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        {
            "name": "VarGenius",
            "description": "VarGenius is a platform for analysis of variants from DNA sequencing data. Currently it can be used for WES and Panels. Starting from fastq files it can execute the GATK Best Practices pipeline doing both single calling and joint calling. Then it executes Annovar for variant annotation and generates a readable output in tabular and XLS format. All the data extracted from the samples (variants, genotypes, etc..) are uploaded into a Postgres database which can be used for further downstream analyses.",
            "homepage": "https://github.com/frankMusacchia/VarGenius",
            "biotoolsID": "VarGenius",
            "biotoolsCURIE": "biotools:VarGenius",
            "version": [
                "1.0"
            ],
            "otherID": [],
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                {
                    "biotoolsID": "vargenius-hzd",
                    "type": "includes"
                }
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            "function": [],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2533",
                    "term": "DNA mutation"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
            "language": [
                "R",
                "Perl"
            ],
            "license": null,
            "collectionID": [],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [
                "Tools"
            ],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://groups.google.com/forum/#!forum/VarGenius",
                    "type": [
                        "Helpdesk"
                    ],
                    "note": null
                }
            ],
            "download": [
                {
                    "url": "https://github.com/frankMusacchia/VarGenius",
                    "type": "Source code",
                    "note": null,
                    "version": "1.0"
                }
            ],
            "documentation": [
                {
                    "url": "https://github.com/frankMusacchia/VarGenius/tree/master/GUIDE",
                    "type": [
                        "Installation instructions"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/s12859-018-2532-4",
                    "pmid": "30541431",
                    "pmcid": "PMC6291943",
                    "type": [
                        "Primary",
                        "Benchmarking study"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "VarGenius executes cohort-level DNA-seq variant calling and annotation and allows to manage the resulting data through a PostgreSQL database",
                        "abstract": "© 2018 The Author(s).Background: Targeted resequencing has become the most used and cost-effective approach for identifying causative mutations of Mendelian diseases both for diagnostics and research purposes. Due to very rapid technological progress, NGS laboratories are expanding their capabilities to address the increasing number of analyses. Several open source tools are available to build a generic variant calling pipeline, but a tool able to simultaneously execute multiple analyses, organize, and categorize the samples is still missing. Results: Here we describe VarGenius, a Linux based command line software able to execute customizable pipelines for the analysis of multiple targeted resequencing data using parallel computing. VarGenius provides a database to store the output of the analysis (calling quality statistics, variant annotations, internal allelic variant frequencies) and sample information (personal data, genotypes, phenotypes). VarGenius can also perform the \"joint analysis\" of hundreds of samples with a single command, drastically reducing the time for the configuration and execution of the analysis. VarGenius executes the standard pipeline of the Genome Analysis Tool-Kit (GATK) best practices (GBP) for germinal variant calling, annotates the variants using Annovar, and generates a user-friendly output displaying the results through a web page. VarGenius has been tested on a parallel computing cluster with 52 machines with 120GB of RAM each. Under this configuration, a 50 M whole exome sequencing (WES) analysis for a family was executed in about 7h (trio or quartet); a joint analysis of 30 WES in about 24 h and the parallel analysis of 34 single samples from a 1 M panel in about 2 h. Conclusions: We developed VarGenius, a \"master\" tool that faces the increasing demand of heterogeneous NGS analyses and allows maximum flexibility for downstream analyses. It paves the way to a different kind of analysis, centered on cohorts rather than on singleton. Patient and variant information are stored into the database and any output file can be accessed programmatically. VarGenius can be used for routine analyses by biomedical researchers with basic Linux skills providing additional flexibility for computational biologists to develop their own algorithms for the comparison and analysis of data. The software is freely available at: https://github.com/frankMusacchia/VarGenius",
                        "date": "2018-12-12T00:00:00Z",
                        "citationCount": 8,
                        "authors": [
                            {
                                "name": "Musacchia F."
                            },
                            {
                                "name": "Ciolfi A."
                            },
                            {
                                "name": "Mutarelli M."
                            },
                            {
                                "name": "Bruselles A."
                            },
                            {
                                "name": "Castello R."
                            },
                            {
                                "name": "Pinelli M."
                            },
                            {
                                "name": "Basu S."
                            },
                            {
                                "name": "Banfi S."
                            },
                            {
                                "name": "Casari G."
                            },
                            {
                                "name": "Tartaglia M."
                            },
                            {
                                "name": "Nigro V."
                            },
                            {
                                "name": "Torella A."
                            },
                            {
                                "name": "Esposito G."
                            },
                            {
                                "name": "Cappuccio G."
                            },
                            {
                                "name": "Mancano G."
                            },
                            {
                                "name": "Maitz S."
                            },
                            {
                                "name": "Brunetti-Pierri N."
                            },
                            {
                                "name": "Parenti G."
                            },
                            {
                                "name": "Selicorni A."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-TELETHON",
                    "email": "f.musacchia@tigem.it",
                    "url": null,
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                    ],
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                }
            ],
            "community": null,
            "owner": "FrankMusacchia",
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        },
        {
            "name": "AraQTL",
            "description": "AraQTL is the A.thaliana workbench and database for eQTL analysis.\nIt allows easy access to the data of all published Arabidopsis thaliana genetical genomics experiments.",
            "homepage": "https://www.bioinformatics.nl/AraQTL",
            "biotoolsID": "araqtl",
            "biotoolsCURIE": "biotools:araqtl",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3232",
                            "term": "Gene expression QTL analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0282",
                            "term": "Genetic mapping"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0224",
                            "term": "Query and retrieval"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3360",
                    "term": "Biomarkers"
                },
                {
                    "uri": "http://edamontology.org/topic_3517",
                    "term": "GWAS study"
                },
                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
                }
            ],
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            "publication": [
                {
                    "doi": "10.1111/tpj.13457",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "AraQTL – workbench and archive for systems genetics in Arabidopsis thaliana",
                        "abstract": "© 2016 The Authors. The Plant Journal published by John Wiley & Sons Ltd and Society for Experimental Biology.Genetical genomics studies uncover genome-wide genetic interactions between genes and their transcriptional regulators. High-throughput measurement of gene expression in recombinant inbred line populations has enabled investigation of the genetic architecture of variation in gene expression. This has the potential to enrich our understanding of the molecular mechanisms affected by and underlying natural variation. Moreover, it contributes to the systems biology of natural variation, as a substantial number of experiments have resulted in a valuable amount of interconnectable phenotypic, molecular and genotypic data. A number of genetical genomics studies have been published for Arabidopsis thaliana, uncovering many expression quantitative trait loci (eQTLs). However, these complex data are not easily accessible to the plant research community, leaving most of the valuable genetic interactions unexplored as cross-analysis of these studies is a major effort. We address this problem with AraQTL (http://www.bioinformatics.nl/Ara QTL/), an easily accessible workbench and database for comparative analysis and meta-analysis of all published Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using and extending upon the results of these experiments. For example, one can easily screen a physical region for specific local eQTLs that could harbour candidate genes for phenotypic QTLs, or detect gene-by-environment interactions by comparing eQTLs under different conditions.",
                        "date": "2017-03-01T00:00:00Z",
                        "citationCount": 8,
                        "authors": [
                            {
                                "name": "Nijveen H."
                            },
                            {
                                "name": "Ligterink W."
                            },
                            {
                                "name": "Keurentjes J.J.B."
                            },
                            {
                                "name": "Loudet O."
                            },
                            {
                                "name": "Long J."
                            },
                            {
                                "name": "Sterken M.G."
                            },
                            {
                                "name": "Prins P."
                            },
                            {
                                "name": "Hilhorst H.W."
                            },
                            {
                                "name": "de Ridder D."
                            },
                            {
                                "name": "Kammenga J.E."
                            },
                            {
                                "name": "Snoek B.L."
                            }
                        ],
                        "journal": "Plant Journal"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Harm Nijveen",
                    "email": "harm.nijveen@wur.nl",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-9167-4945",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "Wilco Ligterink",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0228-169X",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Joost J. B. Keurentjes",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-8918-0711",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Olivier Loudet",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-3717-0137",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Jiao Long",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Mark G. Sterken",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-7119-6213",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Pjotr Prins",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Henk W. Hilhorst",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-6743-583X",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Dick de Ridder",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-4944-4310",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Jan E. Kammenga",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Basten L. Snoek",
                    "email": "basten.snoek@wur.nl",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                }
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            "owner": "marieBvr",
            "additionDate": "2021-03-23T16:13:44Z",
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        },
        {
            "name": "metaQuantome",
            "description": "metaQuantome software suite analyzes the state of a microbiome by leveraging complex taxonomic and functional hierarchies to summarize peptide-level quantitative information. metaQuantome offers differential abundance analysis, principal components analysis, and clustered heat map visualizations, as well as exploratory analysis for a single sample or experimental condition.",
            "homepage": "https://github.com/galaxyproteomics/metaquantome",
            "biotoolsID": "metaQuantome",
            "biotoolsCURIE": "biotools:metaQuantome",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2939",
                            "term": "Principal component visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3459",
                            "term": "Functional clustering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0224",
                            "term": "Query and retrieval"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3741",
                            "term": "Differential protein expression analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0531",
                            "term": "Heat map generation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3799",
                            "term": "Quantification"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0227",
                            "term": "Indexing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3695",
                            "term": "Filtering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3658",
                            "term": "Statistical inference"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0945",
                                "term": "Peptide identification"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2603",
                                "term": "Expression data"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1872",
                                "term": "Taxonomic classification"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2583",
                                "term": "GO concept ID (molecular function)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1011",
                                "term": "EC number"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2583",
                                "term": "GO concept ID (molecular function)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3547",
                                    "term": "Image format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2603",
                                "term": "Expression data"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3547",
                                    "term": "Image format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3028",
                                "term": "Taxonomy"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3547",
                                    "term": "Image format"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Suite"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3941",
                    "term": "Metatranscriptomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3697",
                    "term": "Microbial ecology"
                },
                {
                    "uri": "http://edamontology.org/topic_3520",
                    "term": "Proteomics experiment"
                },
                {
                    "uri": "http://edamontology.org/topic_3174",
                    "term": "Metagenomics"
                }
            ],
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            "publication": [
                {
                    "doi": "10.1074/mcp.RA118.001240",
                    "pmid": "31235611",
                    "pmcid": "PMC6692774",
                    "type": [
                        "Method"
                    ],
                    "version": "1.0",
                    "note": "https://www.mcponline.org/content/18/8_suppl_1/S82",
                    "metadata": {
                        "title": "MetaQuantome: An integrated, quantitative metaproteomics approach reveals connections between taxonomy and protein function in complex microbiomes",
                        "abstract": "© 2019 Easterly et al.Microbiome research offers promising insights into the impact of microorganisms on biological systems. Metaproteomics, the study of microbial proteins at the community level, integrates genomic, transcriptomic, and proteomic data to determine the taxonomic and functional state of a microbiome. However, standard metaproteomics software is subject to several limitations, commonly supporting only spectral counts, emphasizing exploratory analysis rather than hypothesis testing and rarely offering the ability to analyze the interaction of function and taxonomy - that is, which taxa are responsible for different processes. Here we present metaQuantome, a novel, multifaceted software suite that analyzes the state of a microbiome by leveraging complex taxonomic and functional hierarchies to summarize peptide-level quantitative information, emphasizing label-free intensity-based methods. For experiments with multiple experimental conditions, metaQuantome offers differential abundance analysis, principal components analysis, and clustered heat map visualizations, as well as exploratory analysis for a single sample or experimental condition. We benchmark meta- Quantome analysis against standard methods, using two previously published datasets: (1) an artificially assembled microbial community dataset (taxonomy benchmarking) and (2) a dataset with a range of recombinant human proteins spiked into an Escherichia coli background (functional benchmarking). Furthermore, we demonstrate the use of metaQuantome on a previously published human oral microbiome dataset. In both the taxonomic and functional benchmarking analyses, metaQuantome quantified taxonomic and functional terms more accurately than standard summarization- based methods. We use the oral microbiome dataset to demonstrate metaQuantome's ability to produce publication- quality figures and elucidate biological processes of the oral microbiome. metaQuantome enables advanced investigation of metaproteomic datasets, which should be broadly applicable to microbiome-related research. In the interest of accessible, flexible, and reproducible analysis, metaQuantome is open source and available on the command line and in Galaxy.",
                        "date": "2019-01-01T00:00:00Z",
                        "citationCount": 11,
                        "authors": [
                            {
                                "name": "Easterly C.W."
                            },
                            {
                                "name": "Sajulga R."
                            },
                            {
                                "name": "Mehta S."
                            },
                            {
                                "name": "Johnson J."
                            },
                            {
                                "name": "Kumar P."
                            },
                            {
                                "name": "Hubler S."
                            },
                            {
                                "name": "Mesuere B."
                            },
                            {
                                "name": "Rudney J."
                            },
                            {
                                "name": "Griffin T.J."
                            },
                            {
                                "name": "Jagtap P.D."
                            }
                        ],
                        "journal": "Molecular and Cellular Proteomics"
                    }
                },
                {
                    "doi": "10.1021/ACS.JPROTEOME.0C00960",
                    "pmid": "33683127",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Updates on metaQuantome Software for Quantitative Metaproteomics",
                        "abstract": "© 2021 American Chemical Society.metaQuantome is a software suite that enables the quantitative analysis, statistical evaluation. and visualization of mass-spectrometry-based metaproteomics data. In the latest update of this software, we have provided several extensions, including a step-by-step training guide, the ability to perform statistical analysis on samples from multiple conditions, and a comparative analysis of metatranscriptomics data. The training module, accessed via the Galaxy Training Network, will help users to use the suite effectively both for functional as well as for taxonomic analysis. We extend the ability of metaQuantome to now perform multi-data-point quantitative and statistical analyses so that studies with measurements across multiple conditions, such as time-course studies, can be analyzed. With an eye on the multiomics analysis of microbial communities, we have also initiated the use of metaQuantome statistical and visualization tools on outputs from metatranscriptomics data, which complements the metagenomic and metaproteomic analyses already available. For this, we have developed a tool named MT2MQ (\"metatranscriptomics to metaQuantome\"), which takes in outputs from the ASaiM metatranscriptomics workflow and transforms them so that the data can be used as an input for comparative statistical analysis and visualization via metaQuantome. We believe that these improvements to metaQuantome will facilitate the use of the software for quantitative metaproteomics and metatranscriptomics and will enable multipoint data analysis. These improvements will take us a step toward integrative multiomic microbiome analysis so as to understand dynamic taxonomic and functional responses of these complex systems in a variety of biological contexts. The updated metaQuantome and MT2MQ are open-source software and are available via the Galaxy Toolshed and GitHub.",
                        "date": "2021-04-02T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Mehta S."
                            },
                            {
                                "name": "Kumar P."
                            },
                            {
                                "name": "Crane M."
                            },
                            {
                                "name": "Johnson J.E."
                            },
                            {
                                "name": "Sajulga R."
                            },
                            {
                                "name": "Nguyen D.D.A."
                            },
                            {
                                "name": "McGowan T."
                            },
                            {
                                "name": "Arntzen M."
                            },
                            {
                                "name": "Griffin T.J."
                            },
                            {
                                "name": "Jagtap P.D."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                }
            ],
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            "owner": "pjagtap",
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        },
        {
            "name": "PredictSNP",
            "description": "A consensus classifier that combines six of the top performing tools for the prediction of the effects of mutation on protein function. The obtained results are provided together with annotations extracted from the Protein Mutant Database and the UniProt database.",
            "homepage": "https://loschmidt.chemi.muni.cz/predictsnp1",
            "biotoolsID": "predictsnp",
            "biotoolsCURIE": "biotools:predictsnp",
            "version": [
                "1.0"
            ],
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            "relation": [
                {
                    "biotoolsID": "mapp",
                    "type": "uses"
                },
                {
                    "biotoolsID": "nssnpanalyzer",
                    "type": "uses"
                },
                {
                    "biotoolsID": "panther",
                    "type": "uses"
                },
                {
                    "biotoolsID": "phd-snp",
                    "type": "uses"
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                    "biotoolsID": "polyphen",
                    "type": "uses"
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                {
                    "biotoolsID": "polyphen-2",
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                {
                    "biotoolsID": "sift",
                    "type": "uses"
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                {
                    "biotoolsID": "snap",
                    "type": "uses"
                }
            ],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0331",
                            "term": "Variant effect prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2291",
                                "term": "UniProt ID"
                            },
                            "format": []
                        }
                    ],
                    "note": "Prediction of the effect of amino acid substitution on protein function",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Web application"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_3063",
                    "term": "Medical informatics"
                },
                {
                    "uri": "http://edamontology.org/topic_3325",
                    "term": "Rare diseases"
                },
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
                {
                    "uri": "http://edamontology.org/topic_3053",
                    "term": "Genetics"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "JavaScript",
                "Java",
                "Python"
            ],
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            "collectionID": [
                "Czech Republic",
                "Rare Disease",
                "ELIXIR-CZ"
            ],
            "maturity": "Mature",
            "cost": "Free of charge (with restrictions)",
            "accessibility": "Open access",
            "elixirPlatform": [
                "Tools"
            ],
            "elixirNode": [
                "Czech Republic"
            ],
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            "link": [],
            "download": [
                {
                    "url": "https://loschmidt.chemi.muni.cz/predictsnp1/docs/predictsnp-1.0.tar.gz",
                    "type": "Binaries",
                    "note": null,
                    "version": null
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            ],
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                {
                    "url": "https://loschmidt.chemi.muni.cz/predictsnp1/docs/USER_GUIDE.pdf",
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            "publication": [
                {
                    "doi": "10.1371/journal.pcbi.1003440",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
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                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations",
                        "abstract": "Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. © 2014 Bendl et al.",
                        "date": "2014-01-01T00:00:00Z",
                        "citationCount": 320,
                        "authors": [
                            {
                                "name": "Bendl J."
                            },
                            {
                                "name": "Stourac J."
                            },
                            {
                                "name": "Salanda O."
                            },
                            {
                                "name": "Pavelka A."
                            },
                            {
                                "name": "Wieben E.D."
                            },
                            {
                                "name": "Zendulka J."
                            },
                            {
                                "name": "Brezovsky J."
                            },
                            {
                                "name": "Damborsky J."
                            }
                        ],
                        "journal": "PLoS Computational Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Jaroslav Bendl",
                    "email": "jaroslav.bendl@gmail.com",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-9989-2720",
                    "gridid": null,
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                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "Jan Stourac",
                    "email": "stourac.jan@gmail.com",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-3139-3700",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer",
                        "Support"
                    ],
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                },
                {
                    "name": "Ondrej Salanda",
                    "email": null,
                    "url": null,
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                    "typeEntity": "Person",
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                },
                {
                    "name": "Jan Brezovsky",
                    "email": "brezovsky@mail.muni.cz",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-9677-5078",
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                {
                    "name": "Jiri Damborsky",
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                {
                    "name": "Eric D. Wieben",
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                    "name": "Jaroslav Zendulka",
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                    "name": "Antonin Pavelka",
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                    "url": "https://www.muni.cz/",
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                {
                    "name": "International Centre for Clinical Research, Brno, Czech Republic",
                    "email": null,
                    "url": "https://www.fnusa-icrc.org/en/",
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                    "name": "Brno University of Technology, Brno, Czech Republic",
                    "email": null,
                    "url": "https://www.vutbr.cz/",
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                {
                    "name": "Mayo Clinic, Rochester, New York, United States of America",
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                    "url": "https://www.mayoclinic.org/",
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            "owner": "Loschmidt Laboratories",
            "additionDate": "2015-11-07T20:40:08Z",
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        },
        {
            "name": "CaverDock",
            "description": "Performs rapid analysis of transport processes in proteins. It models the transportation of a ligand from outside environment into the protein active or binding site and vice versa. It implements a novel algorithm to produce contiguous ligand trajectory and estimation of a binding energy along the pathway. The current version uses CAVER for pathway identification and heavily modified Autodock Vina as a docking engine.",
            "homepage": "https://loschmidt.chemi.muni.cz/caverdock/",
            "biotoolsID": "caverdock",
            "biotoolsCURIE": "biotools:caverdock",
            "version": [
                "1.0",
                "1.1"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0482",
                            "term": "Protein-ligand docking"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1460",
                                "term": "Protein structure"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1476",
                                    "term": "PDB"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1463",
                                "term": "Small molecule structure"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1476",
                                    "term": "PDB"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1537",
                                "term": "Protein structure report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1957",
                                    "term": "raw"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1476",
                                    "term": "PDB"
                                }
                            ]
                        }
                    ],
                    "note": "trajectory of ligand passing through protein tunnel and its energy",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2814",
                    "term": "Protein structure analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
                },
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "C++",
                "Python"
            ],
            "license": "Proprietary",
            "collectionID": [
                "CAVER",
                "ELIXIR-CZ"
            ],
            "maturity": "Mature",
            "cost": "Free of charge (with restrictions)",
            "accessibility": null,
            "elixirPlatform": [
                "Tools"
            ],
            "elixirNode": [
                "Czech Republic"
            ],
            "elixirCommunity": [
                "3D-BioInfo"
            ],
            "link": [],
            "download": [
                {
                    "url": "https://www.fi.muni.cz/~xfilipov/caverdock/caverdock-ubuntu-14.04.tar.gz",
                    "type": "Binaries",
                    "note": "v1.0, Ubuntu 14.04",
                    "version": "1.0"
                },
                {
                    "url": "https://www.fi.muni.cz/~xfilipov/caverdock/caverdock-ubuntu-16.04.tar.gz",
                    "type": "Binaries",
                    "note": "v1.0, Ubuntu 16.04",
                    "version": "1.0"
                },
                {
                    "url": "https://www.fi.muni.cz/~xfilipov/caverdock/caverdock-1.1-ubuntu-16.04.tar.xz",
                    "type": "Binaries",
                    "note": "v1.1, Ubuntu 16.04",
                    "version": "1.1"
                },
                {
                    "url": "https://www.fi.muni.cz/~xfilipov/caverdock/caverdock-1.1-ubuntu-18.04.tar.xz",
                    "type": "Binaries",
                    "note": "v1.1, Ubuntu 18.04",
                    "version": "1.1"
                }
            ],
            "documentation": [
                {
                    "url": "https://www.fi.muni.cz/~xfilipov/caverdock/manual.pdf",
                    "type": [
                        "User manual"
                    ],
                    "note": "version 1.0"
                },
                {
                    "url": "https://www.fi.muni.cz/~xfilipov/caverdock/manual-1.1.pdf",
                    "type": [
                        "User manual"
                    ],
                    "note": "version 1.1"
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btz386",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "CaverDock: A molecular docking-based tool to analyse ligand transport through protein tunnels and channels",
                        "abstract": "© 2019 The Author(s).Motivation: Protein tunnels and channels are key transport pathways that allow ligands to pass between proteins' external and internal environments. These functionally important structural features warrant detailed attention. It is difficult to study the ligand binding and unbinding processes experimentally, while molecular dynamics simulations can be time-consuming and computationally demanding. Results: CaverDock is a new software tool for analysing the ligand passage through the biomolecules. The method uses the optimized docking algorithm of AutoDock Vina for ligand placement docking and implements a parallel heuristic algorithm to search the space of possible trajectories. The duration of the simulations takes from minutes to a few hours. Here we describe the implementation of the method and demonstrate CaverDock's usability by: (i) comparison of the results with other available tools, (ii) determination of the robustness with large ensembles of ligands and (iii) the analysis and comparison of the ligand trajectories in engineered tunnels. Thorough testing confirms that CaverDock is applicable for the fast analysis of ligand binding and unbinding in fundamental enzymology and protein engineering. Availability and implementation: User guide and binaries for Ubuntu are freely available for non-commercial use at https://loschmidt.chemi.muni.cz/caverdock/. The web implementation is available at https://loschmidt.chemi.muni.cz/caverweb/. The source code is available upon request. Supplementary information: Supplementary data are available at Bioinformatics online.",
                        "date": "2019-12-01T00:00:00Z",
                        "citationCount": 14,
                        "authors": [
                            {
                                "name": "Vavra O."
                            },
                            {
                                "name": "Filipovic J."
                            },
                            {
                                "name": "Plhak J."
                            },
                            {
                                "name": "Bednar D."
                            },
                            {
                                "name": "Marques S.M."
                            },
                            {
                                "name": "Brezovsky J."
                            },
                            {
                                "name": "Stourac J."
                            },
                            {
                                "name": "Matyska L."
                            },
                            {
                                "name": "Damborsky J."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1109/TCBB.2019.2907492",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "CaverDock: A Novel Method for the Fast Analysis of Ligand Transport",
                        "abstract": "Here we present a novel method for the analysis of transport processes in proteins and its implementation called CaverDock. Our method is based on a modified molecular docking algorithm. It iteratively places the ligand along the access tunnel in such a way that the ligand movement is contiguous and the energy is minimized. The result of CaverDock calculation is a ligand trajectory and an energy profile of transport process. CaverDock uses the modified docking program Autodock Vina for molecular docking and implements a parallel heuristic algorithm for searching the space of possible trajectories. Our method lies in between the geometrical approaches and molecular dynamics simulations. Contrary to the geometrical methods, it provides an evaluation of chemical forces. However, it is far less computationally demanding and easier to set up compared to molecular dynamics simulations. CaverDock will find a broad use in the fields of computational enzymology, drug design, and protein engineering. The software is available free of charge to the academic users at https://loschmidt.chemi.muni.cz/caverdock/.",
                        "date": "2020-09-01T00:00:00Z",
                        "citationCount": 8,
                        "authors": [
                            {
                                "name": "Filipovic J."
                            },
                            {
                                "name": "Vavra O."
                            },
                            {
                                "name": "Plhak J."
                            },
                            {
                                "name": "Bednar D."
                            },
                            {
                                "name": "Marques S.M."
                            },
                            {
                                "name": "Brezovsky J."
                            },
                            {
                                "name": "Matyska L."
                            },
                            {
                                "name": "Damborsky J."
                            }
                        ],
                        "journal": "IEEE/ACM transactions on computational biology and bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Jiri Filipovic",
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                    "name": "Ondrej Vavra",
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                {
                    "name": "Jan Plhak",
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                    "name": "David Bednar",
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                    "name": "Sergio Marques",
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        },
        {
            "name": "PredictSNP2",
            "description": "A consensus classifier that combines five of the top performing tools (CADD, DANN, FATHMM, FunSeq2 and GWAVA) for the evaluation of pathogenic effect of SNPs within the human genome. The obtained results are provided together with annotations extracted from dbSNP, GenBank, Clinvar, OMIM, RegulomeDB, HaploReg, UCSC and Ensembl databases.",
            "homepage": "https://loschmidt.chemi.muni.cz/predictsnp2",
            "biotoolsID": "predictsnp2",
            "biotoolsCURIE": "biotools:predictsnp2",
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                {
                    "operation": [
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                            "uri": "http://edamontology.org/operation_3672",
                            "term": "Gene functional annotation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3225",
                            "term": "Variant classification"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3498",
                                "term": "Sequence variations"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3019",
                                    "term": "GVF"
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                                {
                                    "uri": "http://edamontology.org/format_3016",
                                    "term": "VCF"
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                            ]
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3498",
                                "term": "Sequence variations"
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                                {
                                    "uri": "http://edamontology.org/format_3016",
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                    ],
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            ],
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                    "term": "Medical informatics"
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                    "term": "DNA mutation"
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                    "term": "Rare diseases"
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            ],
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                "Linux",
                "Windows",
                "Mac"
            ],
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                "Java"
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                "Rare Disease",
                "ELIXIR-CZ"
            ],
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            "cost": "Free of charge (with restrictions)",
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            "download": [
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                    "url": "https://loschmidt.chemi.muni.cz/peg/software/predictsnp2-standalone/",
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                        "title": "PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions",
                        "abstract": "© 2016 Bendl et al.An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To enable comprehensive evaluation of variants, the predictions are complemented with annotations from eight databases. The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.",
                        "date": "2016-05-01T00:00:00Z",
                        "citationCount": 78,
                        "authors": [
                            {
                                "name": "Bendl J."
                            },
                            {
                                "name": "Musil M."
                            },
                            {
                                "name": "Stourac J."
                            },
                            {
                                "name": "Zendulka J."
                            },
                            {
                                "name": "Damborsky J."
                            },
                            {
                                "name": "Brezovsky J."
                            }
                        ],
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                }
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                    "name": "Jaroslav Bendl",
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                        "title": "ARADEEPOPSIS, an automated workflow for top-view plant phenomics using semantic segmentation of leaf States",
                        "abstract": "© 2020 The authors.Linking plant phenotype to genotype is a common goal to both plant breeders and geneticists. However, collecting phenotypic data for large numbers of plants remain a bottleneck. Plant phenotyping is mostly image based and therefore requires rapid and robust extraction of phenotypic measurements from image data. However, because segmentation tools usually rely on color information, they are sensitive to background or plant color deviations. We have developed a versatile, fully open-source pipeline to extract phenotypic measurements from plant images in an unsupervised manner. ARADEEPOPSIS (https://github.com/Gregor-Mendel-Institute/aradeepopsis) uses semantic segmentation of top-view images to classify leaf tissue into three categories: healthy, anthocyanin rich, and senescent. This makes it particularly powerful at quantitative phenotyping of different developmental stages, mutants with aberrant leaf color and/or phenotype, and plants growing in stressful conditions. On a panel of 210 natural Arabidopsis (Arabidopsis thaliana) accessions, we were able to not only accurately segment images of phenotypically diverse genotypes but also to identify known loci related to anthocyanin production and early necrosis in genome-wide association analyses. Our pipeline accurately processed images of diverse origin, quality, and background composition, and of a distantly related Brassicaceae. ARADEEPOPSIS is deployable on most operating systems and high-performance computing environments and can be used independently of bioinformatics expertise and resources.",
                        "date": "2020-12-01T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Huther P."
                            },
                            {
                                "name": "Schandry N."
                            },
                            {
                                "name": "Jandrasits K."
                            },
                            {
                                "name": "Bezrukov I."
                            },
                            {
                                "name": "Becker C."
                            }
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                        "journal": "Plant Cell"
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            "description": "An alignment-free method capable of processing and counting k-mers in a reasonable time, while evaluating multiple values of the k parameter concurrently.",
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            "homepage": "http://www.pickle.gr",
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                            "term": "Sorting"
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1009",
                                "term": "Protein name"
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                            "data": {
                                "uri": "http://edamontology.org/data_1025",
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                    "term": "Systems biology"
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                    "term": "Human biology"
                },
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                    "uri": "http://edamontology.org/topic_0128",
                    "term": "Protein interactions"
                },
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                    "uri": "http://edamontology.org/topic_3574",
                    "term": "Human genetics"
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            "link": [
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                    "type": [
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                    "note": "Content available to any user with no restrictions\nCopyright: University of Patras & FORTH/ICE-HT, Greece"
                }
            ],
            "download": [
                {
                    "url": "http://www.pickle.gr/Downloads",
                    "type": "Biological data",
                    "note": "The current and archived PICKLE PPI datasets at the UniProt and gene levels can be freely downloaded from the PICKLE webpage.",
                    "version": "active and archived versions"
                },
                {
                    "url": "http://www.pickle.gr/Downloads",
                    "type": "Biological data",
                    "note": "The current and archived PICKLE genetic information ontology networks are available in owl format.",
                    "version": "active and archived versions"
                }
            ],
            "documentation": [
                {
                    "url": "http://www.pickle.gr",
                    "type": [
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                    "note": "The web portal of the meta-database"
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            "publication": [
                {
                    "doi": "10.1186/1752-0509-7-96",
                    "pmid": "24088582",
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                    "metadata": {
                        "title": "Reconstruction of the experimentally supported human protein interactome: What can we learn?",
                        "abstract": "Background: Understanding the topology and dynamics of the human protein-protein interaction (PPI) network will significantly contribute to biomedical research, therefore its systematic reconstruction is required. Several meta-databases integrate source PPI datasets, but the protein node sets of their networks vary depending on the PPI data combined. Due to this inherent heterogeneity, the way in which the human PPI network expands via multiple dataset integration has not been comprehensively analyzed. We aim at assembling the human interactome in a global structured way and exploring it to gain insights of biological relevance. Results: First, we defined the UniProtKB manually reviewed human \" complete\" proteome as the reference protein-node set and then we mined five major source PPI datasets for direct PPIs exclusively between the reference proteins. We updated the protein and publication identifiers and normalized all PPIs to the UniProt identifier level. The reconstructed interactome covers approximately 60% of the human proteome and has a scale-free structure. No apparent differentiating gene functional classification characteristics were identified for the unrepresented proteins. The source dataset integration augments the network mainly in PPIs. Polyubiquitin emerged as the highest-degree node, but the inclusion of most of its identified PPIs may be reconsidered. The high number (>300) of connections of the subsequent fifteen proteins correlates well with their essential biological role. According to the power-law network structure, the unrepresented proteins should mainly have up to four connections with equally poorly-connected interactors. Conclusions: Reconstructing the human interactome based on the a priori definition of the protein nodes enabled us to identify the currently included part of the human \" complete\" proteome, and discuss the role of the proteins within the network topology with respect to their function. As the network expansion has to comply with the scale-free theory, we suggest that the core of the human interactome has essentially emerged. Thus, it could be employed in systems biology and biomedical research, despite the considerable number of currently unrepresented proteins. The latter are probably involved in specialized physiological conditions, justifying the scarcity of related PPI information, and their identification can assist in designing relevant functional experiments and targeted text mining algorithms. © 2013 Klapa et al.; licensee BioMed Central Ltd.",
                        "date": "2013-10-02T00:00:00Z",
                        "citationCount": 15,
                        "authors": [
                            {
                                "name": "Klapa M.I."
                            },
                            {
                                "name": "Tsafou K."
                            },
                            {
                                "name": "Theodoridis E."
                            },
                            {
                                "name": "Tsakalidis A."
                            },
                            {
                                "name": "Moschonas N.K."
                            }
                        ],
                        "journal": "BMC Systems Biology"
                    }
                },
                {
                    "doi": "10.1371/journal.pone.0186039",
                    "pmid": "29023571",
                    "pmcid": null,
                    "type": [
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                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology",
                        "abstract": "© 2017 Gioutlakis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.",
                        "date": "2017-10-01T00:00:00Z",
                        "citationCount": 20,
                        "authors": [
                            {
                                "name": "Gioutlakis A."
                            },
                            {
                                "name": "Klapa M.I."
                            },
                            {
                                "name": "Moschonas N.K."
                            }
                        ],
                        "journal": "PLoS ONE"
                    }
                },
                {
                    "doi": "10.13140/RG.2.2.16985.21608",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": "Poster Presentation in the ELIXIR All Hands Meeting",
                    "metadata": null
                },
                {
                    "doi": "10.1093/bioinformatics/btaa1070",
                    "pmid": "33367505",
                    "pmcid": "PMC8034533",
                    "type": [
                        "Method"
                    ],
                    "version": "3.0",
                    "note": "The PICKLE 3.0 upgrade refers to the enrichment of this human protein–protein interaction (PPI) meta-database with the mouse protein interactome.",
                    "metadata": null
                }
            ],
            "credit": [
                {
                    "name": "Nicholas Moschonas",
                    "email": "n_moschonas@med.upatras.gr",
                    "url": "http://www.pickle.gr",
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                {
                    "name": "Maria Klapa",
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        {
            "name": "TRIP - T-cell Receptor Immunoglobulin Profiler",
            "description": "Antigen receptors are characterized by an extreme diversity of specificities, which poses major computational and analytical challenges, particularly in the era of high-throughput immunoprofiling by next generation sequencing (NGS). The antigen receptor gene profiler tool (TRIP) offers the opportunity for an in-depth analysis based on the processing of the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling, through a number of interoperable modules. These  provide  detailed  information about antigen receptor gene rearrangements, including variable (V), diversity (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins (BcR IG), and characteristics of the somatic hypermutation within the BcR IG genes. TRIP is a web application implemented in R shiny.",
            "homepage": "https://github.com/BiodataAnalysisGroup/TRIP",
            "biotoolsID": "TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler",
            "biotoolsCURIE": "biotools:TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler",
            "version": [
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            "function": [
                {
                    "operation": [
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                            "uri": "http://edamontology.org/operation_2403",
                            "term": "Sequence analysis"
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            "license": "MIT",
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            "maturity": "Mature",
            "cost": "Free of charge",
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            "elixirNode": [
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            "publication": [
                {
                    "doi": "10.1186/s12859-020-03669-1",
                    "pmid": "32993478",
                    "pmcid": "PMC7525938",
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "TRIP - T cell receptor/immunoglobulin profiler",
                        "abstract": "© 2020 The Author(s).Background: Antigen receptors are characterized by an extreme diversity of specificities, which poses major computational and analytical challenges, particularly in the era of high-throughput immunoprofiling by next generation sequencing (NGS). The T cell Receptor/Immunoglobulin Profiler (TRIP) tool offers the opportunity for an in-depth analysis based on the processing of the output files of the IMGT/HighV-Quest tool, a standard in NGS immunoprofiling, through a number of interoperable modules. These provide detailed information about antigen receptor gene rearrangements, including variable (V), diversity (D) and joining (J) gene usage, CDR3 amino acid and nucleotide composition and clonality of both T cell receptors (TR) and B cell receptor immunoglobulins (BcR IG), and characteristics of the somatic hypermutation within the BcR IG genes. TRIP is a web application implemented in R shiny. Results: Two sets of experiments have been performed in order to evaluate the efficiency and performance of the TRIP tool. The first used a number of synthetic datasets, ranging from 250k to 1M sequences, and established the linear response time of the tool (about 6 h for 1M sequences processed through the entire BcR IG data pipeline). The reproducibility of the tool was tested comparing the results produced by the main TRIP workflow with the results from a previous pipeline used on the Galaxy platform. As expected, no significant differences were noted between the two tools; although the preselection process seems to be stricter within the TRIP pipeline, about 0.1% more rearrangements were filtered out, with no impact on the final results. Conclusions: TRIP is a software framework that provides analytical services on antigen receptor gene sequence data. It is accurate and contains functions for data wrangling, cleaning, analysis and visualization, enabling the user to build a pipeline tailored to their needs. TRIP is publicly available at https://bio.tools/TRIP_-_T-cell_Receptor_Immunoglobulin_Profiler.",
                        "date": "2020-09-29T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Kotouza M.T."
                            },
                            {
                                "name": "Gemenetzi K."
                            },
                            {
                                "name": "Galigalidou C."
                            },
                            {
                                "name": "Vlachonikola E."
                            },
                            {
                                "name": "Pechlivanis N."
                            },
                            {
                                "name": "Agathangelidis A."
                            },
                            {
                                "name": "Sandaltzopoulos R."
                            },
                            {
                                "name": "Mitkas P.A."
                            },
                            {
                                "name": "Stamatopoulos K."
                            },
                            {
                                "name": "Chatzidimitriou A."
                            },
                            {
                                "name": "Psomopoulos F.E."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
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                    "name": "Fotis E. Psomopoulos",
                    "email": "fpsom@certh.gr",
                    "url": "https://biodataanalysisgroup.github.io/",
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