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    "count": 450,
    "previous": null,
    "list": [
        {
            "collectionID": [
                "BLAST utility"
            ],
            "accessibility": [
                "Open access"
            ],
            "topic": [
                {
                    "term": "Data management",
                    "uri": "http://edamontology.org/topic_3071"
                },
                {
                    "term": "Bioinformatics",
                    "uri": "http://edamontology.org/topic_0091"
                }
            ],
            "owner": "hlfernandez@uvigo.es",
            "cost": "Free of charge",
            "relation": [
                {
                    "type": "uses",
                    "biotoolsID": "blast"
                }
            ],
            "download": [
                {
                    "url": "https://github.com/sing-group/BDBM/",
                    "note": null,
                    "version": null,
                    "type": "Source code"
                },
                {
                    "url": "http://www.sing-group.org/BDBM/download.html",
                    "note": null,
                    "version": null,
                    "type": "Binaries"
                }
            ],
            "validated": 0,
            "publication": [
                {
                    "doi": "10.1007/s12539-019-00320-3",
                    "version": "1.0",
                    "pmid": "30712176",
                    "type": "Primary",
                    "pmcid": null,
                    "metadata": {
                        "title": "BDBM 1.0: A Desktop Application for Efficient Retrieval and Processing of High-Quality Sequence Data and Application to the Identification of the Putative Coffea S-Locus",
                        "abstract": "© 2019, International Association of Scientists in the Interdisciplinary Areas.Nowadays, bioinformatics is one of the most important areas in modern biology and the creation of high-quality scientific software supporting this recent research area is one of the core activities of many researchers. In this context, high-quality sequence datasets are needed to perform inferences on the evolution of species, genes, and gene families, or to get evidence for adaptive amino acid evolution, among others. Nevertheless, sequence data are very often spread over several databases, many useful genomes and transcriptomes are non-annotated, the available annotation is not for the desired coding sequence isoform, and/or is unlikely to be accurate. Moreover, although the FASTA text-based format is quite simple and usable by most software applications, there are a number of issues that may be critical depending on the software used to analyse such files. Therefore, researchers without training in informatics often use a fraction of all available data. The above issues can be addressed using already available software applications, but there is no easy-to-use single piece of software that allows performing all these tasks within the same graphical interface, such as the one here presented, named BDBM (Blast DataBase Manager). BDBM can be used to efficiently get gene sequences from annotated and non-annotated genomes and transcriptomes. Moreover, it can be used to look for alternatives to existing annotations and to easily create reliable custom databases. Such databases are essential to prepare high-quality datasets. The analyses that we have performed on the Coffea canephora genome using BDBM aimed at the identification of the S-locus region (that harbours the genes involved in gametophytic self-incompatibility) led to the conclusion that there are two likely regions, one on chromosome 2 (around region 6600000–6650000), and another on chromosome 5 (around 15830000–15930000). Such findings are discussed in the context of the Rubiaceae gametophytic self-incompatibility evolution.",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Vazquez N."
                            },
                            {
                                "name": "Lopez-Fernandez H."
                            },
                            {
                                "name": "Vieira C.P."
                            },
                            {
                                "name": "Fdez-Riverola F."
                            },
                            {
                                "name": "Vieira J."
                            },
                            {
                                "name": "Reboiro-Jato M."
                            }
                        ],
                        "date": "2019-03-01T00:00:00Z",
                        "journal": "Interdisciplinary Sciences: Computational Life Sciences"
                    }
                }
            ],
            "homepage_status": 0,
            "credit": [
                {
                    "typeRole": [
                        "Contributor"
                    ],
                    "name": "Molecular Evolution Group (IBMC)",
                    "url": "http://evolution.ibmc.up.pt/",
                    "note": null,
                    "orcidid": null,
                    "typeEntity": null,
                    "email": null
                },
                {
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                        "Developer"
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                    "name": "SING Research Group",
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                    "note": null,
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                    "email": null
                },
                {
                    "typeRole": [
                        "Primary contact"
                    ],
                    "name": null,
                    "url": "http://www.sing-group.org/BDBM/about.html",
                    "note": null,
                    "orcidid": null,
                    "typeEntity": "Person",
                    "email": null
                }
            ],
            "biotoolsCURIE": "biotools:bdbm",
            "elixirPlatform": [],
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            "version": [
                "1.0.2"
            ],
            "elixir_badge": 0,
            "homepage": "http://www.sing-group.org/BDBM",
            "function": [
                {
                    "note": null,
                    "input": [
                        {
                            "data": {
                                "term": "Database search results",
                                "uri": "http://edamontology.org/data_2080"
                            },
                            "format": []
                        }
                    ],
                    "operation": [
                        {
                            "term": "Database search",
                            "uri": "http://edamontology.org/operation_2421"
                        }
                    ],
                    "cmd": null,
                    "output": []
                }
            ],
            "lastUpdate": "2019-07-22T13:00:28.286202Z",
            "otherID": [],
            "description": "Graphical user interface that allows you to create high quality sequence datasets using the tools included. Using these tools you can perform several common tasks such as doing BLAST alignments, getting the open reading frames of the sequences in a Fasta file or changing the format of a Fasta file. All these task make use of several known tools such as EMBOSS, bedtools, and NCBI's BLAST, Splign and Compart.",
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                "Desktop application"
            ],
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                "authors": []
            },
            "language": [
                "Java"
            ],
            "link": [],
            "biotoolsID": "bdbm",
            "additionDate": "2017-01-11T12:47:54Z",
            "name": "BLAST DataBase Manager",
            "license": "GPL-3.0",
            "documentation": [
                {
                    "url": "http://www.sing-group.org/BDBM/manual.html",
                    "note": null,
                    "type": "Manual"
                },
                {
                    "url": "http://www.sing-group.org/BDBM/usecases.html",
                    "note": null,
                    "type": "Training material"
                }
            ],
            "maturity": "Emerging",
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ]
        },
        {
            "collectionID": [],
            "accessibility": [
                "Open access"
            ],
            "topic": [
                {
                    "term": "Small molecules",
                    "uri": "http://edamontology.org/topic_0154"
                },
                {
                    "term": "Proteomics experiment",
                    "uri": "http://edamontology.org/topic_3520"
                },
                {
                    "term": "Proteomics",
                    "uri": "http://edamontology.org/topic_0121"
                }
            ],
            "owner": "Ruta",
            "cost": "Free of charge",
            "relation": [
                {
                    "type": "uses",
                    "biotoolsID": "R"
                }
            ],
            "download": [
                {
                    "url": "https://github.com/LFAQ/LFAQ/releases",
                    "note": null,
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                    "type": "Source code"
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                    "url": "https://github.com/LFAQ/LFAQ/tree/master/ExecutableFiles",
                    "note": null,
                    "version": null,
                    "type": "Binaries"
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            ],
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            "publication": [
                {
                    "doi": "10.1021/acs.analchem.8b03267",
                    "version": null,
                    "pmid": null,
                    "type": "Primary",
                    "pmcid": null,
                    "metadata": {
                        "title": "LFAQ: Toward Unbiased Label-Free Absolute Protein Quantification by Predicting Peptide Quantitative Factors",
                        "abstract": "© 2018 American Chemical Society. Mass spectrometry (MS) has become a predominant choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose an algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on data sets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins. The advantages of LFAQ were further confirmed using the data from published papers.",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Chang C."
                            },
                            {
                                "name": "Gao Z."
                            },
                            {
                                "name": "Ying W."
                            },
                            {
                                "name": "Fu Y."
                            },
                            {
                                "name": "Zhao Y."
                            },
                            {
                                "name": "Wu S."
                            },
                            {
                                "name": "Li M."
                            },
                            {
                                "name": "Wang G."
                            },
                            {
                                "name": "Qian X."
                            },
                            {
                                "name": "Zhu Y."
                            },
                            {
                                "name": "He F."
                            }
                        ],
                        "date": "2019-01-15T00:00:00Z",
                        "journal": "Analytical Chemistry"
                    }
                }
            ],
            "homepage_status": 0,
            "credit": [
                {
                    "typeRole": [
                        "Primary contact"
                    ],
                    "name": "Cheng Chang",
                    "url": null,
                    "note": null,
                    "orcidid": null,
                    "typeEntity": "Person",
                    "email": "1987ccpacer@163.com"
                },
                {
                    "typeRole": [
                        "Primary contact"
                    ],
                    "name": "Zhiqiang Gao",
                    "url": null,
                    "note": null,
                    "orcidid": null,
                    "typeEntity": "Person",
                    "email": "gaozhiqiang13@mails.ucas.ac.cn"
                }
            ],
            "biotoolsCURIE": "biotools:LFAQ",
            "elixirPlatform": [],
            "elixirNode": [],
            "version": [],
            "elixir_badge": 0,
            "homepage": "https://lfaq.github.io/LFAQ/",
            "function": [
                {
                    "note": null,
                    "input": [],
                    "operation": [
                        {
                            "term": "Label-free quantification",
                            "uri": "http://edamontology.org/operation_3634"
                        },
                        {
                            "term": "Protein fragment weight comparison",
                            "uri": "http://edamontology.org/operation_2929"
                        },
                        {
                            "term": "Peptide identification",
                            "uri": "http://edamontology.org/operation_3631"
                        }
                    ],
                    "cmd": null,
                    "output": []
                }
            ],
            "lastUpdate": "2019-07-19T14:19:43Z",
            "otherID": [],
            "description": "Algorithm for label-free absolute protein quantification, which can correct the biased MS intensities using the predicted peptide quantitative factors for all identified peptides.",
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                "Desktop application"
            ],
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            },
            "language": [
                "R",
                "C++"
            ],
            "link": [
                {
                    "url": "https://github.com/LFAQ/LFAQ/issues",
                    "note": null,
                    "type": "Issue tracker"
                },
                {
                    "url": "https://github.com/LFAQ/LFAQ",
                    "note": null,
                    "type": "Repository"
                }
            ],
            "biotoolsID": "LFAQ",
            "additionDate": "2019-07-11T07:34:59Z",
            "name": "LFAQ",
            "license": "Artistic-2.0",
            "documentation": [
                {
                    "url": "https://github.com/LFAQ/LFAQ/blob/master/User%20Guide%20for%20LFAQ.pdf",
                    "note": null,
                    "type": "Tutorial"
                }
            ],
            "maturity": "Mature",
            "operatingSystem": [
                "Windows"
            ]
        },
        {
            "collectionID": [
                "Proteomics"
            ],
            "accessibility": [
                "Freeware"
            ],
            "topic": [
                {
                    "term": "Proteomics",
                    "uri": "http://edamontology.org/topic_0121"
                },
                {
                    "term": "Proteomics experiment",
                    "uri": "http://edamontology.org/topic_3520"
                },
                {
                    "term": "Sequence analysis",
                    "uri": "http://edamontology.org/topic_0080"
                },
                {
                    "term": "Epigenetics",
                    "uri": "http://edamontology.org/topic_3295"
                },
                {
                    "term": "Protein modifications",
                    "uri": "http://edamontology.org/topic_0601"
                }
            ],
            "owner": "mbs_import",
            "cost": "Free of charge",
            "relation": [],
            "download": [
                {
                    "url": "http://prosightlite.northwestern.edu/setup.exe",
                    "note": null,
                    "version": null,
                    "type": "Binaries"
                }
            ],
            "validated": 0,
            "publication": [
                {
                    "doi": null,
                    "version": null,
                    "pmid": "25828799",
                    "type": "Primary",
                    "pmcid": null,
                    "metadata": {
                        "title": "ProSight Lite: Graphical software to analyze top-down mass spectrometry data",
                        "abstract": "© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. Many top-down proteomics experiments focus on identifying and localizing PTMs and other potential sources of \"mass shift\" on a known protein sequence. A simple application to match ion masses and facilitate the iterative hypothesis testing of PTM presence and location would assist with the data analysis in these experiments. ProSight Lite is a free software tool for matching a single candidate sequence against a set of mass spectrometric observations. Fixed or variable modifications, including both PTMs and a select number of glycosylations, can be applied to the amino acid sequence. The application reports multiple scores and a matching fragment list. Fragmentation maps can be exported for publication in either portable network graphic (PNG) or scalable vector graphic (SVG) format. ProSight Lite can be freely downloaded from http://prosightlite.northwestern.edu, installs and updates from the web, and requires Windows 7 or a higher version.",
                        "citationCount": 71,
                        "authors": [
                            {
                                "name": "Fellers R.T."
                            },
                            {
                                "name": "Greer J.B."
                            },
                            {
                                "name": "Early B.P."
                            },
                            {
                                "name": "Yu X."
                            },
                            {
                                "name": "Leduc R.D."
                            },
                            {
                                "name": "Kelleher N.L."
                            },
                            {
                                "name": "Thomas P.M."
                            }
                        ],
                        "date": "2015-04-01T00:00:00Z",
                        "journal": "Proteomics"
                    }
                },
                {
                    "doi": null,
                    "version": null,
                    "pmid": "28150248",
                    "type": "Usage",
                    "pmcid": null,
                    "metadata": {
                        "title": "Bioinformatics analysis of top-down mass spectrometry data with proSight lite",
                        "abstract": "© Springer Science+Business Media LLC 2017.Traditional bottom-up mass spectrometry-based proteomics relies on the use of an enzyme, often trypsin, to generate small peptides (typically < 25 amino acids long). In top-down proteomics, proteins remain intact and are directly measured within the mass spectrometer. This technique, while inherently simpler than bottom-up proteomics, generates data which must be processed and analyzed using software tools “purpose-built” for the job. In this chapter, we will show the analysis of intact protein spectra through deconvolution, deisotoping, and searching with ProSight Lite, a free, vendor-agnostic tool for the analysis of top-down mass spectrometry data. We will illustrate with two examples of intact protein fragmentation spectra and discuss the iterative use of the software to characterize proteoforms and discover the sites of post-translational modifications.",
                        "citationCount": 5,
                        "authors": [
                            {
                                "name": "Dehart C.J."
                            },
                            {
                                "name": "Fellers R.T."
                            },
                            {
                                "name": "Fornelli L."
                            },
                            {
                                "name": "Kelleher N.L."
                            },
                            {
                                "name": "Thomas P.M."
                            }
                        ],
                        "date": "2017-01-01T00:00:00Z",
                        "journal": "Methods in Molecular Biology"
                    }
                }
            ],
            "homepage_status": 0,
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                    "name": null,
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                    "email": "pce@northwestern.edu"
                },
                {
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                    "url": null,
                    "note": null,
                    "orcidid": null,
                    "typeEntity": "Person",
                    "email": "nrtdp-help@northwestern.edu"
                }
            ],
            "biotoolsCURIE": "biotools:prosight_lite",
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            "elixir_badge": 0,
            "homepage": "http://prosightlite.northwestern.edu/",
            "function": [
                {
                    "note": null,
                    "input": [
                        {
                            "data": {
                                "term": "Mass spectrometry data",
                                "uri": "http://edamontology.org/data_2536"
                            },
                            "format": [
                                {
                                    "term": "XML",
                                    "uri": "http://edamontology.org/format_2332"
                                },
                                {
                                    "term": "Textual format",
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                                }
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                        {
                            "data": {
                                "term": "Protein sequence",
                                "uri": "http://edamontology.org/data_2976"
                            },
                            "format": [
                                {
                                    "term": "Textual format",
                                    "uri": "http://edamontology.org/format_2330"
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                                "term": "UniProt accession",
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                                    "term": "UniProtKB format",
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                    ],
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                            "term": "PTM localisation",
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                        },
                        {
                            "term": "PTM identification",
                            "uri": "http://edamontology.org/operation_3645"
                        },
                        {
                            "term": "Post-translation modification site prediction",
                            "uri": "http://edamontology.org/operation_0417"
                        },
                        {
                            "term": "Sequence visualisation",
                            "uri": "http://edamontology.org/operation_0564"
                        },
                        {
                            "term": "Protein sequence analysis",
                            "uri": "http://edamontology.org/operation_2479"
                        },
                        {
                            "term": "Mapping",
                            "uri": "http://edamontology.org/operation_2429"
                        }
                    ],
                    "cmd": null,
                    "output": [
                        {
                            "data": {
                                "term": "Mass spectrometry data",
                                "uri": "http://edamontology.org/data_2536"
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                                {
                                    "term": "XML",
                                    "uri": "http://edamontology.org/format_2332"
                                }
                            ]
                        },
                        {
                            "data": {
                                "term": "Sequence image",
                                "uri": "http://edamontology.org/data_2969"
                            },
                            "format": [
                                {
                                    "term": "png",
                                    "uri": "http://edamontology.org/format_3603"
                                },
                                {
                                    "term": "svg",
                                    "uri": "http://edamontology.org/format_3604"
                                }
                            ]
                        }
                    ]
                }
            ],
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            "description": "Free Windows application for matching a single candidate protein sequence and its modifications against a set of mass spectrometric observations.",
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            "language": [
                "C#"
            ],
            "link": [
                {
                    "url": "http://prosightlite.northwestern.edu/",
                    "note": null,
                    "type": "Registry"
                },
                {
                    "url": "http://www.mybiosoftware.com/prosight-lite-1-0-analyze-top-down-mass-spectrometry-data.html",
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                    "type": "Mirror"
                }
            ],
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            "additionDate": "2017-08-03T18:56:30Z",
            "name": "ProSight Lite",
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                    "url": "http://prosightlite.northwestern.edu/img/ProSight%20Lite%20-%20asms2014.pdf",
                    "note": null,
                    "type": "General"
                }
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        },
        {
            "collectionID": [
                "Czech Republic",
                "Rare Disease",
                "ELIXIR-CZ"
            ],
            "accessibility": [],
            "topic": [
                {
                    "term": "DNA polymorphism",
                    "uri": "http://edamontology.org/topic_2885"
                },
                {
                    "term": "Medical informatics",
                    "uri": "http://edamontology.org/topic_3063"
                }
            ],
            "owner": "Loschmidt Laboratories",
            "cost": "Free of charge (with restrictions)",
            "relation": [],
            "download": [
                {
                    "url": "https://loschmidt.chemi.muni.cz/predictsnp1/docs/predictsnp-1.0.tar.gz",
                    "note": null,
                    "version": null,
                    "type": "Binaries"
                }
            ],
            "validated": 0,
            "publication": [
                {
                    "doi": "10.1371/journal.pcbi.1003440",
                    "version": null,
                    "pmid": null,
                    "type": "Primary",
                    "pmcid": 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.",
                        "citationCount": 185,
                        "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."
                            }
                        ],
                        "date": "2014-01-01T00:00:00Z",
                        "journal": "PLoS Computational Biology"
                    }
                }
            ],
            "homepage_status": 0,
            "credit": [
                {
                    "typeRole": [
                        "Developer"
                    ],
                    "name": "Jaroslav Bendl",
                    "url": null,
                    "note": null,
                    "orcidid": null,
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                    "email": null
                },
                {
                    "typeRole": [
                        "Developer"
                    ],
                    "name": "Jan Brezovsky",
                    "url": null,
                    "note": null,
                    "orcidid": null,
                    "typeEntity": "Person",
                    "email": null
                },
                {
                    "typeRole": [
                        "Developer"
                    ],
                    "name": "Ondrej Salanda",
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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.",
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                            {
                                "name": "Bendl J."
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                            {
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                            {
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                            {
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                        "abstract": "© 2017 The Author. Motivation: Quantitative large-scale cell microscopy is widely used in biological and medical research. Such experiments produce huge amounts of image data and thus require automated analysis. However, automated detection of cell outlines (cell segmentation) is typically challenging due to, e.g. high cell densities, cell-to-cell variability and low signal-to-noise ratios. Results: Here, we evaluate accuracy and speed of various state-of-the-art approaches for cell segmentation in light microscopy images using challenging real and synthetic image data. The results vary between datasets and show that the tested tools are either not robust enough or computationally expensive, thus limiting their application to large-scale experiments. We therefore developed fastER, a trainable tool that is orders of magnitude faster while producing state-of-the-art segmentation quality. It supports various cell types and image acquisition modalities, but is easy-to-use even for non-experts: It has no parameters and can be adapted to specific image sets by interactively labelling cells for training. As a proof of concept, we segment and count cells in over 200 000 brightfield images (1388×1040 pixels each) from a six day time-lapse microscopy experiment; identification of over 46 000 000 single cells requires only about two and a half hours on a desktop computer. Availability and Implementation: C\\+\\+code, binaries and data at https://www.bsse.ethz.ch/csd/soft ware/faster.html.",
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                            {
                                "name": "Hilsenbeck O."
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                            {
                                "name": "Schwarzfischer M."
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                            {
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                                "name": "Marr C."
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                            {
                                "name": "Schroeder T."
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                            {
                                "name": "Dorier J."
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                        "title": "Automated assignment of MS/MS cleavable cross-links in protein 3d-structure analysis",
                        "abstract": "© 2014 American Society for Mass Spectrometry.CID-MS/MS cleavable cross-linkers hold an enormous potential for an automated analysis of cross-linked products, which is essential for conducting structural proteomics studies. The created characteristic fragment ion patterns can easily be used for an automated assignment and discrimination of cross-linked products. To date, there are only a few software solutions available that make use of these properties, but none allows for an automated analysis of cleavable cross-linked products. The MeroX software fills this gap and presents a powerful tool for protein 3D-structure analysis in combination with MS/MS cleavable cross-linkers. We show that MeroX allows an automatic screening of characteristic fragment ions, considering static and variable peptide modifications, and effectively scores different types of cross-links. No manual input is required for a correct assignment of cross-links and false discovery rates are calculated. The self-explanatory graphical user interface of MeroX provides easy access for an automated cross-link search platform that is compatible with commonly used data file formats, enabling analysis of data originating from different instruments. The combination of an MS/MS cleavable cross-linker with a dedicated software tool for data analysis provides an automated workflow for 3D-structure analysis of proteins. MeroX is available at www.StavroX.com.",
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                            {
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                            },
                            {
                                "name": "Ihling C.H."
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                            {
                                "name": "Schafer M."
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                            {
                                "name": "Sinz A."
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                        "title": "Integrated Workflow for Structural Proteomics Studies Based on Cross-Linking/Mass Spectrometry with an MS/MS Cleavable Cross-Linker",
                        "abstract": "© 2016 American Chemical Society.Cross-linking combined with mass spectrometry (MS) has evolved as an alternative strategy in structural biology for characterizing three-dimensional structures of protein assemblies and for mapping protein-protein interactions. Here, we describe an integrated workflow for an automated identification of cross-linked products that is based on the use of a tandem mass spectrometry (MS/MS) cleavable cross-linker (containing a 1,3-bis-(4-oxo-butyl)-urea group, BuUrBu) generating characteristic doublet patterns upon fragmentation. We evaluate different fragmentation methods available on an Orbitrap Fusion mass spectrometer for three proteins and an E. coli cell lysate. An updated version of the dedicated software tool MeroX was employed for a fully automated identification of cross-links. The strength of our cleavable cross-linker is that characteristic patterns of the cross-linker as well as backbone fragments of the connected peptides are already observed at the MS/MS level, eliminating the need for conducting MS3 or sequential CID (collision-induced dissociation)- and ETD (electron transfer dissociation)-MS/MS experiments. This makes our strategy applicable to a broad range of mass spectrometers with MS/MS capabilities. For purified proteins and protein complexes, our workflow using CID-MS/MS acquisition performs with high confidence, scoring cross-links at 0.5% false discovery rate (FDR). The cross-links provide structural insights into the intrinsically disordered tetrameric tumor suppressor protein p53. As a time-consuming manual inspection of cross-linking data is not required, our workflow will pave the way for making the cross-linking/MS approach a routine technique for structural proteomics studies.",
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                            {
                                "name": "Arlt C."
                            },
                            {
                                "name": "Gotze M."
                            },
                            {
                                "name": "Ihling C.H."
                            },
                            {
                                "name": "Hage C."
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                            {
                                "name": "Schafer M."
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                            {
                                "name": "Sinz A."
                            }
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                        "journal": "Analytical Chemistry"
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                        "title": "A cross-linking/mass spectrometry workflow based on MS-cleavable cross-linkers and the MeroX software for studying protein structures and protein–protein interactions",
                        "abstract": "© 2018, Springer Nature Limited. Chemical cross-linking in combination with mass spectrometric analysis of the created cross-linked products is an emerging technology aimed at deriving valuable structural information from proteins and protein complexes. The goal of our protocol is to obtain distance constraints for structure determination of proteins and to investigate protein–protein interactions. We present an integrated workflow for cross-linking/mass spectrometry (MS) based on protein cross-linking with MS-cleavable reagents, followed by enzymatic digestion, enrichment of cross-linked peptides by strong cation-exchange chromatography (SCX), and LC/MS/MS analysis. To exploit the full potential of MS-cleavable cross-linkers, we developed an updated version of the freely available MeroX software for automated data analysis. The commercially available, MS-cleavable cross-linkers (DSBU and CDI) used herein possess different lengths and react with amine as well as hydroxy groups. Owing to the formation of two characteristic 26-u doublets in their MS/MS spectra, many fewer false positives are found than when using classic, non-cleavable cross-linkers. The protocol, exemplified herein for BSA and the whole Escherichia coli ribosome, is robust and widely applicable, and it allows facile identification of cross-links for deriving spatial constraints from purified proteins and protein complexes. The cross-linking/MS procedure takes 2–3 days to complete.",
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                            {
                                "name": "Piotrowski C."
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                        "title": "Testing for differential abundance in mass cytometry data",
                        "abstract": "© 2017 Nature America, Inc., part of Springer Nature. All rights reserved. When comparing biological conditions using mass cytometry data, a key challenge is to identify cellular populations that change in abundance. Here, we present a computational strategy for detecting 'differentially abundant' populations by assigning cells to hyperspheres, testing for significant differences between conditions and controlling the spatial false discovery rate. Our method (http://bioconductor.org/packages/cydar) outperforms other approaches in simulations and finds novel patterns of differential abundance in real data.",
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