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{
    "count": 239,
    "next": "?page=2",
    "previous": null,
    "list": [
        {
            "name": "HMMER3",
            "description": "This tool is used for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models. The new HMMER3 project, HMMER is now as fast as BLAST for protein search.",
            "homepage": "http://hmmer.org/",
            "biotoolsID": "hmmer3",
            "biotoolsCURIE": "biotools:hmmer3",
            "version": [
                "3.1b1"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0863",
                                "term": "Sequence alignment"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0863",
                                "term": "Sequence alignment"
                            },
                            "format": []
                        }
                    ],
                    "note": "alimask is used to apply a mask line to a multiple sequence alignment, based on provided alignment or model coordinates.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0492",
                            "term": "Multiple sequence alignment"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0850",
                                "term": "Sequence set"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1916",
                                "term": "Alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1961",
                                    "term": "Stockholm format"
                                }
                            ]
                        }
                    ],
                    "note": "Align sequences to a profile HMM The < hmmfile > should contain only a single profile.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0296",
                            "term": "Sequence profile generation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0863",
                                "term": "Sequence alignment"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "note": "Construct profile HMM(s) from multiple sequence alignment(s).",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0336",
                            "term": "Format validation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3434",
                            "term": "Conversion"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1370",
                                    "term": "HMMER format"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3329",
                                    "term": "HMMER3"
                                }
                            ]
                        }
                    ],
                    "note": "The hmmconvert utility converts an input profile file to different HMMER formats.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0230",
                            "term": "Sequence generation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2044",
                                "term": "Sequence"
                            },
                            "format": []
                        }
                    ],
                    "note": "Sample sequences from a profile HMM.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2422",
                            "term": "Data retrieval"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0968",
                                "term": "Keyword"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "note": "Retrieve profile HMM(s) from a file.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2238",
                            "term": "Statistical calculation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1361",
                                "term": "Position frequency matrix"
                            },
                            "format": []
                        }
                    ],
                    "note": "Given an HMM, produce data required to build an HMM logo.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": "Daemon for searching a protein query against a protein database.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3329",
                                    "term": "HMMER3"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0955",
                                "term": "Data index"
                            },
                            "format": []
                        }
                    ],
                    "note": "Prepare an HMM database for hmmscan.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1927",
                                    "term": "EMBL format"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1936",
                                    "term": "GenBank format"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1963",
                                    "term": "UniProtKB format"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0957",
                                "term": "Database metadata"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2080",
                                "term": "Database search results"
                            },
                            "format": []
                        }
                    ],
                    "note": "Search protein sequence(s) against a protein profile database. query sequence to search the target database of profiles Needs to have those four auxiliary binary files generated by hmmpress. The output format is designed to be human-readable, but is often so voluminous that reading it is impractical.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0850",
                                "term": "Sequence set"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2080",
                                "term": "Database search results"
                            },
                            "format": []
                        }
                    ],
                    "note": "Search profile(s) against a sequence database. Query profile to search the target database of sequences. Sequence database. Ranked lists of the sequences with the most significant matches to the profile.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3481",
                            "term": "Probabilistic sequence generation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2238",
                            "term": "Statistical calculation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": []
                        }
                    ],
                    "note": "Collect score distributions on random sequences. The output is a table of numbers, one row for each model. Four different parametric fits to the score data are tested: (1) maximum likelihood fits to both location (mu/tau) and slope (lambda) parameters; (2) assuming lambda=log 2, maximum likelihood fit to the location parameter only; (3) same but assuming an edge- corrected lambda, using current procedures in H3 [Eddy, 2008]; and (4) using both parameters determined by H3s current procedures.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2238",
                            "term": "Statistical calculation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": []
                        }
                    ],
                    "note": "Display summary statistics for a profile file. Tabular file of summary statistics for each profile.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0338",
                            "term": "Sequence database search"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1233",
                                "term": "Sequence set (protein)"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
                            },
                            "format": []
                        }
                    ],
                    "note": "Iteratively search sequence(s) against a protein database. database sequence The output format is designed to be human-readable, but is often so voluminous that reading it is impractical.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3494",
                                "term": "DNA sequence"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2044",
                                "term": "Sequence"
                            },
                            "format": []
                        }
                    ],
                    "note": "Build a HMMER binary database file from a sequence file. This binary file may be used as a target database for the DNA search tool nhmmer.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0338",
                            "term": "Sequence database search"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2977",
                                "term": "Nucleic acid sequence"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1234",
                                "term": "Sequence set (nucleic acid)"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
                            },
                            "format": []
                        }
                    ],
                    "note": "Search DNA/RNA queries against a DNA/RNA sequence database. nucleic sequence database",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2977",
                                "term": "Nucleic acid sequence"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1364",
                                "term": "Hidden Markov model"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2080",
                                "term": "Database search results"
                            },
                            "format": []
                        }
                    ],
                    "note": "Search nucleotide sequence(s) against collections of nucleotide profiles. The <hmmdb> needs to be pressed using hmmpress before it can be searched with hmmscan. The output format is designed to be human-readable, but is often so voluminous that reading it is impractical.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0338",
                            "term": "Sequence database search"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": []
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1233",
                                "term": "Sequence set (protein)"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0857",
                                "term": "Sequence search results"
                            },
                            "format": []
                        }
                    ],
                    "note": "Search protein sequence(s) against a protein sequence database. database of sequences Ranked lists of the sequences with the most significant matches to the query. The output format is designed to be human-readable, but is often so voluminous that reading it is impractical.",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0160",
                    "term": "Sequence sites, features and motifs"
                },
                {
                    "uri": "http://edamontology.org/topic_0623",
                    "term": "Gene and protein families"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Perl",
                "C"
            ],
            "license": "Other",
            "collectionID": [
                "HMMER",
                "Animal and Crop Genomics"
            ],
            "maturity": null,
            "cost": null,
            "accessibility": null,
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/EddyRivasLab/hmmer",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/EddyRivasLab/hmmer/issues",
                    "type": [
                        "Issue tracker"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "http://eddylab.org/software/hmmer3/3.1b2/Userguide.pdf",
                    "type": [
                        "User manual"
                    ],
                    "note": "HMMER User Guide"
                },
                {
                    "url": "http://hmmer.org/documentation.html",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkr367",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "HMMER web server: Interactive sequence similarity searching",
                        "abstract": "HMMER is a software suite for protein sequence similarity searches using probabilistic methods. Previously, HMMER has mainly been available only as a computationally intensive UNIX command-line tool, restricting its use. Recent advances in the software, HMMER3, have resulted in a 100-fold speed gain relative to previous versions. It is now feasible to make efficient profile hidden Markov model (profile HMM) searches via the web. A HMMER web server (http://hmmer.janelia.org) has been designed and implemented such that most protein database searches return within a few seconds. Methods are available for searching either a single protein sequence, multiple protein sequence alignment or profile HMM against a target sequence database, and for searching a protein sequence against Pfam. The web server is designed to cater to a range of different user expertise and accepts batch uploading of multiple queries at once. All search methods are also available as RESTful web services, thereby allowing them to be readily integrated as remotely executed tasks in locally scripted workflows. We have focused on minimizing search times and the ability to rapidly display tabular results, regardless of the number of matches found, developing graphical summaries of the search results to provide quick, intuitive appraisement of them. © 2011 The Author(s).",
                        "date": "2011-07-01T00:00:00Z",
                        "citationCount": 2484,
                        "authors": [
                            {
                                "name": "Finn R.D."
                            },
                            {
                                "name": "Clements J."
                            },
                            {
                                "name": "Eddy S.R."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1371/journal.pcbi.1002195",
                    "pmid": "22039361",
                    "pmcid": "PMC3197634",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Accelerated profile HMM searches",
                        "abstract": "Profile hidden Markov models (profile HMMs) and probabilistic inference methods have made important contributions to the theory of sequence database homology search. However, practical use of profile HMM methods has been hindered by the computational expense of existing software implementations. Here I describe an acceleration heuristic for profile HMMs, the \"multiple segment Viterbi\" (MSV) algorithm. The MSV algorithm computes an optimal sum of multiple ungapped local alignment segments using a striped vector-parallel approach previously described for fast Smith/Waterman alignment. MSV scores follow the same statistical distribution as gapped optimal local alignment scores, allowing rapid evaluation of significance of an MSV score and thus facilitating its use as a heuristic filter. I also describe a 20-fold acceleration of the standard profile HMM Forward/Backward algorithms using a method I call \"sparse rescaling\". These methods are assembled in a pipeline in which high-scoring MSV hits are passed on for reanalysis with the full HMM Forward/Backward algorithm. This accelerated pipeline is implemented in the freely available HMMER3 software package. Performance benchmarks show that the use of the heuristic MSV filter sacrifices negligible sensitivity compared to unaccelerated profile HMM searches. HMMER3 is substantially more sensitive and 100- to 1000-fold faster than HMMER2. HMMER3 is now about as fast as BLAST for protein searches. © 2011 Sean R. Eddy.",
                        "date": "2011-10-01T00:00:00Z",
                        "citationCount": 2414,
                        "authors": [
                            {
                                "name": "Eddy S.R."
                            }
                        ],
                        "journal": "PLoS Computational Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Sean Eddy",
                    "email": "sean@eddylab.org",
                    "url": null,
                    "orcidid": "http://orcid.org/0000-0001-6676-4706",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": {
                "biolib": {
                    "app_name": "hmmsearch",
                    "author_name": "HMMER",
                    "author_username": "HMMER"
                }
            },
            "owner": "ELIXIR-FR",
            "additionDate": "2015-04-03T13:17:38Z",
            "lastUpdate": "2021-10-15T14:09:31.104633Z",
            "editPermission": {
                "type": "group",
                "authors": [
                    "animalandcropgenomics",
                    "laurasansc"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "IPC 2.0",
            "description": "Isoelectric Point Calculator 2.0 is a web service and a standalone program for the estimation of protein and peptide isoelectric point (pI) and dissociation constant (pKa) values using a mixture of deep learning and support vector regression models.\n\n\nIsoelectric point, the pH at which a particular molecule carries no net electrical charge, is a critical parameter for many analytical biochemistry and proteomics techniques, especially for 2D gel electrophoresis (2D-PAGE), capillary isoelectric focusing (cIEF), X-ray crystallography, and liquid chromatography–mass spectrometry (LC-MS).\n\nAccording to the benchmarks, the prediction accuracy (RMSD) of IPC 2.0 for proteins and peptides outperforms previous algorithms: 0.848 versus 0.868 and 0.222 versus 0.405, respectively. Moreover, the IPC 2.0 prediction of pKa using sequence information alone was better than the prediction from structure-based methods (0.576 versus 0.826) and a few folds faster.",
            "homepage": "http://ipc2-isoelectric-point.org/",
            "biotoolsID": "ipc_2.0",
            "biotoolsCURIE": "biotools:ipc_2.0",
            "version": [
                "2.0"
            ],
            "otherID": [],
            "relation": [
                {
                    "biotoolsID": "ipc",
                    "type": "isNewVersionOf"
                },
                {
                    "biotoolsID": "proteome-pi_2.0",
                    "type": "usedBy"
                }
            ],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2886",
                                "term": "Protein sequence record"
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                    "metadata": {
                        "title": "IPC 2.0: Prediction of isoelectric point and pKadissociation constants",
                        "abstract": "© 2021 The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.The isoelectric point is the pH at which a particular molecule is electrically neutral due to the equilibrium of positive and negative charges. In proteins and peptides, this depends on the dissociation constant (pKa) of charged groups of seven amino acids and NH+ and COO- groups at polypeptide termini. Information regarding isoelectric point and pKa is extensively used in two-dimensional gel electrophoresis (2D-PAGE), capillary isoelectric focusing (cIEF), crystallisation, and mass spectrometry. Therefore, there is a strong need for the in silico prediction of isoelectric point and pKa values. In this paper, I present Isoelectric Point Calculator 2.0 (IPC 2.0), a web server for the prediction of isoelectric points and pKa values using a mixture of deep learning and support vector regression models. The prediction accuracy (RMSD) of IPC 2.0 for proteins and peptides outperforms previous algorithms: 0.848 versus 0.868 and 0.222 versus 0.405, respectively. Moreover, the IPC 2.0 prediction of pKa using sequence information alone was better than the prediction from structure-based methods (0.576 versus 0.826) and a few folds faster. The IPC 2.0 webserver is freely available at www.ipc2-isoelectric-point.org.",
                        "date": "2021-07-02T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Kozlowski L.P."
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        {
            "name": "IPC",
            "description": "Web service and standalone program for the estimation of protein and peptide pI using different sets of dissociation constant (pKa) values, including computationally optimized pKa sets. According to the benchmarks, the newly developed pKa sets outperform previous algorithms by at least 14.9 % for proteins and 0.9 % for peptides (on average, 22.1 % and 59.6 %, respectively), which corresponds to an average error of the pI estimation of 0.87 and 0.25 pH units, respectively.",
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                            "uri": "http://edamontology.org/operation_0250",
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                                "uri": "http://edamontology.org/data_1528",
                                "term": "Protein isoelectric point"
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                                    "uri": "http://edamontology.org/format_2330",
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                    "note": "Isoelectric point, molecular weight",
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                },
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                    "term": "Proteomics experiment"
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                    "doi": "10.1186/s13062-016-0159-9",
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                    "metadata": {
                        "title": "IPC - Isoelectric Point Calculator",
                        "abstract": "© 2016 The Author(s).Background: Accurate estimation of the isoelectric point (pI) based on the amino acid sequence is useful for many analytical biochemistry and proteomics techniques such as 2-D polyacrylamide gel electrophoresis, or capillary isoelectric focusing used in combination with high-throughput mass spectrometry. Additionally, pI estimation can be helpful during protein crystallization trials. Results: Here, I present the Isoelectric Point Calculator (IPC), a web service and a standalone program for the accurate estimation of protein and peptide pI using different sets of dissociation constant (pKa) values, including two new computationally optimized pKa sets. According to the presented benchmarks, the newly developed IPC pKa sets outperform previous algorithms by at least 14.9 % for proteins and 0.9 % for peptides (on average, 22.1 % and 59.6 %, respectively), which corresponds to an average error of the pI estimation equal to 0.87 and 0.25 pH units for proteins and peptides, respectively. Moreover, the prediction of pI using the IPC pKa's leads to fewer outliers, i.e., predictions affected by errors greater than a given threshold. Conclusions: The IPC service is freely available at http://isoelectric.ovh.org Peptide and protein datasets used in the study and the precalculated pI for the PDB and some of the most frequently used proteomes are available for large-scale analysis and future development. Reviewers: This article was reviewed by Frank Eisenhaber and Zoltán Gáspári",
                        "date": "2016-10-21T00:00:00Z",
                        "citationCount": 163,
                        "authors": [
                            {
                                "name": "Kozlowski L.P."
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                        "journal": "Biology Direct"
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        {
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                    "note": "Data input is a one-letter-code protein sequence (as used in the fasta format). The output of UPF ist displayed in the GUI, which enables copy and paste into e.g. standard spreadsheet programs (columns are tab separated).",
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            "name": "XPlatCom (Cross PLATform COMmander)",
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                    "input": [
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                    "note": "Calculation and visualization of correlation between quantitative data arising from different omics platforms. The software imports quantitative expression profiles of currently two biomolecule entities (i.e. quantitative Transcriptomics (mRNA) and Proteomics expression data).\nThe output contains also the result of GO and pathway analysis for both omics platforms. Integrative analysis gives a scatter plot (and linear regression) of the corresponding Transcriptomics and Proteomics expression profiles.",
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                    "note": "ProLiC includes different possibilities to solve the problem of protein list comparison. \nProLiC uses its own data format (tsv or csv files) containing protein accessions as well as protein and peptide sequences. However in the medium run support of standard format is intended. The result of a ProLiC protein list comparison is a single text file, which gives the calculated overlaps and set differences in a very simple way.",
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                        "title": "BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences",
                        "abstract": "© 2015 The Authors.BlastKOALA and GhostKOALA are automatic annotation servers for genome and metagenome sequences, which perform KO (KEGG Orthology) assignments to characterize individual gene functions and reconstruct KEGG pathways, BRITE hierarchies and KEGG modules to infer high-level functions of the organism or the ecosystem. Both servers are made freely available at the KEGG Web site (http://www.kegg.jp/blastkoala/). In BlastKOALA, the KO assignment is performed by a modified version of the internally used KOALA algorithm after the BLAST search against a non-redundant dataset of pangenome sequences at the species, genus or family level, which is generated from the KEGG GENES database by retaining the KO content of each taxonomic category. In GhostKOALA, which utilizes more rapid GHOSTX for database search and is suitable for metagenome annotation, the pangenome dataset is supplemented with Cd-hit clusters including those for viral genes. The result files may be downloaded and manipulated for further KEGG Mapper analysis, such as comparative pathway analysis using multiple BlastKOALA results.",
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                            {
                                "name": "Kanehisa M."
                            },
                            {
                                "name": "Sato Y."
                            },
                            {
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                    "note": null,
                    "metadata": {
                        "title": "Unipept web services for metaproteomics analysis",
                        "abstract": "© 2016 The Author 2016. Published by Oxford University Press. All rights reserved.Summary Unipept is an open source web application that is designed for metaproteomics analysis with a focus on interactive datavisualization. It is underpinned by a fast index built from UniProtKB and the NCBI taxonomy that enables quick retrieval of all UniProt entries in which a given tryptic peptide occurs. Unipept version 2.4 introduced web services that provide programmatic access to the metaproteomics analysis features. This enables integration of Unipept functionality in custom applications and data processing pipelines. Availability and implementation: The web services are freely available at http://api.unipept.ugent.be and are open sourced under the MIT license. Supplementary information: Supplementary data are available at Bioinformatics online.",
                        "date": "2016-06-01T00:00:00Z",
                        "citationCount": 32,
                        "authors": [
                            {
                                "name": "Mesuere B."
                            },
                            {
                                "name": "Willems T."
                            },
                            {
                                "name": "Van Der Jeugt F."
                            },
                            {
                                "name": "Devreese B."
                            },
                            {
                                "name": "Vandamme P."
                            },
                            {
                                "name": "Dawyndt P."
                            }
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                    "note": null,
                    "metadata": {
                        "title": "Unipept Desktop: A Faster, More Powerful Metaproteomics Results Analysis Tool",
                        "abstract": "© 2021 American Chemical Society.Metaproteomics has become an important research tool to study microbial systems, which has resulted in increased metaproteomics data generation. However, efficient tools for processing the acquired data have lagged behind. One widely used tool for metaproteomics data interpretation is Unipept, a web-based tool that provides, among others, interactive and insightful visualizations. Due to its web-based implementation, however, the Unipept web application is limited in the amount of data that can be analyzed. In this manuscript we therefore present Unipept Desktop, a desktop application version of Unipept that is designed to drastically increase the throughput and capacity of metaproteomics data analysis. Moreover, it provides a novel comparative analysis pipeline and improves the organization of experimental data into projects, thus addressing the growing need for more efficient and versatile analysis tools for metaproteomics data.",
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                        "authors": [
                            {
                                "name": "Verschaffelt P."
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                            {
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                            {
                                "name": "Martens L."
                            },
                            {
                                "name": "Dawyndt P."
                            },
                            {
                                "name": "Mesuere B."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                }
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            "description": "A consensus classifier that combines six of the top performing tools for the prediction of the effects of mutation on protein function. The obtained results are provided together with annotations extracted from the Protein Mutant Database and the UniProt database.",
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            "biotoolsID": "predictsnp",
            "biotoolsCURIE": "biotools:predictsnp",
            "version": [
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            ],
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                    "biotoolsID": "mapp",
                    "type": "uses"
                },
                {
                    "biotoolsID": "nssnpanalyzer",
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                },
                {
                    "biotoolsID": "panther",
                    "type": "uses"
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                {
                    "biotoolsID": "phd-snp",
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                {
                    "biotoolsID": "polyphen",
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                {
                    "biotoolsID": "polyphen-2",
                    "type": "uses"
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                    "biotoolsID": "sift",
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                            "uri": "http://edamontology.org/operation_0331",
                            "term": "Variant effect prediction"
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
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                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                            "data": {
                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
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                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
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                                "uri": "http://edamontology.org/data_2291",
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                    ],
                    "note": "Prediction of the effect of amino acid substitution on protein function",
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                }
            ],
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                "Web application"
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                    "term": "DNA polymorphism"
                },
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                    "term": "Medical informatics"
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                    "uri": "http://edamontology.org/topic_3325",
                    "term": "Rare diseases"
                },
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                    "term": "Proteins"
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                    "term": "Genetics"
                }
            ],
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                "Windows",
                "Mac"
            ],
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                "Java",
                "Python"
            ],
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            "collectionID": [
                "Czech Republic",
                "Rare Disease",
                "ELIXIR-CZ"
            ],
            "maturity": "Mature",
            "cost": "Free of charge (with restrictions)",
            "accessibility": "Open access",
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            ],
            "elixirNode": [
                "Czech Republic"
            ],
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            "download": [
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                    "url": "https://loschmidt.chemi.muni.cz/predictsnp1/docs/predictsnp-1.0.tar.gz",
                    "type": "Binaries",
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                    "version": null
                }
            ],
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                {
                    "url": "https://loschmidt.chemi.muni.cz/predictsnp1/docs/USER_GUIDE.pdf",
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            ],
            "publication": [
                {
                    "doi": "10.1371/journal.pcbi.1003440",
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                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations",
                        "abstract": "Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. © 2014 Bendl et al.",
                        "date": "2014-01-01T00:00:00Z",
                        "citationCount": 320,
                        "authors": [
                            {
                                "name": "Bendl J."
                            },
                            {
                                "name": "Stourac J."
                            },
                            {
                                "name": "Salanda O."
                            },
                            {
                                "name": "Pavelka A."
                            },
                            {
                                "name": "Wieben E.D."
                            },
                            {
                                "name": "Zendulka J."
                            },
                            {
                                "name": "Brezovsky J."
                            },
                            {
                                "name": "Damborsky J."
                            }
                        ],
                        "journal": "PLoS Computational Biology"
                    }
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            ],
            "credit": [
                {
                    "name": "Jaroslav Bendl",
                    "email": "jaroslav.bendl@gmail.com",
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                    "name": "Jan Stourac",
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