated at each Pfam release to ensure they are comprehensive and up to date. They provide one of the largest available databases of active site annotation. © 2007 Mistry et al; licensee BioMed Central Ltd.",
                        "date": "2007-08-09T00:00:00Z",
                        "citationCount": 164,
                        "authors": [
                            {
                                "name": "Mistry J."
                            },
                            {
                                "name": "Bateman A."
                            },
                            {
                                "name": "Finn R.D."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
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                    "pmcid": null,
                    "type": [
                        "Other"
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                },
                {
                    "doi": "10.1093/nar/gkac240",
                    "pmid": null,
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                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Search and sequence analysis tools services from EMBL-EBI in 2022",
                        "abstract": "The EMBL-EBI search and sequence analysis tools frameworks provide integrated access to EMBL-EBI's data resources and core bioinformatics analytical tools. EBI Search (https://www.ebi.ac.uk/ebisearch) provides a full-text search engine across nearly 5 billion entries, while the Job Dispatcher tools framework (https://www.ebi.ac.uk/services) enables the scientific community to perform a diverse range of sequence analysis using popular bioinformatics applications. Both allow users to interact through user-friendly web applications, as well as via RESTful and SOAP-based APIs. Here, we describe recent improvements to these services and updates made to accommodate the increasing data requirements during the COVID-19 pandemic.",
                        "date": "2022-07-05T00:00:00Z",
                        "citationCount": 867,
                        "authors": [
                            {
                                "name": "Madeira F."
                            },
                            {
                                "name": "Pearce M."
                            },
                            {
                                "name": "Tivey A.R.N."
                            },
                            {
                                "name": "Basutkar P."
                            },
                            {
                                "name": "Lee J."
                            },
                            {
                                "name": "Edbali O."
                            },
                            {
                                "name": "Madhusoodanan N."
                            },
                            {
                                "name": "Kolesnikov A."
                            },
                            {
                                "name": "Lopez R."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Rob Finn",
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            "name": "pepwindow (EBI)",
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                                "term": "Protein sequence record"
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                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1522",
                                "term": "Protein sequence hydropathy plot"
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                        }
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                }
            ],
            "toolType": [
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            "topic": [
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                    "uri": "http://edamontology.org/topic_0157",
                    "term": "Sequence composition, complexity and repeats"
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            ],
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            ],
            "download": [
                {
                    "url": "https://emboss.sourceforge.net/",
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                }
            ],
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