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        {
            "name": "SBOLCanvas",
            "description": "SBOLCavas is a web application for creation and editing of genetic constructs using the SBOL data and visual standard. SBOLCanvas allows a user to create a genetic design from start to finish, with the option to incorporate existing SBOL data from a SynBioHub repository.",
            "homepage": "https://sbolcanvas.org",
            "biotoolsID": "sbolcanvas",
            "biotoolsCURIE": "biotools:sbolcanvas",
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3096",
                            "term": "Editing"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3053",
                    "term": "Genetics"
                },
                {
                    "uri": "http://edamontology.org/topic_3382",
                    "term": "Imaging"
                },
                {
                    "uri": "http://edamontology.org/topic_3895",
                    "term": "Synthetic biology"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "Java",
                "Shell",
                "Other"
            ],
            "license": "Apache-2.0",
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            "elixirNode": [],
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            "link": [
                {
                    "url": "https://github.com/SynBioDex/SBOLCanvas/issues",
                    "type": [
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            ],
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            "documentation": [
                {
                    "url": "https://github.com/SynBioDex/SBOLCanvas",
                    "type": [
                        "General"
                    ],
                    "note": null
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            ],
            "publication": [
                {
                    "doi": "10.1021/ACSSYNBIO.1C00096",
                    "pmid": "34152132",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "SBOLCanvas: A Visual Editor for Genetic Designs",
                        "abstract": "© 2021 American Chemical Society.SBOLCanvas is a web-based application that can create and edit genetic constructs using the SBOL data and visual standards. SBOLCanvas allows a user to create a genetic design visually and structurally from start to finish. It also allows users to incorporate existing SBOL data from a SynBioHub repository. By the nature of being a web-based application, SBOLCanvas is readily accessible and easy to use. A live version of the latest release can be found at https://sbolcanvas.org.",
                        "date": "2021-07-16T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Terry L."
                            },
                            {
                                "name": "Earl J."
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                            {
                                "name": "Thayer S."
                            },
                            {
                                "name": "Bridge S."
                            },
                            {
                                "name": "Myers C.J."
                            }
                        ],
                        "journal": "ACS Synthetic Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Logan Terry",
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                    "url": null,
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                    "name": "Jared Earl",
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                    "name": "Sam Thayer",
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                    "name": "Samuel Bridge",
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                {
                    "name": "Chris J Myers",
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                    "url": null,
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            "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",
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                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
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                    ],
                    "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
                },
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_0296",
                            "term": "Sequence profile generation"
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                    ],
                    "input": [
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                            "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": [
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                "Animal and Crop Genomics"
            ],
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                    "note": null
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                {
                    "url": "https://github.com/EddyRivasLab/hmmer/issues",
                    "type": [
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                    ],
                    "note": null
                }
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            "download": [],
            "documentation": [
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                    "url": "http://eddylab.org/software/hmmer3/3.1b2/Userguide.pdf",
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                    ],
                    "note": "HMMER User Guide"
                },
                {
                    "url": "http://hmmer.org/documentation.html",
                    "type": [
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            ],
            "publication": [
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                    "doi": "10.1093/nar/gkr367",
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                    "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",
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                    "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."
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                        "journal": "PLoS Computational Biology"
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                    "term": "Population genetics"
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                    "url": "https://tskit.dev/msprime/docs/latest",
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            ],
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                    "doi": "10.1371/journal.pcbi.1004842",
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                    "metadata": {
                        "title": "Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes",
                        "abstract": "© 2016 Kelleher et al.A central challenge in the analysis of genetic variation is to provide realistic genome simulation across millions of samples. Present day coalescent simulations do not scale well, or use approximations that fail to capture important long-range linkage properties. Analysing the results of simulations also presents a substantial challenge, as current methods to store genealogies consume a great deal of space, are slow to parse and do not take advantage of shared structure in correlated trees. We solve these problems by introducing sparse trees and coalescence records as the key units of genealogical analysis. Using these tools, exact simulation of the coalescent with recombination for chromosome-sized regions over hundreds of thousands of samples is possible, and substantially faster than present-day approximate methods. We can also analyse the results orders of magnitude more quickly than with existing methods.",
                        "date": "2016-05-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Kelleher J."
                            },
                            {
                                "name": "Etheridge A.M."
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                            {
                                "name": "McVean G."
                            }
                        ],
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        },
        {
            "name": "MUFFIN",
            "description": "MUFFIN is multi-scale feature fusion for drug-drug interaction prediction.",
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                            "uri": "http://edamontology.org/operation_4009",
                            "term": "Small molecule design"
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                    "term": "Small molecules"
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            "name": "muCNV",
            "description": "muCNV is genotyping structural variants for population-level sequencingMulti-sample SV genotyper for large-scale WGS data. muCNV uses multiple steps for multi-sample SV genotyping, to handle large number of samples and to enable efficient parallelization:.",
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            "biotoolsID": "mucnv",
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            "version": [],
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                            "term": "Aggregation"
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                    "term": "Genotype and phenotype"
                },
                {
                    "uri": "http://edamontology.org/topic_3673",
                    "term": "Whole genome sequencing"
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                    "term": "Copy number variation"
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                    "metadata": {
                        "title": "MuCNV: Genotyping structural variants for population-level sequencing",
                        "abstract": "© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.Motivation: There are high demands for joint genotyping of structural variations with short-read sequencing, but efficient and accurate genotyping in population scale is a challenging task. Results: We developed muCNV that aggregates per-sample summary pileups for joint genotyping of >100 000 samples. Pilot results show very low Mendelian inconsistencies. Applications to large-scale projects in cloud show the computational efficiencies of muCNV genotyping pipeline. Availabilityand implementation: muCNV is publicly available for download at: https://github.com/gjun/muCNV.",
                        "date": "2021-07-15T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Jun G."
                            },
                            {
                                "name": "Sedlazeck F."
                            },
                            {
                                "name": "Zhu Q."
                            },
                            {
                                "name": "English A."
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                            {
                                "name": "Metcalf G."
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                            {
                                "name": "Kang H.M."
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                                "name": "Lee C."
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                            {
                                "name": "Gibbs R."
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                            {
                                "name": "Boerwinkle E."
                            }
                        ],
                        "journal": "Bioinformatics"
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                            "term": "Query and retrieval"
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                            "term": "Gene prediction"
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                            "term": "Gene expression profiling"
                        },
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                            "term": "Dimensionality reduction"
                        },
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                            "term": "DNA barcoding"
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                    "term": "Model organisms"
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                    "doi": "10.1021/ACS.JCIM.0C01358",
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                    "metadata": {
                        "title": "MSLDOCK: Multi-Swarm Optimization for Flexible Ligand Docking and Virtual Screening",
                        "abstract": "© Autodock and its various variants are widely utilized docking approaches, which adopt optimization methods as search algorithms for flexible ligand docking and virtual screening. However, many of them have their limitations, such as poor accuracy for dockings with highly flexible ligands and low docking efficiency. In this paper, a multi-swarm optimization algorithm integrated with Autodock environment is proposed to design a high-performance and high-efficiency docking program, namely, MSLDOCK. The search algorithm is a combination of the random drift particle swarm optimization with a novel multi-swarm strategy and the Solis and Wets local search method with a modified implementation. Due to the algorithm's structure, MSLDOCK also has a multithread mode. The experimental results reveal that MSLDOCK outperforms other two Autodock-based approaches in many aspects, such as self-docking, cross-docking, and virtual screening accuracies as well as docking efficiency. Moreover, compared with three non-Autodock-based docking programs, MSLDOCK can be a reliable choice for self-docking and virtual screening, especially for dealing with highly flexible ligand docking problems. The source code of MSLDOCK can be downloaded for free from https://github.com/lcmeteor/MSLDOCK.",
                        "date": "2021-03-22T00:00:00Z",
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                            {
                                "name": "Li C."
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                            {
                                "name": "Sun J."
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                            {
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                            }
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                        "journal": "Journal of Chemical Information and Modeling"
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                            "term": "Read pre-processing"
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                            "term": "Peak calling"
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                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_3360",
                    "term": "Biomarkers"
                },
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
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                    "term": "Oncology"
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                    "metadata": {
                        "title": "MSIFinder: a python package for detecting MSI status using random forest classifier",
                        "abstract": "© 2021, The Author(s).Background: Microsatellite instability (MSI) is a common genomic alteration in colorectal cancer, endometrial carcinoma, and other solid tumors. MSI is characterized by a high degree of polymorphism in microsatellite lengths owing to the deficiency in the mismatch repair system. Based on the degree, MSI can be classified as microsatellite instability-high (MSI-H) and microsatellite stable (MSS). MSI is a predictive biomarker for immunotherapy efficacy in advanced/metastatic solid tumors, especially in colorectal cancer patients. Several computational approaches based on target panel sequencing data have been used to detect MSI; however, they are considerably affected by the sequencing depth and panel size. Results: We developed MSIFinder, a python package for automatic MSI classification, using random forest classifier (RFC)-based genome sequencing, which is a machine learning technology. We included 19 MSI-H and 25 MSS samples as training sets. First, we selected 54 feature markers from the training sets, built an RFC model, and validated the classifier using a test set comprising 21 MSI-H and 379 MSS samples. With this test set, MSIFinder achieved a sensitivity (recall) of 1.0, a specificity of 0.997, an accuracy of 0.998, a positive predictive value of 0.954, an F1 score of 0.977, and an area under the curve of 0.999. To further verify the robustness and effectiveness of the model, we used a prospective cohort consisting of 18 MSI-H samples and 122 MSS samples. MSIFinder achieved a sensitivity (recall) of 1.0 and a specificity of 1.0. We discovered that MSIFinder is less affected by a low sequencing depth and can achieve a concordance of 0.993 while exhibiting a sequencing depth of 100×. Furthermore, we realized that MSIFinder is less affected by the panel size and can achieve a concordance of 0.99 when the panel size is 0.5 M (million bases). Conclusion: These results indicate that MSIFinder is a robust and effective MSI classification tool that can provide reliable MSI detection for scientific and clinical purposes.",
                        "date": "2021-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Zhou T."
                            },
                            {
                                "name": "Chen L."
                            },
                            {
                                "name": "Guo J."
                            },
                            {
                                "name": "Zhang M."
                            },
                            {
                                "name": "Zhang Y."
                            },
                            {
                                "name": "Cao S."
                            },
                            {
                                "name": "Lou F."
                            },
                            {
                                "name": "Wang H."
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                        ],
                        "journal": "BMC Bioinformatics"
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                }
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                    "name": "Feng Lou",
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                    "name": "Haijun Wang",
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            "description": "MRPC is an R package for inference of Causal Graphs. MRPC builds on existing PC algorithms and learns a causal network with increased accuracy. The inferred causal network contains directed and undirected edges, with the direction indicating causality. For genomic data, MRPC determines edge direction under the principle of Mendelian randomization when genotype and molecular phenotype (e.g. gene expression) data are both available at the individual level. Nodes in the inferred network may be a genotype or a molecular phenotype.",
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                            "uri": "http://edamontology.org/operation_3196",
                            "term": "Genotyping"
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                            "uri": "http://edamontology.org/operation_3766",
                            "term": "Weighted correlation network analysis"
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                    "term": "Genotype and phenotype"
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                    "term": "Molecular interactions, pathways and networks"
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                    "doi": "10.3389/FGENE.2021.651812",
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                    "metadata": {
                        "title": "MRPC: An R Package for Inference of Causal Graphs",
                        "abstract": "© Copyright © 2021 Badsha, Martin and Fu.Understanding the causal relationships between variables is a central goal of many scientific inquiries. Causal relationships may be represented by directed edges in a graph (or equivalently, a network). In biology, for example, gene regulatory networks may be viewed as a type of causal networks, where X→Y represents gene X regulating (i.e., being causal to) gene Y. However, existing general-purpose graph inference methods often result in a high number of false edges, whereas current causal inference methods developed for observational data in genomics can handle only limited types of causal relationships. We present MRPC (a PC algorithm with the principle of Mendelian Randomization), an R package that learns causal graphs with improved accuracy over existing methods. Our algorithm builds on the powerful PC algorithm (named after its developers Peter Spirtes and Clark Glymour), a canonical algorithm in computer science for learning directed acyclic graphs. The improvements in MRPC result in increased accuracy in identifying v-structures (i.e., X→Y←Z), and robustness to how the nodes are arranged in the input data. In the special case of genomic data that contain genotypes and phenotypes (e.g., gene expression) at the individual level, MRPC incorporates the principle of Mendelian randomization as constraints on edge direction to help orient the edges. MRPC allows for inference of causal graphs not only for general purposes, but also for biomedical data where multiple types of data may be input to provide evidence for causality. The R package is available on CRAN and is a free open-source software package under a GPL (≥2) license.",
                        "date": "2021-04-30T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Badsha M.B."
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                                "name": "Fu A.Q."
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                        "journal": "Frontiers in Genetics"
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