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{
    "count": 45,
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    "list": [
        {
            "name": "WEBnma",
            "description": "WEBnm@ provides quick, automated computation and analysis of low-frequency normal modes for protein structures.",
            "homepage": "http://apps.cbu.uib.no/webnma",
            "biotoolsID": "webnma",
            "biotoolsCURIE": "biotools:webnma",
            "version": [
                "3.5"
            ],
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                    "type": "uses"
                },
                {
                    "biotoolsID": "biopython",
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                {
                    "biotoolsID": "matplotlib",
                    "type": "uses"
                },
                {
                    "biotoolsID": "mustang",
                    "type": "uses"
                },
                {
                    "biotoolsID": "dssp",
                    "type": "uses"
                }
            ],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0570",
                            "term": "Structure visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0244",
                            "term": "Protein flexibility and motion analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1460",
                                "term": "Protein structure"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1476",
                                    "term": "PDB"
                                }
                            ]
                        }
                    ],
                    "output": [
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                            "data": {
                                "uri": "http://edamontology.org/data_2884",
                                "term": "Plot"
                            },
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                                    "uri": "http://edamontology.org/format_3508",
                                    "term": "PDF"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1354",
                                "term": "Sequence profile"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": "Constructs elastic network model from alpha carbon coordinates of the protein, and computes properties to describe large scale conformations. Computes normal modes, fluctuation profiles, inter-residue correlations, conformational overlap analysis and vector field representations. Structural amino acid profiles, and normal mode characteristics describing protein motion, visualized in plots and decorated structure visualizations. White space delimited tabular data for normal modes and the provided plots",
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2487",
                            "term": "Protein structure comparison"
                        },
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                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0886",
                                "term": "Structure alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2200",
                                    "term": "FASTA-like (text)"
                                }
                            ]
                        },
                        {
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                                "uri": "http://edamontology.org/data_1460",
                                "term": "Protein structure"
                            },
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                                {
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                                    "term": "PDB"
                                }
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                    ],
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                                "uri": "http://edamontology.org/data_2884",
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                                    "term": "PDF"
                                }
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                            "data": {
                                "uri": "http://edamontology.org/data_0889",
                                "term": "Structural profile"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": "Performs comparative analysis of the normal modes of protein structures. Computes the Bhattacharyya Coefficient (BC) and the Root Mean Squared Inner Product (RMSIP) of aligned parts of the proteins. Alignment of sets of proteins to be compared. Multiple protein structures Heatmaps, dendrograms and structural amino acid profiles for visual comparison of structural similarity. White space delimited tabular data for the provided plots",
                    "cmd": null
                }
            ],
            "toolType": [
                "Web API",
                "Suite"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2814",
                    "term": "Protein structure analysis"
                },
                {
                    "uri": "http://edamontology.org/topic_0736",
                    "term": "Protein folds and structural domains"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "Python"
            ],
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                "BiB tools",
                "CBU tools",
                "UiB tools",
                "ELIXIR-NO",
                "ELIXIR-Norway"
            ],
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            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Norway"
            ],
            "elixirCommunity": [
                "3D-BioInfo"
            ],
            "link": [
                {
                    "url": "https://github.com/reuter-group/webnma3",
                    "type": [
                        "Repository",
                        "Issue tracker"
                    ],
                    "note": null
                },
                {
                    "url": "https://elixir.no/helpdesk",
                    "type": [
                        "Helpdesk"
                    ],
                    "note": "Helpdesk and support for ELIXIR Norway services."
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "http://apps.cbu.uib.no/webnma3/howto/single",
                    "type": [
                        "Quick start guide"
                    ],
                    "note": null
                },
                {
                    "url": "http://apps.cbu.uib.no/webnma3/qanda",
                    "type": [
                        "FAQ"
                    ],
                    "note": null
                },
                {
                    "url": "http://apps.cbu.uib.no/webnma3/about",
                    "type": [
                        "General",
                        "Citation instructions"
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            ],
            "publication": [
                {
                    "doi": "10.1186/s12859-014-0427-6",
                    "pmid": "25547242",
                    "pmcid": "PMC4339738",
                    "type": [
                        "Primary"
                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "WEBnmat v2.0: Web server and services for comparing protein flexibility",
                        "abstract": "Background: Normal mode analysis (NMA) using elastic network models is a reliable and cost-effective computational method to characterise protein flexibility and by extension, their dynamics. Further insight into the dynamics-function relationship can be gained by comparing protein motions between protein homologs and functional classifications. This can be achieved by comparing normal modes obtained from sets of evolutionary related proteins. Results: We have developed an automated tool for comparative NMA of a set of pre-aligned protein structures. The user can submit a sequence alignment in the FASTA format and the corresponding coordinate files in the Protein Data Bank (PDB) format. The computed normalised squared atomic fluctuations and atomic deformation energies of the submitted structures can be easily compared on graphs provided by the web user interface. The web server provides pairwise comparison of the dynamics of all proteins included in the submitted set using two measures: the Root Mean Squared Inner Product and the Bhattacharyya Coefficient. The Comparative Analysis has been implemented on our web server for NMA, WEBnmat, which also provides recently upgraded functionality for NMA of single protein structures. This includes new visualisations of protein motion, visualisation of inter-residue correlations and the analysis of conformational change using the. In addition, programmatic access to WEBnmat is now available through a SOAP-based web service. WEBnmat is available at. Conclusion: WEBnmat v2.0 is an online tool offering unique capability for comparative NMA on multiple protein structures. Along with a convenient web interface, powerful computing resources, and several methods for mode analyses, WEBnmat facilitates the assessment of protein flexibility within protein families and superfamilies. These analyses can give a good view of how the structures move and how the flexibility is conserved over the different structures.",
                        "date": "2014-12-30T00:00:00Z",
                        "citationCount": 70,
                        "authors": [
                            {
                                "name": "Tiwari S.P."
                            },
                            {
                                "name": "Fuglebakk E."
                            },
                            {
                                "name": "Hollup S.M."
                            },
                            {
                                "name": "Skjaerven L."
                            },
                            {
                                "name": "Cragnolini T."
                            },
                            {
                                "name": "Grindhaug S.H."
                            },
                            {
                                "name": "Tekle K.M."
                            },
                            {
                                "name": "Reuter N."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1186/1471-2105-6-52",
                    "pmid": "15762993",
                    "pmcid": "PMC1274249",
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "WEBnm@: A web application for normal mode analyses of proteins",
                        "abstract": "Background: Normal mode analysis (NMA) has become the method of choice to investigate the slowest motions in macromolecular systems. NMA is especially useful for large biomolecular assemblies, such as transmembrane channels or virus capsids. NMA relies on the hypothesis that the vibrational normal modes having the lowest frequencies (also named soft modes) describe the largest movements in a protein and are the ones that are functionally relevant. Results: We developed a web-based server to perform normal modes calculations and different types of analyses. Starting from a structure file provided by the user in the PDB format, the server calculates the normal modes and subsequently offers the user a series of automated calculations; normalized squared atomic displacements, vector field representation and animation of the first six vibrational modes. Each analysis is performed independently from the others and results can be visualized using only a web browser. No additional plug-in or software is required. For users who would like to analyze the results with their favorite software, raw results can also be downloaded. The application is available on http://www.bioinfo.no/tools/normalmodes. We present here the underlying theory, the application architecture and an illustration of its features using a large transmembrane protein as an example. Conclusion: We built an efficient and modular web application for normal mode analysis of proteins. Non specialists can easily and rapidly evaluate the degree of flexibility of multi-domain protein assemblies and characterize the large amplitude movements of their domains. © 2005 Hollup et al; licensee BioMed Central Ltd.",
                        "date": "2005-03-11T00:00:00Z",
                        "citationCount": 103,
                        "authors": [
                            {
                                "name": "Hollup S.M."
                            },
                            {
                                "name": "Salensminde G."
                            },
                            {
                                "name": "Reuter N."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Nathalie Reuter",
                    "email": "Nathalie.Reuter@uib.no",
                    "url": "http://www.cbu.uib.no/reuter/",
                    "orcidid": "https://orcid.org/0000-0002-3649-7675",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "Sandhya P Tiwari",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0747-3826",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "Kidane M Tekle",
                    "email": null,
                    "url": null,
                    "orcidid": null,
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                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "Tristan Cragnolini",
                    "email": null,
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                        "Developer"
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                    "note": null
                },
                {
                    "name": "Svenn H Grindhaug",
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                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "Lars Skjærven",
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                },
                {
                    "name": "Gisle Salensminde",
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                {
                    "name": "Edvin Fuglebakk",
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                },
                {
                    "name": "Siv M Hollup",
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                {
                    "name": "Department of Molecular Biology, University of Bergen, Norway",
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        },
        {
            "name": "MirGeneDB",
            "description": "MirGeneDB is a miRBase-derived database for microRNA genes that have been manually validated and annotated. Currently, microRNA genes are available for 75 metazoan species and can be browsed, blasted and downloaded.",
            "homepage": "http://mirgenedb.org/",
            "biotoolsID": "mirgen",
            "biotoolsCURIE": "biotools:mirgen",
            "version": [
                "2.1"
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                    "value": "doi:10.25504/FAIRsharing.QXSgvF",
                    "type": "doi",
                    "version": "2.0"
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_2422",
                            "term": "Data retrieval"
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                        {
                            "uri": "http://edamontology.org/operation_0564",
                            "term": "Sequence visualisation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1097",
                                "term": "Sequence accession (nucleic acid)"
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                            "format": [
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                                    "term": "FASTA"
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                                "uri": "http://edamontology.org/data_1869",
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                                "uri": "http://edamontology.org/data_0880",
                                "term": "RNA secondary structure"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
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                            ]
                        }
                    ],
                    "note": "Data retrieval: curated miRNA. Organism identifier: a specific miRNA identifier or a species  for all miRNAs for that species. Gene transcript report: with metadata and visualization. RNA secondary structure: the hairpin loop of the miRNA with bases.",
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                },
                {
                    "operation": [
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                            "term": "Query and retrieval"
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                                "uri": "http://edamontology.org/data_1097",
                                "term": "Sequence accession (nucleic acid)"
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                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3495",
                                "term": "RNA sequence"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        },
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                            "data": {
                                "uri": "http://edamontology.org/data_2012",
                                "term": "Sequence coordinates"
                            },
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                                    "term": "BED"
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                                "term": "Count matrix"
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                                    "term": "Textual format"
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            ],
            "toolType": [
                "Database portal"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0659",
                    "term": "Functional, regulatory and non-coding RNA"
                },
                {
                    "uri": "http://edamontology.org/topic_0204",
                    "term": "Gene regulation"
                },
                {
                    "uri": "http://edamontology.org/topic_3299",
                    "term": "Evolutionary biology"
                },
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                    "uri": "http://edamontology.org/topic_3500",
                    "term": "Zoology"
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                    "uri": "http://edamontology.org/topic_2815",
                    "term": "Human biology"
                }
            ],
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                "Windows",
                "Mac"
            ],
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            "license": "CC0-1.0",
            "collectionID": [
                "UiO tools",
                "ELIXIR-NO",
                "ELIXIR-Norway"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Norway"
            ],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://elixir.no/helpdesk",
                    "type": [
                        "Helpdesk"
                    ],
                    "note": null
                }
            ],
            "download": [
                {
                    "url": "https://www.mirgenedb.org/download",
                    "type": "Biological data",
                    "note": "Sequence downloads for 75 species",
                    "version": "2.1"
                }
            ],
            "documentation": [
                {
                    "url": "https://www.mirgenedb.org/information",
                    "type": [
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            ],
            "publication": [
                {
                    "doi": "10.1093/nar/gkab1101",
                    "pmid": "34850127",
                    "pmcid": "PMC8728216",
                    "type": [
                        "Primary"
                    ],
                    "version": "2.1",
                    "note": null,
                    "metadata": {
                        "title": "MirGeneDB 2.1: Toward a complete sampling of all major animal phyla",
                        "abstract": "We describe an update of MirGeneDB, the manually curated microRNA gene database. Adhering to uniform and consistent criteria for microRNA annotation and nomenclature, we substantially expanded MirGeneDB with 30 additional species representing previously missing metazoan phyla such as sponges, jellyfish, rotifers and flatworms. MirGeneDB 2.1 now consists of 75 species spanning over ∼800 million years of animal evolution, and contains a total number of 16 670 microRNAs from 1549 families. Over 6000 microRNAs were added in this update using ∼550 datasets with ∼7.5 billion sequencing reads. By adding new phylogenetically important species, especially those relevant for the study of whole genome duplication events, and through updating evolutionary nodes of origin for many families and genes, we were able to substantially refine our nomenclature system. All changes are traceable in the specifically developed MirGeneDB version tracker. The performance of read-pages is improved and microRNA expression matrices for all tissues and species are now also downloadable. Altogether, this update represents a significant step toward a complete sampling of all major metazoan phyla, and a widely needed foundation for comparative microRNA genomics and transcriptomics studies. MirGeneDB 2.1 is part of RNAcentral and Elixir Norway, publicly and freely available at http://www.mirgenedb.org/.",
                        "date": "2022-01-07T00:00:00Z",
                        "citationCount": 28,
                        "authors": [
                            {
                                "name": "Fromm B."
                            },
                            {
                                "name": "Hoye E."
                            },
                            {
                                "name": "Domanska D."
                            },
                            {
                                "name": "Zhong X."
                            },
                            {
                                "name": "Aparicio-Puerta E."
                            },
                            {
                                "name": "Ovchinnikov V."
                            },
                            {
                                "name": "Umu S.U."
                            },
                            {
                                "name": "Chabot P.J."
                            },
                            {
                                "name": "Kang W."
                            },
                            {
                                "name": "Aslanzadeh M."
                            },
                            {
                                "name": "Tarbier M."
                            },
                            {
                                "name": "Marmol-Sanchez E."
                            },
                            {
                                "name": "Urgese G."
                            },
                            {
                                "name": "Johansen M."
                            },
                            {
                                "name": "Hovig E."
                            },
                            {
                                "name": "Hackenberg M."
                            },
                            {
                                "name": "Friedlander M.R."
                            },
                            {
                                "name": "Peterson K.J."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1093/nar/gkz885",
                    "pmid": "31598695",
                    "pmcid": "PMC6943042",
                    "type": [
                        "Primary"
                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "MirGeneDB 2.0: The metazoan microRNA complement",
                        "abstract": "Small non-coding RNAs have gained substantial attention due to their roles in animal development and human disorders. Among them, microRNAs are special because individual gene sequences are conserved across the animal kingdom. In addition, unique and mechanistically well understood features can clearly distinguish bona fide miRNAs from the myriad other small RNAs generated by cells. However, making this distinction is not a common practice and, thus, not surprisingly, the heterogeneous quality of available miRNA complements has become a major concern in microRNA research. We addressed this by extensively expanding our curated microRNA gene database-MirGeneDB-to 45 organisms, encompassing a wide phylogenetic swath of animal evolution. By consistently annotating and naming 10,899 microRNA genes in these organisms, we show that previous microRNA annotations contained not only many false positives, but surprisingly lacked >2000 bona fide microRNAs. Indeed, curated microRNA complements of closely related organisms are very similar and can be used to reconstruct ancestral miRNA repertoires. MirGeneDB represents a robust platform for microRNA-based research, providing deeper and more significant insights into the biology and evolution of miRNAs as well as biomedical and biomarker research. MirGeneDB is publicly and freely available at http://mirgenedb.org/.",
                        "date": "2020-01-01T00:00:00Z",
                        "citationCount": 122,
                        "authors": [
                            {
                                "name": "Fromm B."
                            },
                            {
                                "name": "Domanska D."
                            },
                            {
                                "name": "Hoye E."
                            },
                            {
                                "name": "Ovchinnikov V."
                            },
                            {
                                "name": "Kang W."
                            },
                            {
                                "name": "Aparicio-Puerta E."
                            },
                            {
                                "name": "Johansen M."
                            },
                            {
                                "name": "Flatmark K."
                            },
                            {
                                "name": "Mathelier A."
                            },
                            {
                                "name": "Hovig E."
                            },
                            {
                                "name": "Hackenberg M."
                            },
                            {
                                "name": "Friedlander M.R."
                            },
                            {
                                "name": "Peterson K.J."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
                },
                {
                    "doi": "10.1146/annurev-genet-120213-092023",
                    "pmid": "26473382",
                    "pmcid": "PMC4743252",
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "A Uniform System for the Annotation of Vertebrate microRNA Genes and the Evolution of the Human microRNAome",
                        "abstract": "Although microRNAs (miRNAs) are among the most intensively studied molecules of the past 20 years, determining what is and what is not a miRNA has not been straightforward. Here, we present a uniform system for the annotation and nomenclature of miRNA genes. We show that less than a third of the 1,881 human miRBase entries, and only approximately 16% of the 7,095 metazoan miRBase entries, are robustly supported as miRNA genes. Furthermore, we show that the human repertoire of miRNAs has been shaped by periods of intense miRNA innovation and that mature gene products show a very different tempo and mode of sequence evolution than star products. We establish a new open access database-MirGeneDB (http://mirgenedb.org)-to catalog this set of miRNAs, which complements the efforts of miRBase but differs from it by annotating the mature versus star products and by imposing an evolutionary hierarchy upon this curated and consistently named repertoire.",
                        "date": "2015-11-23T00:00:00Z",
                        "citationCount": 367,
                        "authors": [
                            {
                                "name": "Fromm B."
                            },
                            {
                                "name": "Billipp T."
                            },
                            {
                                "name": "Peck L.E."
                            },
                            {
                                "name": "Johansen M."
                            },
                            {
                                "name": "Tarver J.E."
                            },
                            {
                                "name": "King B.L."
                            },
                            {
                                "name": "Newcomb J.M."
                            },
                            {
                                "name": "Sempere L.F."
                            },
                            {
                                "name": "Flatmark K."
                            },
                            {
                                "name": "Hovig E."
                            },
                            {
                                "name": "Peterson K.J."
                            }
                        ],
                        "journal": "Annual Review of Genetics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Bastian Fromm",
                    "email": "BastianFromm@gmail.com",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-0352-3037",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Developer",
                        "Maintainer",
                        "Support"
                    ],
                    "note": null
                },
                {
                    "name": "Kevin J. Peterson",
                    "email": "kevin.j.peterson@dartmouth.edu",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer",
                        "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": "University of Oslo",
                    "email": null,
                    "url": "https://www.uio.no/english/index.html",
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                    "gridid": "grid.5510.1",
                    "rorid": "01xtthb56",
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                        "Provider"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "UiO",
            "additionDate": "2016-02-09T13:19:44Z",
            "lastUpdate": "2023-08-01T08:37:51.018148Z",
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            "homepage_status": 0,
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            "confidence_flag": null
        },
        {
            "name": "MaCPepDB - Mass Centric Peptide Database",
            "description": "A Database to Quickly Access All Tryptic Peptides of the UniProtKB",
            "homepage": "https://macpepdb.mpc.rub.de/",
            "biotoolsID": "macpepdb_-_mass_centric_peptide_database",
            "biotoolsCURIE": "biotools:macpepdb_-_mass_centric_peptide_database",
            "version": [
                "2.3.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [],
            "toolType": [
                "Database portal"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Python",
                "JavaScript",
                "SQL"
            ],
            "license": null,
            "collectionID": [],
            "maturity": "Emerging",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Germany"
            ],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://github.com/mpc-bioinformatics/macpepdb",
                    "type": [
                        "Repository"
                    ],
                    "note": "Backend implementation written in Flask"
                },
                {
                    "url": "https://github.com/mpc-bioinformatics/macpepdb",
                    "type": [
                        "Repository"
                    ],
                    "note": "Frontend implementation written in NuxtJS"
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "https://macpepdb.mpc.rub.de/docs/api",
                    "type": [
                        "API documentation"
                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/mpc-bioinformatics/macpepdb/blob/main/Readme.md",
                    "type": [
                        "Installation instructions"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1021/acs.jproteome.0c00967",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "MaCPepDB: A Database to Quickly Access All Tryptic Peptides of the UniProtKB",
                        "abstract": "Protein sequence databases play a crucial role in the majority of the currently applied mass-spectrometry-based proteomics workflows. Here UniProtKB serves as one of the major sources, as it combines the information of several smaller databases and enriches the entries with additional biological information. For the identification of peptides in a sample by tandem mass spectra, as generated by data-dependent acquisition, protein sequence databases provide the basis for most spectrum identification search engines. In addition, for targeted proteomics approaches like selected reaction monitoring (SRM) and parallel reaction monitoring (PRM), knowledge of the peptide sequences, their masses, and whether they are unique for a protein is essential. Because most bottom-up proteomics approaches use trypsin to cleave the proteins in a sample, the tryptic peptides contained in a protein database are of great interest. We present a database, called MaCPepDB (mass-centric peptide database), that consists of the complete tryptic digest of the Swiss-Prot and TrEMBL parts of UniProtKB. This database is especially designed to not only allow queries of peptide sequences and return the respective information about connected proteins and thus whether a peptide is unique but also allow queries of specific masses of peptides or precursors of MS/MS spectra. Furthermore, posttranslational modifications can be considered in a query as well as different mass deviations for posttranslational modifications. Hence the database can be used by a sequence query not only to, for example, check in which proteins of the UniProt database a tryptic peptide can be found but also to find possibly interfering peptides in PRM/SRM experiments using the mass query. The complete database contains currently 5 939 244 990 peptides from 185 561 610 proteins (UniProt version 2020_03), for which a single query usually takes less than 1 s. For easy exploration of the data, a web interface was developed. A REST application programming interface (API) for programmatic and workflow access is also available at https://macpepdb.mpc.rub.de.",
                        "date": "2021-04-02T00:00:00Z",
                        "citationCount": 3,
                        "authors": [
                            {
                                "name": "Uszkoreit J."
                            },
                            {
                                "name": "Winkelhardt D."
                            },
                            {
                                "name": "Barkovits K."
                            },
                            {
                                "name": "Wulf M."
                            },
                            {
                                "name": "Roocke S."
                            },
                            {
                                "name": "Marcus K."
                            },
                            {
                                "name": "Eisenacher M."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Julian Uszkoreit",
                    "email": null,
                    "url": null,
                    "orcidid": "http://orcid.org/0000-0001-7522-4007",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": null,
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Dirk Winkelhardt",
                    "email": "dirk.winkelhardt@rub.de",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-8770-2221",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": null,
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "PD Dr. Martin Eisenacher",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-2687-7444",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": null,
                    "typeRole": [],
                    "note": null
                }
            ],
            "community": null,
            "owner": "di_hardt",
            "additionDate": "2023-07-25T12:35:27.605861Z",
            "lastUpdate": "2023-07-31T15:54:10.630197Z",
            "editPermission": {
                "type": "private",
                "authors": [
                    "di_hardt",
                    "martin.eisenacher@ruhr-uni-boc"
                ]
            },
            "validated": 0,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "Variant Combination Pathogenicity Predictor (VarCoPP) 2.0",
            "description": "VarCoPP is a machine learning method that predicts the potential pathogenicity of variant combinations in gene pairs. It is based on digenic data present in OLIDA and it was trained against variants from the 1000 Genomes Project. VarCoPP2.0 is a Balanced Random Forest predictor consisting of 400 decision trees. \nA variant combination can be either predicted as disease-causing (i.e. candidate or probably pathogenic) or neutral (i.e. probably neutral). \n\nVarCoPP can be applied for Single Nucleotide Variants (SNVs) and small insertions/deletions from a single individual. It is highly recommended to perform beforehand an initial variant filtering procedure and generally restrict the analysis to variants from up to 150 genes. \n\nVarCoPP was developed in the Interuniversity Institute of Bioinformatics in Brussels, under the collaboration of Université libre de Bruxelles and Vrije Universiteit Brussel.\n\nYou can use it through the online tool ORVAL: https://orval.ibsquare.be.",
            "homepage": "http://varcopp.ibsquare.be",
            "biotoolsID": "Variant_Combinaton_Pathogenicity_Predictor",
            "biotoolsCURIE": "biotools:Variant_Combinaton_Pathogenicity_Predictor",
            "version": [
                "1.0",
                "2.0"
            ],
            "otherID": [],
            "relation": [
                {
                    "biotoolsID": "oligogenic_resource_for_variant_analysis",
                    "type": "includedIn"
                },
                {
                    "biotoolsID": "olida",
                    "type": "uses"
                }
            ],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2423",
                            "term": "Prediction and recognition"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3225",
                            "term": "Variant classification"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3197",
                            "term": "Genetic variation analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Plug-in"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0622",
                    "term": "Genomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
                },
                {
                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
                },
                {
                    "uri": "http://edamontology.org/topic_3063",
                    "term": "Medical informatics"
                },
                {
                    "uri": "http://edamontology.org/topic_3325",
                    "term": "Rare diseases"
                },
                {
                    "uri": "http://edamontology.org/topic_0625",
                    "term": "Genotype and phenotype"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [],
            "license": "GPL-3.0",
            "collectionID": [
                "ELIXIR-BE",
                "RD-Connect",
                "Rare Disease"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [
                "Belgium"
            ],
            "elixirCommunity": [
                "Rare Diseases"
            ],
            "link": [
                {
                    "url": "https://github.com/oligogenic/VarCoPP2.0",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                },
                {
                    "url": "https://github.com/sofiapapad90/VarCoPP/",
                    "type": [
                        "Repository"
                    ],
                    "note": null
                },
                {
                    "url": "https://orval.ibsquare.be",
                    "type": [
                        "Service"
                    ],
                    "note": "Web app integrating VarCoPP2.0"
                }
            ],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1073/pnas.1815601116",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "Predicting disease-causing variant combinations",
                        "abstract": "Notwithstanding important advances in the context of single-variant pathogenicity identification, novel breakthroughs in discerning the origins of many rare diseases require methods able to identify more complex genetic models. We present here the Variant Combinations Pathogenicity Predictor (VarCoPP), a machine-learning approach that identifies pathogenic variant combinations in gene pairs (called digenic or bilocus variant combinations). We show that the results produced by this method are highly accurate and precise, an efficacy that is endorsed when validating the method on recently published independent disease-causing data. Confidence labels of 95% and 99% are identified, representing the probability of a bilocus combination being a true pathogenic result, providing geneticists with rational markers to evaluate the most relevant pathogenic combinations and limit the search space and time. Finally, the VarCoPP has been designed to act as an interpretable method that can provide explanations on why a bilocus combination is predicted as pathogenic and which biological information is important for that prediction. This work provides an important step toward the genetic understanding of rare diseases, paving the way to clinical knowledge and improved patient care.",
                        "date": "2019-06-11T00:00:00Z",
                        "citationCount": 48,
                        "authors": [
                            {
                                "name": "Papadimitriou S."
                            },
                            {
                                "name": "Gazzo A."
                            },
                            {
                                "name": "Versbraegen N."
                            },
                            {
                                "name": "Nachtegael C."
                            },
                            {
                                "name": "Aerts J."
                            },
                            {
                                "name": "Moreau Y."
                            },
                            {
                                "name": "Van Dooren S."
                            },
                            {
                                "name": "Nowe A."
                            },
                            {
                                "name": "Smits G."
                            },
                            {
                                "name": "Lenaerts T."
                            }
                        ],
                        "journal": "Proceedings of the National Academy of Sciences of the United States of America"
                    }
                },
                {
                    "doi": "10.1186/s12859-023-05291-3",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "Faster and more accurate pathogenic combination predictions with VarCoPP2.0",
                        "abstract": "Background: The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases. Models tailored towards such cases can help shorten the gap of missing diagnoses and can aid researchers in dealing with the high complexity of the derived data. The predictor VarCoPP (Variant Combinations Pathogenicity Predictor) that was published in 2019 and identified potentially pathogenic variant combinations in gene pairs (bilocus variant combinations), was the first important step in this direction. Despite its usefulness and applicability, several issues still remained that hindered a better performance, such as its False Positive (FP) rate, the quality of its training set and its complex architecture. Results: We present VarCoPP2.0: the successor of VarCoPP that is a simplified, faster and more accurate predictive model identifying potentially pathogenic bilocus variant combinations. Results from cross-validation and on independent data sets reveal that VarCoPP2.0 has improved in terms of both sensitivity (95% in cross-validation and 98% during testing) and specificity (5% FP rate). At the same time, its running time shows a significant 150-fold decrease due to the selection of a simpler Balanced Random Forest model. Its positive training set now consists of variant combinations that are more confidently linked with evidence of pathogenicity, based on the confidence scores present in OLIDA, the Oligogenic Diseases Database (https://olida.ibsquare.be). The improvement of its performance is also attributed to a more careful selection of up-to-date features identified via an original wrapper method. We show that the combination of different variant and gene pair features together is important for predictions, highlighting the usefulness of integrating biological information at different levels. Conclusions: Through its improved performance and faster execution time, VarCoPP2.0 enables a more accurate analysis of larger data sets linked to oligogenic diseases. Users can access the ORVAL platform (https://orval.ibsquare.be) to apply VarCoPP2.0 on their data.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Versbraegen N."
                            },
                            {
                                "name": "Gravel B."
                            },
                            {
                                "name": "Nachtegael C."
                            },
                            {
                                "name": "Renaux A."
                            },
                            {
                                "name": "Verkinderen E."
                            },
                            {
                                "name": "Nowe A."
                            },
                            {
                                "name": "Lenaerts T."
                            },
                            {
                                "name": "Papadimitriou S."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Tom Lenaerts",
                    "email": "tlenaert@ulb.ac.be",
                    "url": "http://www.ibsquare.be",
                    "orcidid": "https://orcid.org/0000-0003-3645-1455",
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                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                },
                {
                    "name": "Sofia Papadomitriou",
                    "email": "sofiapapad.bio@gmail.com",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": null
                },
                {
                    "name": "Interuniversity Institute of Bioinformatics in Brussels",
                    "email": null,
                    "url": "https://ibsquare.be",
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "tlenaert@ulb.ac.be",
            "additionDate": "2019-07-03T15:13:23Z",
            "lastUpdate": "2023-07-31T11:59:09.158293Z",
            "editPermission": {
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                "authors": [
                    "emmaver"
                ]
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            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "compareMS2",
            "description": "A simple tool for comparing sets of (tandem) mass spectra between datasets for clustering data, molecular phylogenetics, species and tissue identification. compareMS2 currently supports Mascot Generic Format or MGF.",
            "homepage": "https://github.com/524D/compareMS2",
            "biotoolsID": "comparems2",
            "biotoolsCURIE": "biotools:comparems2",
            "version": [
                "1.0",
                "2.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2424",
                            "term": "Comparison"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0567",
                            "term": "Phylogenetic tree visualisation"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2536",
                                "term": "Mass spectrometry data"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3651",
                                    "term": "MGF"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3272",
                                "term": "Species tree"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3603",
                                    "term": "PNG"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2855",
                                "term": "Distance matrix"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0084",
                    "term": "Phylogeny"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3172",
                    "term": "Metabolomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3520",
                    "term": "Proteomics experiment"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "C",
                "JavaScript"
            ],
            "license": "MIT",
            "collectionID": [
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                {
                    "url": "https://github.com/524D/compareMS2",
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            "publication": [
                {
                    "doi": "10.1002/rcm.6162",
                    "pmid": "22368051",
                    "pmcid": null,
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                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "Molecular phylogenetics by direct comparison of tandem mass spectra",
                        "abstract": "Rationale: Molecular phylogenetics is the study of evolution and relatedness of organisms or genes. Mass spectrometry is used routinely for bacterial identification and has also been used for phylogenetic analysis, for instance from bone material. Unfortunately, only a small fraction of the acquired tandem mass spectra allow direct interpretation. Methods: We describe a new algorithm and software for molecular phylogenetics using pairwise comparisons of tandem mass spectra from enzymatically digested proteins. The spectra need not be annotated and all acquired data is used in the analysis. To demonstrate the method, we analyzed tryptic digests of sera from four great apes and two other primates. Results: The distribution of spectra dot products for thousands of tandem mass spectra collected from two samples provides a measure on the fraction of shared peptides between the two samples. When inverted, this becomes a distance metric. By pairwise comparison between species and averaging over four individuals per species, it was possible to reconstruct the unique correct phylogenetic tree for the great apes and other primates. Conclusions: The new method described here has several attractive features compared with existing methods, among them simplicity, the unbiased use of all acquired data rather than a small subset of spectra, and the potential use of heavily degraded proteins or proteins with a priori unknown modifications. © 2012 John Wiley & Sons, Ltd.",
                        "date": "2012-04-15T00:00:00Z",
                        "citationCount": 26,
                        "authors": [
                            {
                                "name": "Palmblad M."
                            },
                            {
                                "name": "Deelder A.M."
                            }
                        ],
                        "journal": "Rapid Communications in Mass Spectrometry"
                    }
                },
                {
                    "doi": "10.1021/acs.jproteome.2c00457",
                    "pmid": "36173614",
                    "pmcid": "PMC9903320",
                    "type": [
                        "Other"
                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "compareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets",
                        "abstract": "It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.",
                        "date": "2023-02-03T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Marissen R."
                            },
                            {
                                "name": "Varunjikar M.S."
                            },
                            {
                                "name": "Laros J.F.J."
                            },
                            {
                                "name": "Rasinger J.D."
                            },
                            {
                                "name": "Neely B.A."
                            },
                            {
                                "name": "Palmblad M."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                },
                {
                    "doi": "10.1021/acs.jproteome.1c00528",
                    "pmid": "34523928",
                    "pmcid": "PMC8491155",
                    "type": [
                        "Review"
                    ],
                    "version": "2.0",
                    "note": null,
                    "metadata": {
                        "title": "Rewinding the Molecular Clock: Looking at Pioneering Molecular Phylogenetics Experiments in the Light of Proteomics",
                        "abstract": "Science is full of overlooked and undervalued research waiting to be rediscovered. Proteomics is no exception. In this perspective, we follow the ripples from a 1960 study of Zuckerkandl, Jones, and Pauling comparing tryptic peptides across animal species. This pioneering work directly led to the molecular clock hypothesis and the ensuing explosion in molecular phylogenetics. In the decades following, proteins continued to provide essential clues on evolutionary history. While technology has continued to improve, contemporary proteomics has strayed from this larger biological context, rarely comparing species or asking how protein structure, function, and interactions have evolved. Here we recombine proteomics with molecular phylogenetics, highlighting the value of framing proteomic results in a larger biological context and how almost forgotten research, though technologically surpassed, can still generate new ideas and illuminate our work from a different perspective. Though it is infeasible to read all research published on a large topic, looking up older papers can be surprisingly rewarding when rediscovering a \"gem\"at the end of a long citation chain, aided by digital collections and perpetually helpful librarians. Proper literature study reduces unnecessary repetition and allows research to be more insightful and impactful by truly standing on the shoulders of giants. All data was uploaded to MassIVE (https://massive.ucsd.edu/) as dataset MSV000087993.",
                        "date": "2021-10-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Neely B.A."
                            },
                            {
                                "name": "Palmblad M."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                }
            ],
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                {
                    "name": "Magnus Palmblad",
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        {
            "name": "Kojak",
            "description": "A database search algorithm for solving cross-linked peptide mass spectra.",
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                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3520",
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            "download": [
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                {
                    "doi": "10.1021/pr501321h",
                    "pmid": "25812159",
                    "pmcid": "PMC4428575",
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                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "Kojak: Efficient analysis of chemically cross-linked protein complexes",
                        "abstract": "Protein chemical cross-linking and mass spectrometry enable the analysis of protein-protein interactions and protein topologies; however, complicated cross-linked peptide spectra require specialized algorithms to identify interacting sites. The Kojak cross-linking software application is a new, efficient approach to identify cross-linked peptides, enabling large-scale analysis of protein-protein interactions by chemical cross-linking techniques. The algorithm integrates spectral processing and scoring schemes adopted from traditional database search algorithms and can identify cross-linked peptides using many different chemical cross-linkers with or without heavy isotope labels. Kojak was used to analyze both novel and existing data sets and was compared to existing cross-linking algorithms. The algorithm provided increased cross-link identifications over existing algorithms and, equally importantly, the results in a fraction of computational time. The Kojak algorithm is open-source, cross-platform, and freely available. This software provides both existing and new cross-linking researchers alike an effective way to derive additional cross-link identifications from new or existing data sets. For new users, it provides a simple analytical resource resulting in more cross-link identifications than other methods.",
                        "date": "2015-05-01T00:00:00Z",
                        "citationCount": 126,
                        "authors": [
                            {
                                "name": "Hoopmann M.R."
                            },
                            {
                                "name": "Zelter A."
                            },
                            {
                                "name": "Johnson R.S."
                            },
                            {
                                "name": "Riffle M."
                            },
                            {
                                "name": "Maccoss M.J."
                            },
                            {
                                "name": "Davis T.N."
                            },
                            {
                                "name": "Moritz R.L."
                            }
                        ],
                        "journal": "Journal of Proteome Research"
                    }
                },
                {
                    "doi": "10.1021/acs.jproteome.2c00670",
                    "pmid": "36629399",
                    "pmcid": "PMC10234491",
                    "type": [
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                    "version": "2.0.0",
                    "note": null,
                    "metadata": {
                        "title": "Improved Analysis of Cross-Linking Mass Spectrometry Data with Kojak 2.0, Advanced by Integration into the Trans-Proteomic Pipeline",
                        "abstract": "Fragmentation ion spectral analysis of chemically cross-linked proteins is an established technology in the proteomics research repertoire for determining protein interactions, spatial orientation, and structure. Here we present Kojak version 2.0, a major update to the original Kojak algorithm, which was developed to identify cross-linked peptides from fragment ion spectra using a database search approach. A substantially improved algorithm with updated scoring metrics, support for cleavable cross-linkers, and identification of cross-links between 15N-labeled homomultimers are among the newest features of Kojak 2.0 presented here. Kojak 2.0 is now integrated into the Trans-Proteomic Pipeline, enabling access to dozens of additional tools within that suite. In particular, the PeptideProphet and iProphet tools for validation of cross-links improve the sensitivity and accuracy of correct cross-link identifications at user-defined thresholds. These new features improve the versatility of the algorithm, enabling its use in a wider range of experimental designs and analysis pipelines. Kojak 2.0 remains open-source and multiplatform.",
                        "date": "2023-02-03T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Hoopmann M.R."
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                            {
                                "name": "Shteynberg D.D."
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                            {
                                "name": "Zelter A."
                            },
                            {
                                "name": "Riffle M."
                            },
                            {
                                "name": "Lyon A.S."
                            },
                            {
                                "name": "Agard D.A."
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                            {
                                "name": "Luan Q."
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                            {
                                "name": "Nolen B.J."
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                            {
                                "name": "MacCoss M.J."
                            },
                            {
                                "name": "Davis T.N."
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                            {
                                "name": "Moritz R.L."
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                        ],
                        "journal": "Journal of Proteome Research"
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                    "doi": "10.3389/fgene.2021.618170",
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                        "title": "A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data",
                        "abstract": "The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learning techniques in order to identify characteristic k-mers of the input dataset across a range of different k-values and within a reasonable time frame. We use these k-mers as features for clustering the input sequences and identifying differences between the distributions of k-mers across the dataset. The developed algorithm is part of an innovative and much promising approach both to the problem of grouping sequence data based on their inherent characteristic features, as well as for the study of changes in the distributions of k-mers, as the k-value is fluctuating within a range of values. Our framework is fully developed in Python language as an open source software licensed under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.",
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                                "name": "Pechlivanis N."
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                                "name": "Tsagiopoulou M."
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                            {
                                "name": "Sgardelis S."
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                            {
                                "name": "Kappas I."
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                            {
                                "name": "Psomopoulos F."
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                    "term": "Gene transcripts"
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                    "doi": "10.1038/s41598-022-06625-6",
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                    "metadata": {
                        "title": "Detecting SARS-CoV-2 lineages and mutational load in municipal wastewater and a use-case in the metropolitan area of Thessaloniki, Greece",
                        "abstract": "The COVID-19 pandemic represents an unprecedented global crisis necessitating novel approaches for, amongst others, early detection of emerging variants relating to the evolution and spread of the virus. Recently, the detection of SARS-CoV-2 RNA in wastewater has emerged as a useful tool to monitor the prevalence of the virus in the community. Here, we propose a novel methodology, called lineagespot, for the monitoring of mutations and the detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing (NGS). Our proposed method was tested and evaluated using NGS data produced by the sequencing of 14 wastewater samples from the municipality of Thessaloniki, Greece, covering a 6-month period. The results showed the presence of SARS-CoV-2 variants in wastewater data. lineagespot was able to record the evolution and rapid domination of the Alpha variant (B.1.1.7) in the community, and allowed the correlation between the mutations evident through our approach and the mutations observed in patients from the same area and time periods. lineagespot is an open-source tool, implemented in R, and is freely available on GitHub and registered on bio.tools.",
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                            {
                                "name": "Pechlivanis N."
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                            {
                                "name": "Tsagiopoulou M."
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                            {
                                "name": "Maniou M.C."
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                            {
                                "name": "Togkousidis A."
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                            {
                                "name": "Mouchtaropoulou E."
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                            {
                                "name": "Chassalevris T."
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                            {
                                "name": "Chaintoutis S.C."
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                            {
                                "name": "Petala M."
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                            {
                                "name": "Kostoglou M."
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                            {
                                "name": "Karapantsios T."
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                            {
                                "name": "Laidou S."
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                            {
                                "name": "Vlachonikola E."
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                            {
                                "name": "Chatzidimitriou A."
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                            {
                                "name": "Papadopoulos A."
                            },
                            {
                                "name": "Papaioannou N."
                            },
                            {
                                "name": "Dovas C.I."
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                            {
                                "name": "Argiriou A."
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                            {
                                "name": "Psomopoulos F."
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                        "journal": "Scientific Reports"
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                {
                    "name": "Fotis E. Psomopoulos",
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        {
            "name": "Beyondcell",
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                            "uri": "http://edamontology.org/operation_3223",
                            "term": "Differential gene expression profiling"
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                        {
                            "uri": "http://edamontology.org/operation_4009",
                            "term": "Small molecule design"
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                            "uri": "http://edamontology.org/operation_0313",
                            "term": "Expression profile clustering"
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                    ],
                    "input": [
                        {
                            "data": {
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                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1621",
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                    "note": "Beyondcell can make use of custom gene expression profiles on top of its own catalogue of drug related signatures. Beyondcell allows the user to input a GMT file containing the functional pathways/signatures of interest as well as numeric matrices containing a ranking\ncriteria such as the t-statistic or logFoldChange.",
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                    "term": "Oncology"
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                    "term": "Cell biology"
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                    "note": "Source code for the current software version",
                    "version": "1.3.3."
                },
                {
                    "url": "https://anaconda.org/bioconda/r-beyondcell",
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                {
                    "url": "https://gitlab.com/bu_cnio/beyondcell/-/tree/master/tutorial/analysis_workflow",
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                    "url": "https://gitlab.com/bu_cnio/beyondcell/-/tree/master/tutorial/analysis_workflow",
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                    "url": "https://gitlab.com/bu_cnio/beyondcell/-/tree/master/tutorial/analysis_workflow",
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                },
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                    "url": "https://gitlab.com/bu_cnio/beyondcell/-/blob/master/CHANGELOG.md",
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                {
                    "doi": "10.1101/2021.04.08.438954",
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            ],
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                    "name": "Dr. Fátima Al-Shahrour",
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