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            "name": "RiboGalaxy",
            "description": "Galaxy-based web server where researchers can pre-process, align, analyze and visualize their own ribosome profiling (ribo-seq) data. GUI-based tools are provided to determine the strength of the triplet periodicity signal in ribo-seq data, generate metagene and ribosome profiles and carry out differential translation expression analysis using the riboSeqR suite. The RUST suite of tools can be used to quickly characterize ribosome profiling datasets to assess their quality and more.",
            "homepage": "http://ribogalaxy.ucc.ie",
            "biotoolsID": "ribogalaxy",
            "biotoolsCURIE": "biotools:ribogalaxy",
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2928",
                            "term": "Alignment"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0314",
                            "term": "Gene expression profiling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2495",
                            "term": "Gene expression analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0848",
                                "term": "Raw sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1932",
                                    "term": "FASTQ-sanger"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
                            },
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                                    "term": "PNG"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0863",
                                "term": "Sequence alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2572",
                                    "term": "BAM"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3006",
                                    "term": "bigWig"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2969",
                                "term": "Sequence image"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_3603",
                                    "term": "PNG"
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                            ]
                        }
                    ],
                    "note": "Raw ribo-seq and mRNA-seq FASTQ files There are multiple outputs depending on the tool/jobs that are run. Most of the ribo-seq analysis tools provide the output in both spreadsheet and/or visual format.",
                    "cmd": null
                }
            ],
            "toolType": [
                "Workbench"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0203",
                    "term": "Gene expression"
                }
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            "link": [
                {
                    "url": "https://github.com/riboseqorg/RiboGalaxy",
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                    "url": "http://ribogalaxy.ucc.ie/static/conditions_of_use.html",
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            "publication": [
                {
                    "doi": "10.1080/15476286.2016.1141862",
                    "pmid": "26821742",
                    "pmcid": "PMC4829337",
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                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "RiboGalaxy: A browser based platform for the alignment, analysis and visualization of ribosome profiling data",
                        "abstract": "Ribosome profiling (ribo-seq) is a technique that uses high-throughput sequencing to reveal the exact locations and densities of translating ribosomes at the entire transcriptome level. The technique has become very popular since its inception in 2009. Yet experimentalists who generate ribo-seq data often have to rely on bioinformaticians to process and analyze their data. We present RiboGalaxy (http://ribogalaxy.ucc.ie), a freely available Galaxy-based web server for processing and analyzing ribosome profiling data with the visualization functionality provided by GWIPS-viz (http://gwips.ucc.ie). RiboGalaxy offers researchers a suite of tools specifically tailored for processing ribo-seq and corresponding mRNA-seq data. Researchers can take advantage of the published workflows which reduce the multi-step alignment process to a minimum of inputs from the user. Users can then explore their own aligned data as custom tracks in GWIPS-viz and compare their ribosome profiles to existing ribo-seq tracks from published studies. In addition, users can assess the quality of their ribo-seq data, determine the strength of the triplet periodicity signal, generate meta-gene ribosome profiles as well as analyze the relative impact of mRNA sequence features on local read density. RiboGalaxy is accompanied by extensive documentation and tips for helping users. In addition we provide a forum (http://gwips.ucc.ie/Forum) where we encourage users to post their questions and feedback to improve the overall RiboGalaxy service.",
                        "date": "2016-03-03T00:00:00Z",
                        "citationCount": 67,
                        "authors": [
                            {
                                "name": "Michel A.M."
                            },
                            {
                                "name": "Mullan J.P.A."
                            },
                            {
                                "name": "Velayudhan V."
                            },
                            {
                                "name": "O'Connor P.B.F."
                            },
                            {
                                "name": "Donohue C.A."
                            },
                            {
                                "name": "Baranov P.V."
                            }
                        ],
                        "journal": "RNA Biology"
                    }
                },
                {
                    "doi": "10.1016/J.JMB.2023.168043",
                    "pmid": "37356899",
                    "pmcid": null,
                    "type": [
                        "Review"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "RiboGalaxy: A Galaxy-based Web Platform for Ribosome Profiling Data Processing – 2023 Update",
                        "abstract": "Ribosome profiling (Ribo-Seq) captures a “snapshot” of ribosomes’ locations at the entire transcriptome of a cell at sub-codon resolution providing insights into gene expression and enabling the discovery of novel translated regions. RiboGalaxy (https://ribogalaxy.genomicsdatascience.ie/), a Galaxy-based platform for processing Ribo-Seq data is a RiboSeq.Org (https://riboseq.org/) resource. RiboSeq.Org is an online gateway to a set of integrated tools for the processing and analysis of Ribo-Seq data. In this RiboGalaxy update we introduce changes to both the tools available on RiboGalaxy and to how the resource is managed on the backend. For example, in order to improve interoperability between Riboseq.Org resources, we added tools that link RiboGalaxy outputs with Trips-Viz and GWIPS-viz browsers for downstream analysis and visualisation. RiboGalaxy's backend now utilises Ansible configuration management which enhances its stability and jobs are executed within Singularity containers and are managed by Slurm, strengthening reproducibility and performance respectively.",
                        "date": "2023-07-15T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Fedorova A.D."
                            },
                            {
                                "name": "Tierney J.A.S."
                            },
                            {
                                "name": "Michel A.M."
                            },
                            {
                                "name": "Baranov P.V."
                            }
                        ],
                        "journal": "Journal of Molecular Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Science Foundation Ireland",
                    "email": "info@sfi.ie",
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                },
                {
                    "name": "Pavel V. Baranov",
                    "email": "p.baranov@ucc.ie",
                    "url": null,
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            ],
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            "owner": "A.Michel@ucc.ie",
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        },
        {
            "name": "Integron Finder",
            "description": "A tool to detect Integron in DNA sequences.",
            "homepage": "https://github.com/gem-pasteur/Integron_Finder",
            "biotoolsID": "integron_finder",
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                            "uri": "http://edamontology.org/operation_0415",
                            "term": "Nucleic acid feature detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0239",
                            "term": "Sequence motif recognition"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0362",
                            "term": "Genome annotation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3497",
                                "term": "DNA sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
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                    ],
                    "output": [
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                            "data": {
                                "uri": "http://edamontology.org/data_0849",
                                "term": "Sequence record"
                            },
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                                {
                                    "uri": "http://edamontology.org/format_1936",
                                    "term": "GenBank format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1270",
                                "term": "Feature table"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3475",
                                    "term": "TSV"
                                }
                            ]
                        }
                    ],
                    "note": "Single fasta file",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0085",
                    "term": "Functional genomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0798",
                    "term": "Mobile genetic elements"
                },
                {
                    "uri": "http://edamontology.org/topic_3047",
                    "term": "Molecular biology"
                },
                {
                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Mac"
            ],
            "language": [
                "Python"
            ],
            "license": "GPL-3.0",
            "collectionID": [
                "GEM Pasteur"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
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            "link": [
                {
                    "url": "https://github.com/gem-pasteur/Integron_Finder/",
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                    "note": null
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "http://integronfinder.readthedocs.org/",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkw319",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.5.1",
                    "note": null,
                    "metadata": {
                        "title": "Identification and analysis of integrons and cassette arrays in bacterial genomes",
                        "abstract": "Integrons recombine gene arrays and favor the spread of antibiotic resistance. Their broader roles in bacterial adaptation remain mysterious, partly due to lack of computational tools. We made a program-IntegronFinder-to identify integrons with high accuracy and sensitivity. IntegronFinder is available as a standalone program and as a web application. It searches for attC sites using covariance models, for integron-integrases using HMM profiles, and for other features (promoters, attI site) using pattern matching. We searched for integrons, integron-integrases lacking attC sites, and clusters of attC sites lacking a neighboring integron-integrase in bacterial genomes. All these elements are especially frequent in genomes of intermediate size. They are missing in some key phyla, such as α-Proteobacteria, which might reflect selection against cell lineages that acquire integrons. The similarity between attC sites is proportional to the number of cassettes in the integron, and is particularly low in clusters of attC sites lacking integron-integrases. The latter are unexpectedly abundant in genomes lacking integron-integrases or their remains, and have a large novel pool of cassettes lacking homologs in the databases. They might represent an evolutionary step between the acquisition of genes within integrons and their stabilization in the new genome.",
                        "date": "2016-06-02T00:00:00Z",
                        "citationCount": 186,
                        "authors": [
                            {
                                "name": "Cury J."
                            },
                            {
                                "name": "Jove T."
                            },
                            {
                                "name": "Touchon M."
                            },
                            {
                                "name": "Neron B."
                            },
                            {
                                "name": "Rocha E.P."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.3390/microorganisms10020224",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "2.0.1",
                    "note": null,
                    "metadata": {
                        "title": "Integron Functionality and Genome Innovation: An Update on the Subtle and Smart Strategy of Integrase and Gene Cassette Expression Regulation",
                        "abstract": "Integrons are considered hot spots for bacterial evolution, since these platforms allow one-step genomic innovation by capturing and expressing genes that provide advantageous novelties, such as antibiotic resistance. The acquisition and shuffling of gene cassettes featured by integrons enable the population to rapidly respond to changing selective pressures. However, in order to avoid deleterious effects and fitness burden, the integron activity must be tightly controlled, which happens in an elegant and elaborate fashion, as discussed in detail in the present review. Here, we aimed to provide an up‐to‐date overview of the complex regulatory networks that permeate the expression and functionality of integrons at both transcriptional and translational levels. It was possible to compile strong shreds of evidence clearly proving that these versatile platforms include functions other than acquiring and expressing gene cassettes. The well‐balanced mechanism of integron expression is intricately related with environmental signals, host cell physiology, fitness, and survival, ultimately leading to adaptation on the demand.",
                        "date": "2022-02-01T00:00:00Z",
                        "citationCount": 9,
                        "authors": [
                            {
                                "name": "Fonseca E.L."
                            },
                            {
                                "name": "Vicente A.C."
                            }
                        ],
                        "journal": "Microorganisms"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Institut Pasteur",
                    "email": null,
                    "url": "http://www.pasteur.fr/en",
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                    "gridid": "grid.428999.7",
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                },
                {
                    "name": "C3BI",
                    "email": null,
                    "url": "https://research.pasteur.fr/en/center/c3bi/",
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                {
                    "name": "ERC EVOMOBILOME",
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                },
                {
                    "name": "Jean Cury",
                    "email": "jean.cury@normalesup.org",
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                },
                {
                    "name": "Bertrand Neron",
                    "email": "bneron@pasteur.fr",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0220-0482",
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                    "typeRole": [
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                {
                    "name": "Eduardo Rocha",
                    "email": "eduardo.rocha@pasteur.fr",
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                {
                    "name": "Microbial Evolutionary Genomics",
                    "email": null,
                    "url": "https://research.pasteur.fr/en/team/microbial-evolutionary-genomics/",
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                    "typeRole": [],
                    "note": "CNRS - UMR 352"
                },
                {
                    "name": "Jean Cury",
                    "email": "jean.cury@normalesup.org",
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                    "name": "Bertrand Neron",
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                    "name": "Eduardo Rocha",
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                    "name": "Eloi Littner",
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        {
            "name": "AltaiR",
            "description": "AltaiR is a C toolkit for alignment-free and spatial-temporal analysis of multi-FASTA data.\n\nThis method provides alignment-free and spatial-temporal analysis of multi-FASTA data through the implementation of a C toolkit that is highly flexible and with characteristics covering large-scale data, namely extensive collections of genomes/proteomes. This toolkit is ideal for scenarios entangling the presence of multiple sequences from epidemic and pandemic events. AlcoR is implemented in C language using multi-threading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence(s) in (multi-) FASTA format.",
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                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
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                    "uri": "http://edamontology.org/topic_3307",
                    "term": "Computational biology"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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                    "uri": "http://edamontology.org/topic_3303",
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            "biotoolsCURIE": "biotools:carveme",
            "version": [
                "1.0.0",
                "1.1.0",
                "1.2.0",
                "1.3.0",
                "1.4.0 - 1.4.1",
                "1.5.0 - 1.5.2"
            ],
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                {
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                    "type": "uses"
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                {
                    "biotoolsID": "diamond",
                    "type": "uses"
                }
            ],
            "function": [
                {
                    "operation": [
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                            "uri": "http://edamontology.org/operation_3660",
                            "term": "Metabolic network modelling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3663",
                            "term": "Homology-based gene prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3494",
                                "term": "DNA sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2787",
                                "term": "NCBI genome accession"
                            },
                            "format": []
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2984",
                                "term": "Pathway or network report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2332",
                                    "term": "XML"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2585",
                                    "term": "SBML"
                                }
                            ]
                        }
                    ],
                    "note": "Homology-based search is default using diamond. Orthology-based search can be performed with eggNOG-mapper (kegg-bigg branch).",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3217",
                            "term": "Scaffold gap completion"
                        }
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2976",
                                "term": "Protein sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
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                                    "term": "FASTA"
                                }
                            ]
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                            "data": {
                                "uri": "http://edamontology.org/data_2787",
                                "term": "NCBI genome accession"
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                        },
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                            "data": {
                                "uri": "http://edamontology.org/data_2984",
                                "term": "Pathway or network report"
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                                    "uri": "http://edamontology.org/format_2332",
                                    "term": "XML"
                                }
                            ]
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                            "data": {
                                "uri": "http://edamontology.org/data_3724",
                                "term": "Cultivation parameter"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3475",
                                    "term": "TSV"
                                }
                            ]
                        }
                    ],
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2984",
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                            },
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                                    "uri": "http://edamontology.org/format_2332",
                                    "term": "XML"
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                            "term": "Aggregation"
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                "Command-line tool"
            ],
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                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_3391",
                    "term": "Omics"
                },
                {
                    "uri": "http://edamontology.org/topic_2259",
                    "term": "Systems biology"
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                    "term": "Computational biology"
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                "ELIXIR-Norway",
                "NTNU tools"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
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                "Norway"
            ],
            "elixirCommunity": [
                "Marine Metagenomics",
                "Metabolomics",
                "Microbial Biotechnology",
                "Proteomics"
            ],
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                    "url": "https://github.com/cdanielmachado/carveme/releases/tag/1.5.2",
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            ],
            "documentation": [
                {
                    "url": "https://carveme.readthedocs.io/en/latest/index.html",
                    "type": [
                        "General",
                        "Installation instructions",
                        "Quick start guide",
                        "Command-line options",
                        "User manual"
                    ],
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                },
                {
                    "url": "https://github.com/cdanielmachado/carveme#readme",
                    "type": [
                        "Citation instructions"
                    ],
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                },
                {
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                        "Release notes"
                    ],
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                }
            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gky537",
                    "pmid": "30192979",
                    "pmcid": "PMC6125623",
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0.0",
                    "note": null,
                    "metadata": {
                        "title": "Fast automated reconstruction of genome-scale metabolic models for microbial species and communities",
                        "abstract": "Genome-scale metabolic models are instrumental in uncovering operating principles of cellular metabolism, for model-guided re-engineering, and unraveling cross-feeding in microbial communities. Yet, the application of genome-scale models, especially to microbial communities, is lagging behind the availability of sequenced genomes. This is largely due to the time-consuming steps of manual curation required to obtain good quality models. Here, we present an automated tool, CarveMe, for reconstruction of species and community level metabolic models. We introduce the concept of a universal model, which is manually curated and simulation ready. Starting with this universal model and annotated genome sequences, CarveMe uses a topdown approach to build single-species and community models in a fast and scalable manner. We show that CarveMe models perform closely to manually curated models in reproducing experimental phenotypes (substrate utilization and gene essentiality). Additionally, we build a collection of 74 models for human gut bacteria and test their ability to reproduce growth on a set of experimentally defined media. Finally, we create a database of 5587 bacterial models and demonstrate its potential for fast generation of microbial community models. Overall, CarveMe provides an open-source and user-friendly tool towards broadening the use of metabolic modeling in studying microbial species and communities.",
                        "date": "2018-09-06T00:00:00Z",
                        "citationCount": 239,
                        "authors": [
                            {
                                "name": "Machado D."
                            },
                            {
                                "name": "Andrejev S."
                            },
                            {
                                "name": "Tramontano M."
                            },
                            {
                                "name": "Patil K.R."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1038/s41559-022-01939-0",
                    "pmid": "36471121",
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Reply to: Erroneous predictions of auxotrophies by CarveMe",
                        "abstract": "",
                        "date": "2023-02-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Machado D."
                            },
                            {
                                "name": "Patil K.R."
                            }
                        ],
                        "journal": "Nature Ecology and Evolution"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Daniel Machado",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-2063-5383",
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                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Developer",
                        "Documentor",
                        "Maintainer"
                    ],
                    "note": null
                },
                {
                    "name": "The Norwegian Bioinformatics Platform (ELIXIR-Norway) Helpdesk",
                    "email": "support@elixir.no",
                    "url": "https://elixir.no/helpdesk",
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Consortium",
                    "typeRole": [
                        "Support"
                    ],
                    "note": null
                },
                {
                    "name": "Sergej Andrejev",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-7875-0261",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Contributor"
                    ],
                    "note": null
                },
                {
                    "name": "Melanie Tramontano",
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                    "orcidid": "https://orcid.org/0000-0001-6407-527X",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Contributor"
                    ],
                    "note": null
                },
                {
                    "name": "Kiran Raosaheb Patil",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-6166-8640",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Contributor"
                    ],
                    "note": null
                },
                {
                    "name": "NTNU",
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                    "url": "http://www.ntnu.edu",
                    "orcidid": null,
                    "gridid": "grid.5947.f",
                    "rorid": "05xg72x27",
                    "fundrefid": null,
                    "typeEntity": "Institute",
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                    ],
                    "note": "Developed at NTNU 2020-present"
                },
                {
                    "name": "European Molecular Biology Laboratory (EMBL)",
                    "email": null,
                    "url": "http://embl.org/",
                    "orcidid": null,
                    "gridid": "grid.4709.a",
                    "rorid": "03mstc592",
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": "Developed at EMBL 2017-2019"
                },
                {
                    "name": "European Union's Horizon 2020",
                    "email": null,
                    "url": "https://doi.org/10.3030/686070",
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Funding agency",
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                    "note": null
                },
                {
                    "name": "Erin Calhoun",
                    "email": "erin.calhoun@uit.no",
                    "url": null,
                    "orcidid": "https://orcid.org/0009-0003-3752-7156",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Documentor"
                    ],
                    "note": "Curation of bio.tools profile - contact me with updates/corrections."
                }
            ],
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            "owner": "dmachado",
            "additionDate": "2021-02-18T12:08:51Z",
            "lastUpdate": "2023-08-15T10:51:32.592010Z",
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            },
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        },
        {
            "name": "omnipy",
            "description": "Omnipy is a Python library for type-driven data wrangling and scalable data flow orchestration. It simplifies creation and deployment of (meta)data transformation processes, emphasizing FAIRification, executable metadata crosswalks and data brokering. Omnipy aids generic data tasks like extraction from multiple sources, (meta)data mapping and systematic data model transformations. Embracing a \"parse, don't validate\" approach, omnipy uses Python type hints and pydantic for data consistency and adherence to specific models. Highly modular, it allows integration of (meta)data transformation steps dependent on software like pandas and R. The architecture supports pluggable execution engines like Prefect, a highly interoperable industry-developed Open Source data flow orchestration engine. Envisioned as a community effort, omnipy aims to offer various import formats and data models (both hierarchical and tabular representations), as well as ontology mapping and semiautomatic data cleaning.",
            "homepage": "https://fairtracks.net/fair/#fair-07-transformation",
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            ],
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            "relation": [
                {
                    "biotoolsID": "pandas",
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                            "uri": "http://edamontology.org/operation_2422",
                            "term": "Data retrieval"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3763",
                            "term": "Service invocation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2429",
                            "term": "Mapping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3695",
                            "term": "Filtering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
                        }
                    ],
                    "input": [
                        {
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                                "uri": "http://edamontology.org/data_2093",
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                                {
                                    "uri": "http://edamontology.org/format_3996",
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                    ],
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                            "data": {
                                "uri": "http://edamontology.org/data_2080",
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                                    "uri": "http://edamontology.org/format_3464",
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                        }
                    ],
                    "note": "Import diverse data from API endpoints (JSON) and relational databases. Output as JSON files specifying fields from the mapping API or as pandas DataFrames with reference metadata.",
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                },
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                            "uri": "http://edamontology.org/operation_3357",
                            "term": "Format detection"
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                            "uri": "http://edamontology.org/operation_1812",
                            "term": "Parsing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3096",
                            "term": "Editing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3359",
                            "term": "Splitting"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2526",
                                "term": "Text data"
                            },
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                                {
                                    "uri": "http://edamontology.org/format_3464",
                                    "term": "JSON"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3475",
                                    "term": "TSV"
                                },
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                                    "term": "Binary format"
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                                    "term": "GSuite"
                                },
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                                    "uri": "http://edamontology.org/format_2330",
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                    ],
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                                "term": "Matrix"
                            },
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                        }
                    ],
                    "note": "Import data as files in a supported format. Output as pandas DataFrames with embedded JSON objects/lists and reference metadata.",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3435",
                            "term": "Standardisation and normalisation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
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                                "term": "Matrix"
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                    ],
                    "output": [
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                                "term": "Matrix"
                            },
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                        }
                    ],
                    "note": "Data cleanup and transformation.",
                    "cmd": null
                },
                {
                    "operation": [
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                            "uri": "http://edamontology.org/operation_2429",
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                    ],
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                        }
                    ],
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                                "term": "Database cross-mapping"
                            },
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                        }
                    ],
                    "note": "Manual mapping of FAIRtracks objects and attributes to corresponding tables and columns in the original data.",
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                },
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                    "operation": [
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                            "term": "Aggregation"
                        },
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                            "uri": "http://edamontology.org/operation_2429",
                            "term": "Mapping"
                        },
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                            "uri": "http://edamontology.org/operation_3763",
                            "term": "Service invocation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2082",
                                "term": "Matrix"
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                        },
                        {
                            "data": {
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                                "term": "Data reference"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_3033",
                                    "term": "Matrix format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0954",
                                "term": "Database cross-mapping"
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                        }
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                            "data": {
                                "uri": "http://edamontology.org/data_3509",
                                "term": "Ontology mapping"
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                                {
                                    "uri": "http://edamontology.org/format_3033",
                                    "term": "Matrix format"
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                        }
                    ],
                    "note": "Generate initial FAIRtracks tables by applying the model map, mapping FAIRtracks attributes with one or more attributes (columns) in the original table.",
                    "cmd": null
                },
                {
                    "operation": [
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                            "uri": "http://edamontology.org/operation_2428",
                            "term": "Validation"
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                    "term": "Data architecture, analysis and design"
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                    "term": "Data identity and mapping"
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                    "term": "Data integration and warehousing"
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                    "uri": "http://edamontology.org/topic_3473",
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                    "term": "Ontology and terminology"
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                    "uri": "http://edamontology.org/topic_0769",
                    "term": "Workflows"
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            ],
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                "Windows",
                "Mac"
            ],
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            "license": "Apache-2.0",
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                "UiO tools"
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                        "title": "JASPAR 2020: Update of the open-Access database of transcription factor binding profiles",
                        "abstract": "JASPAR (http://jaspar.genereg.net) is an open-Access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) for TFs across multiple species in six taxonomic groups. In this 8th release of JASPAR, the CORE collection has been expanded with 245 new PFMs (169 for vertebrates, 42 for plants, 17 for nematodes, 10 for insects, and 7 for fungi), and 156 PFMs were updated (125 for vertebrates, 28 for plants and 3 for insects). These new profiles represent an 18% expansion compared to the previous release. JASPAR 2020 comes with a novel collection of unvalidated TF-binding profiles for which our curators did not find orthogonal supporting evidence in the literature. This collection has a dedicated web form to engage the community in the curation of unvalidated TF-binding profiles. Moreover, we created a Q&A forum to ease the communication between the user community and JASPAR curators. Finally, we updated the genomic tracks, inference tool, and TF-binding profile similarity clusters. All the data is available through the JASPAR website, its associated RESTful API, and through the JASPAR2020 R/Bioconductor package.",
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                            {
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                            {
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                    "version": "2018",
                    "note": null,
                    "metadata": {
                        "title": "JASPAR 2018: Update of the open-access database of transcription factor binding profiles and its web framework",
                        "abstract": "JASPAR (http://jaspar.genereg.net) is an open-access database of curated, non-redundant transcription factor (TF)-binding profiles stored as position frequency matrices (PFMs) and TF flexible models (TFFMs) for TFs across multiple species in six taxonomic groups. In the 2018 release of JASPAR, the CORE collection has been expanded with 322 new PFMs (60 for vertebrates and 262 for plants) and 33 PFMs were updated (24 for vertebrates, 8 for plants and 1 for insects). These new profiles represent a 30% expansion compared to the 2016 release. In addition, we have introduced 316 TFFMs (95 for vertebrates, 218 for plants and 3 for insects). This release incorporates clusters of similar PFMs in each taxon and each TF class per taxon. The JASPAR 2018 CORE vertebrate collection of PFMs was used to predict TF-binding sites in the human genome. The predictions are made available to the scientific community through a UCSC Genome Browser track data hub. Finally, this update comes with a new web framework with an interactive and responsive user-interface, along with new features. All the underlying data can be retrieved programmatically using a RESTful API and through the JASPAR 2018 R/Bioconductor package.",
                        "date": "2018-01-01T00:00:00Z",
                        "citationCount": 879,
                        "authors": [
                            {
                                "name": "Khan A."
                            },
                            {
                                "name": "Fornes O."
                            },
                            {
                                "name": "Stigliani A."
                            },
                            {
                                "name": "Gheorghe M."
                            },
                            {
                                "name": "Castro-Mondragon J.A."
                            },
                            {
                                "name": "Van Der Lee R."
                            },
                            {
                                "name": "Bessy A."
                            },
                            {
                                "name": "Cheneby J."
                            },
                            {
                                "name": "Kulkarni S.R."
                            },
                            {
                                "name": "Tan G."
                            },
                            {
                                "name": "Baranasic D."
                            },
                            {
                                "name": "Arenillas D.J."
                            },
                            {
                                "name": "Sandelin A."
                            },
                            {
                                "name": "Vandepoele K."
                            },
                            {
                                "name": "Lenhard B."
                            },
                            {
                                "name": "Ballester B."
                            },
                            {
                                "name": "Wasserman W.W."
                            },
                            {
                                "name": "Parcy F."
                            },
                            {
                                "name": "Mathelier A."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btx804",
                    "pmid": "29253085",
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "JASPAR RESTful API: Accessing JASPAR data from any programming language",
                        "abstract": "JASPAR is a widely used open-access database of curated, non-redundant transcription factor binding profiles. Currently, data from JASPAR can be retrieved as flat files or by using programming language-specific interfaces. Here, we present a programming language-independent application programming interface (API) to access JASPAR data using the Representational State Transfer (REST) architecture. The REST API enables programmatic access to JASPAR by most programming languages and returns data in eight widely used formats. Several endpoints are available to access the data and an endpoint is available to infer the TF binding profile(s) likely bound by a given DNA binding domain protein sequence. Additionally, it provides an interactive browsable interface for bioinformatics tool developers. Availability and implementation This REST API is implemented in Python using the Django REST Framework. It is accessible at http://jaspar.genereg.net/api/ and the source code is freely available at https://bitbucket.org/CBGR/jaspar under GPL v3 license. Contact aziz.khan@ncmm.uio.no or anthony.mathelier@ncmm.uio.no Supplementary informationSupplementary dataare available at Bioinformatics online.",
                        "date": "2018-05-01T00:00:00Z",
                        "citationCount": 11,
                        "authors": [
                            {
                                "name": "Khan A."
                            },
                            {
                                "name": "Mathelier A."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/nar/gkv1176",
                    "pmid": "26531826",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "2016",
                    "note": null,
                    "metadata": {
                        "title": "JASPAR 2016: A major expansion and update of the open-access database of transcription factor binding profiles",
                        "abstract": "JASPAR (http://jaspar.genereg.net) is an open-access database storing curated, non-redundant transcription factor (TF) binding profiles representing transcription factor binding preferences as position frequency matrices for multiple species in six taxonomic groups. For this 2016 release, we expanded the JASPAR CORE collection with 494 new TF binding profiles (315 in vertebrates, 11 in nematodes, 3 in insects, 1 in fungi and 164 in plants) and updated 59 profiles (58 in vertebrates and 1 in fungi). The introduced profiles represent an 83% expansion and 10% update when compared to the previous release. We updated the structural annotation of the TF DNA binding domains (DBDs) following a published hierarchical structural classification. In addition, we introduced 130 transcription factor flexible models trained on ChIP-seq data for vertebrates, which capture dinucleotide dependencies within TF binding sites. This new JASPAR release is accompanied by a new web tool to infer JASPAR TF binding profiles recognized by a given TF protein sequence. Moreover, we provide the users with a Ruby module complementing the JASPAR API to ease programmatic access and use of the JASPAR collection of profiles. Finally, we provide the JASPAR2016 R/Bioconductor data package with the data of this release.",
                        "date": "2016-01-01T00:00:00Z",
                        "citationCount": 717,
                        "authors": [
                            {
                                "name": "Mathelier A."
                            },
                            {
                                "name": "Fornes O."
                            },
                            {
                                "name": "Arenillas D.J."
                            },
                            {
                                "name": "Chen C.-Y."
                            },
                            {
                                "name": "Denay G."
                            },
                            {
                                "name": "Lee J."
                            },
                            {
                                "name": "Shi W."
                            },
                            {
                                "name": "Shyr C."
                            },
                            {
                                "name": "Tan G."
                            },
                            {
                                "name": "Worsley-Hunt R."
                            },
                            {
                                "name": "Zhang A.W."
                            },
                            {
                                "name": "Parcy F."
                            },
                            {
                                "name": "Lenhard B."
                            },
                            {
                                "name": "Sandelin A."
                            },
                            {
                                "name": "Wasserman W.W."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkt997",
                    "pmid": "24194598",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "5.0_ALPHA",
                    "note": null,
                    "metadata": {
                        "title": "JASPAR 2014: An extensively expanded and updated open-access database of transcription factor binding profiles",
                        "abstract": "JASPAR (http://jaspar.genereg.net) is the largest open-access database of matrix-based nucleotide profiles describing the binding preference of transcription factors from multiple species. The fifth major release greatly expands the heart of JASPAR - the JASPAR CORE subcollection, which contains curated, non-redundant profiles - with 135 new curated profiles (74 in vertebrates, 8 in Drosophila melanogaster, 10 in Caenorhabditis elegans and 43 in Arabidopsis thaliana; a 30% increase in total) and 43 older updated profiles (36 in vertebrates, 3 in D. melanogaster and 4 in A. thaliana; a 9% update in total). The new and updated profiles are mainly derived from published chromatin immunoprecipitation-seq experimental datasets. In addition, the web interface has been enhanced with advanced capabilities in browsing, searching and subsetting. Finally, the new JASPAR release is accompanied by a new BioPython package, a new R tool package and a new R/Bioconductor data package to facilitate access for both manual and automated methods. © 2013 The Author(s). Published by Oxford University Press.",
                        "date": "2014-01-01T00:00:00Z",
                        "citationCount": 789,
                        "authors": [
                            {
                                "name": "Mathelier A."
                            },
                            {
                                "name": "Zhao X."
                            },
                            {
                                "name": "Zhang A.W."
                            },
                            {
                                "name": "Parcy F."
                            },
                            {
                                "name": "Worsley-Hunt R."
                            },
                            {
                                "name": "Arenillas D.J."
                            },
                            {
                                "name": "Buchman S."
                            },
                            {
                                "name": "Chen C.-Y."
                            },
                            {
                                "name": "Chou A."
                            },
                            {
                                "name": "Ienasescu H."
                            },
                            {
                                "name": "Lim J."
                            },
                            {
                                "name": "Shyr C."
                            },
                            {
                                "name": "Tan G."
                            },
                            {
                                "name": "Zhou M."
                            },
                            {
                                "name": "Lenhard B."
                            },
                            {
                                "name": "Sandelin A."
                            },
                            {
                                "name": "Wasserman W.W."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkp950",
                    "pmid": "19906716",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "4",
                    "note": null,
                    "metadata": {
                        "title": "JASPAR 2010: The greatly expanded open-access database of transcription factor binding profiles",
                        "abstract": "JASPAR (http://jaspar.genereg.net) is the leading open-access database of matrix profiles describing the DNA-binding patterns of transcription factors (TFs) and other proteins interacting with DNA in a sequence-specific manner. Its fourth major release is the largest expansion of the core database to date: the database now holds 457 non-redundant, curated profiles. The new entries include the first batch of profiles derived from ChIP-seq and ChIP-chip whole-genome binding experiments, and 177 yeast TF binding profiles. The introduction of a yeast division brings the convenience of JASPAR to an active research community. As binding models are refined by newer data, the JASPAR database now uses versioning of matrices: in this release, 12% of the older models were updated to improved versions. Classification of TF families has been improved by adopting a new DNA-binding domain nomenclature. A curated catalog of mammalian TFs is provided, extending the use of the JASPAR profiles to additional TFs belonging to the same structural family. The changes in the database set the system ready for more rapid acquisition of new high-throughput data sources. Additionally, three new special collections provide matrix profile data produced by recent alternative high-throughput approaches. © The Author(s) 2009. Published by Oxford University Press.",
                        "date": "2009-11-10T00:00:00Z",
                        "citationCount": 478,
                        "authors": [
                            {
                                "name": "Portales-Casamar E."
                            },
                            {
                                "name": "Thongjuea S."
                            },
                            {
                                "name": "Kwon A.T."
                            },
                            {
                                "name": "Arenillas D."
                            },
                            {
                                "name": "Zhao X."
                            },
                            {
                                "name": "Valen E."
                            },
                            {
                                "name": "Yusuf D."
                            },
                            {
                                "name": "Lenhard B."
                            },
                            {
                                "name": "Wasserman W.W."
                            },
                            {
                                "name": "Sandelin A."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkm955",
                    "pmid": "18006571",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "3",
                    "note": null,
                    "metadata": {
                        "title": "JASPAR, the open access database of transcription factor-binding profiles: New content and tools in the 2008 update",
                        "abstract": "JASPAR is a popular open-access database for matrix models describing DNA-binding preferences for transcription factors and other DNA patterns. With its third major release, JASPAR has been expanded and equipped with additional functions aimed at both casual and power users. The heart of the JASPAR database - the JASPAR CORE sub-database - has increased by 12% in size, and three new specialized sub-databases have been added. New functions include clustering of matrix models by similarity, generation of random matrices by sampling from selected sets of existing models and a language-independent Web Service applications programming interface for matrix retrieval. JASPAR is available at http://jaspar.genereg.net. © 2007 The Author(s).",
                        "date": "2008-01-01T00:00:00Z",
                        "citationCount": 550,
                        "authors": [
                            {
                                "name": "Bryne J.C."
                            },
                            {
                                "name": "Valen E."
                            },
                            {
                                "name": "Tang M.-H.E."
                            },
                            {
                                "name": "Marstrand T."
                            },
                            {
                                "name": "Winther O."
                            },
                            {
                                "name": "Da piedade I."
                            },
                            {
                                "name": "Krogh A."
                            },
                            {
                                "name": "Lenhard B."
                            },
                            {
                                "name": "Sandelin A."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkj115",
                    "pmid": "16381983",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "2",
                    "note": null,
                    "metadata": null
                },
                {
                    "doi": "10.1093/nar/gkh012",
                    "pmid": "14681366",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1",
                    "note": null,
                    "metadata": null
                }
            ],
            "credit": [
                {
                    "name": "Albin Sandelin",
                    "email": "albin@binf.ku.dk",
                    "url": "https://albinsandelin.wixsite.com/sandelinlab",
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                    "name": "Boris Lenhard",
                    "email": "b.lenhard@imperial.ac.uk",
                    "url": "https://lms.mrc.ac.uk/research-group/computational-regulatory-genomics/",
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                },
                {
                    "name": "Wyeth Wasserman",
                    "email": "wyeth@cmmt.ubc.ca",
                    "url": "https://cmmt.ubc.ca/wasserman-lab/",
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                    "name": "François Parcy",
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                    "name": "Anthony Mathelier",
                    "email": "anthony.mathelier@ncmm.uio.no",
                    "url": "https://mathelierlab.com/",
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                {
                    "name": "The Norwegian Bioinformatics Platform (ELIXIR-Norway) Helpdesk",
                    "email": "support@elixir.no",
                    "url": "https://elixir.no/helpdesk",
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                    "note": "Helpdesk and support for ELIXIR Norway services"
                },
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                    "name": "Tiffany Y Leung",
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                    "name": "Jérémy Lucas",
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                    "name": "Nicolás Manosalva Pérez",
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                    "orcidid": "https://orcid.org/0000-0002-1559-7802",
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                {
                    "name": "Klaas Vandepoele",
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                    "orcidid": "https://orcid.org/0000-0003-4790-2725",
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                    "note": null
                },
                {
                    "name": "The Research Council of Norway",
                    "email": null,
                    "url": "http://www.forskningsradet.no/en/Home_page/1177315753906",
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                    "note": "Curation for bio.tools profile - please contact me with updates/corrections"
                }
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        },
        {
            "name": "LiceBase",
            "description": "LiceBase is a genomics database for sea lice (Lepeophtheirus salmonis and Caligus spp.), which are major pathogens affecting the global salmon farming industry. The database contains the genome annotation of the Atlantic salmon louse (L. salmonis) and is designed to help researchers, industry professionals, and other interested parties access and share information about these parasites, with the goal of developing new treatment methods and tools to ensure a sustainable salmon farming industry. LiceBase provides annotated genome reports, genome browsing, RNA interference (RNAi) experiment browsing for several additional species, BLAST search, and in-situ images. The database homepage posts news updates and recent publications related to sea lice and salmonid species.",
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                            "data": {
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                    "uri": "http://edamontology.org/topic_0091",
                    "term": "Bioinformatics"
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                {
                    "uri": "http://edamontology.org/topic_3302",
                    "term": "Parasitology"
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                {
                    "uri": "http://edamontology.org/topic_3387",
                    "term": "Marine biology"
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                    "uri": "http://edamontology.org/topic_3810",
                    "term": "Agricultural science"
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                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
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                }
            ],
            "documentation": [
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                {
                    "url": "https://licebase.org/?q=node/760957",
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                    "note": null
                },
                {
                    "url": "https://licebase.org/terms_of_use",
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                },
                {
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                    "metadata": {
                        "title": "CHOPCHOP v3: Expanding the CRISPR web toolbox beyond genome editing",
                        "abstract": "The CRISPR-Cas system is a powerful genome editing tool that functions in a diverse array of organisms and cell types. The technology was initially developed to induce targeted mutations in DNA, but CRISPR-Cas has now been adapted to target nucleic acids for a range of purposes. CHOPCHOP is a web tool for identifying CRISPR-Cas single guide RNA (sgRNA) targets. In this major update of CHOPCHOP, we expand our toolbox beyond knockouts. We introduce functionality for targeting RNA with Cas13, which includes support for alternative transcript isoforms and RNA accessibility predictions. We incorporate new DNA targeting modes, including CRISPR activation/repression, targeted enrichment of loci for long-read sequencing, and prediction of Cas9 repair outcomes. Finally, we expand our results page visualization to reveal alternative isoforms and downstream ATG sites, which will aid users in avoiding the expression of truncated proteins. The CHOPCHOP web tool now supports over 200 genomes and we have released a command-line script for running larger jobs and handling unsupported genomes. CHOPCHOP v3 can be found at https://chopchop.cbu.uib.no",
                        "date": "2019-07-01T00:00:00Z",
                        "citationCount": 600,
                        "authors": [
                            {
                                "name": "Labun K."
                            },
                            {
                                "name": "Montague T.G."
                            },
                            {
                                "name": "Krause M."
                            },
                            {
                                "name": "Torres Cleuren Y.N."
                            },
                            {
                                "name": "Tjeldnes H."
                            },
                            {
                                "name": "Valen E."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1002/cpz1.46",
                    "pmid": "33905612",
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": "3",
                    "note": null,
                    "metadata": {
                        "title": "CRISPR Genome Editing Made Easy Through the CHOPCHOP Website",
                        "abstract": "The design of optimal guide RNA (gRNA) sequences for CRISPR systems is challenged by the need to achieve highly efficient editing at the desired location (on-target editing) with minimal editing at unintended locations (off-target editing). Although laboratory validation should ideally be used to detect off-target activity, computational predictions are almost always preferred in practice due to their speed and low cost. Several studies have therefore explored gRNA-DNA interactions in order to understand how CRISPR complexes select their genomic targets. CHOPCHOP (https://chopchop.cbu.uib.no/) leverages these developments to build a user-friendly web interface that helps users design optimal gRNAs. CHOPCHOP supports a wide range of CRISPR applications, including gene knock-out, sequence knock-in, and RNA knock-down. Furthermore, CHOPCHOP offers visualization that enables an informed choice of gRNAs and supports experimental validation. In these protocols, we describe the best practices for gRNA design using CHOPCHOP. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Design of gRNAs for gene knock-out. Alternate Protocol 1: Design of gRNAs for dCas9 fusion/effector targeting. Support Protocol: Design of gRNAs for targeting transgenic or plasmid sequences. Basic Protocol 2: Design of gRNAs for RNA targeting. Basic Protocol 3: Design of gRNAs for sequence knock-in. Alternate Protocol 2: Design of gRNAs for knock-in using non-homologous end joining. Basic Protocol 4: Design of gRNAs for knock-in using Cas9 nickases.",
                        "date": "2021-04-01T00:00:00Z",
                        "citationCount": 12,
                        "authors": [
                            {
                                "name": "Labun K."
                            },
                            {
                                "name": "Krause M."
                            },
                            {
                                "name": "Torres Cleuren Y."
                            },
                            {
                                "name": "Valen E."
                            }
                        ],
                        "journal": "Current Protocols"
                    }
                },
                {
                    "doi": "10.1093/nar/gkw398",
                    "pmid": "27185894",
                    "pmcid": "PMC4987937",
                    "type": [
                        "Primary"
                    ],
                    "version": "2",
                    "note": null,
                    "metadata": {
                        "title": "CHOPCHOP v2: a web tool for the next generation of CRISPR genome engineering",
                        "abstract": "In just 3 years CRISPR genome editing has transformed biology, and its popularity and potency continue to grow. New CRISPR effectors and rules for locating optimum targets continue to be reported, highlighting the need for computational CRISPR targeting tools to compile these rules and facilitate target selection and design. CHOPCHOP is one of the most widely used web tools for CRISPR- and TALEN-based genome editing. Its overarching principle is to provide an intuitive and powerful tool that can serve both novice and experienced users. In this major update we introduce tools for the next generation of CRISPR advances, including Cpf1 and Cas9 nickases. We support a number of new features that improve the targeting power, usability and efficiency of CHOPCHOP. To increase targeting range and specificity we provide support for custom length sgRNAs, and we evaluate the sequence composition of the whole sgRNA and its surrounding region using models compiled from multiple large-scale studies. These and other new features, coupled with an updated interface for increased usability and support for a continually growing list of organisms, maintain CHOPCHOP as one of the leading tools for CRISPR genome editing. CHOPCHOP v2 can be found at http://chopchop.cbu.uib.no.",
                        "date": "2016-07-08T00:00:00Z",
                        "citationCount": 552,
                        "authors": [
                            {
                                "name": "Labun K."
                            },
                            {
                                "name": "Montague T.G."
                            },
                            {
                                "name": "Gagnon J.A."
                            },
                            {
                                "name": "Thyme S.B."
                            },
                            {
                                "name": "Valen E."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gku410",
                    "pmid": "24861617",
                    "pmcid": "PMC4086086",
                    "type": [
                        "Primary"
                    ],
                    "version": "1",
                    "note": null,
                    "metadata": {
                        "title": "CHOPCHOP: A CRISPR/Cas9 and TALEN web tool for genome editing",
                        "abstract": "Major advances in genome editing have recently been made possible with the development of the TALEN and CRISPR/Cas9 methods. The speed and ease of implementing these technologies has led to an explosion of mutant and transgenic organisms. A rate-limiting step in efficiently applying TALEN and CRISPR/Cas9 methods is the selection and design of targeting constructs. We have developed an online tool, CHOPCHOP (https://chopchop.rc.fas.harvard.edu), to expedite the design process. CHOPCHOP accepts a wide range of inputs (gene identifiers, genomic regions or pasted sequences) and provides an array of advanced options for target selection. It uses efficient sequence alignment algorithms to minimize search times, and rigorously predicts off-target binding of single-guide RNAs (sgRNAs) and TALENs. Each query produces an interactive visualization of the gene with candidate target sites displayed at their genomic positions and color-coded according to quality scores. In addition, for each possible target site, restriction sites and primer candidates are visualized, facilitating a streamlined pipeline of mutant generation and validation. The ease-of-use and speed of CHOPCHOP make it a valuable tool for genome engineering. © 2014 The Author(s).",
                        "date": "2014-07-01T00:00:00Z",
                        "citationCount": 736,
                        "authors": [
                            {
                                "name": "Montague T.G."
                            },
                            {
                                "name": "Cruz J.M."
                            },
                            {
                                "name": "Gagnon J.A."
                            },
                            {
                                "name": "Church G.M."
                            },
                            {
                                "name": "Valen E."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Kornel Labun",
                    "email": "kornel.labun@uib.no",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-1262-2611",
                    "gridid": null,
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Developer",
                        "Maintainer",
                        "Support"
                    ],
                    "note": null
                },
                {
                    "name": "Tessa Montague",
                    "email": "tessa.chopchop@gmail.com",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-5918-6327",
                    "gridid": null,
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Contributor",
                        "Support"
                    ],
                    "note": null
                },
                {
                    "name": "Valen Group (Computational Biology Unit)",
                    "email": null,
                    "url": "https://www.cbu.uib.no/valen/",
                    "orcidid": null,
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                    "typeEntity": "Division",
                    "typeRole": [
                        "Developer",
                        "Maintainer",
                        "Support"
                    ],
                    "note": null
                },
                {
                    "name": "The Norwegian Bioinformatics Platform (ELIXIR-Norway) Helpdesk",
                    "email": "support@elixir.no",
                    "url": "https://elixir.no/helpdesk",
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Consortium",
                    "typeRole": [
                        "Support"
                    ],
                    "note": "Helpdesk and support for ELIXIR Norway services"
                },
                {
                    "name": "University of Bergen",
                    "email": null,
                    "url": "https://www.uib.no/en",
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                    "gridid": "grid.7914.b",
                    "rorid": "03zga2b32",
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                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "James Graham",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-8639-7592",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Contributor"
                    ],
                    "note": "Recommendations on the long-read enrichment mode"
                },
                {
                    "name": "Bergen Research Foundation",
                    "email": null,
                    "url": "https://mohnfoundation.no/en/about/",
                    "orcidid": null,
                    "gridid": "grid.478482.5",
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                    "fundrefid": "10.13039/501100006475",
                    "typeEntity": "Funding agency",
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                    "note": "Now part of the Trond Mohn Foundation"
                },
                {
                    "name": "University of Bergen",
                    "email": null,
                    "url": "https://www.uib.no/en",
                    "orcidid": null,
                    "gridid": "grid.7914.b",
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                    "fundrefid": "10.13039/501100005036",
                    "typeEntity": "Funding agency",
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                    "note": null
                },
                {
                    "name": "The Research Council of Norway",
                    "email": null,
                    "url": "http://www.forskningsradet.no/en/Home_page/1177315753906",
                    "orcidid": null,
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                    "name": "Erin Calhoun",
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                    "typeRole": [
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                    ],
                    "note": "Curation of bio.tools profile - contact me with updates/corrections."
                }
            ],
            "community": null,
            "owner": "edv",
            "additionDate": "2016-03-11T10:35:00Z",
            "lastUpdate": "2023-08-03T13:19:40.201053Z",
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}