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                        "title": "SBOLCanvas: A Visual Editor for Genetic Designs",
                        "abstract": "© 2021 American Chemical Society.SBOLCanvas is a web-based application that can create and edit genetic constructs using the SBOL data and visual standards. SBOLCanvas allows a user to create a genetic design visually and structurally from start to finish. It also allows users to incorporate existing SBOL data from a SynBioHub repository. By the nature of being a web-based application, SBOLCanvas is readily accessible and easy to use. A live version of the latest release can be found at https://sbolcanvas.org.",
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                                "name": "Terry L."
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                    "name": "Yadong Wang",
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            "name": "MAUI",
            "description": "MAUI (MBI Analysis User Interface) is an image processing pipeline for Multiplexed Mass Based Imaging. MIBI_GUI is a set of three graphical user interfaces (GUIs) for low-level analysis of MIBI data. The three steps of low-level analysis (after you've extracted your data) are background removal, denoising, and aggregate removal.",
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                            "term": "Image analysis"
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                    "term": "Cytometry"
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                    "uri": "http://edamontology.org/topic_3382",
                    "term": "Imaging"
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                    "uri": "http://edamontology.org/topic_0749",
                    "term": "Transcription factors and regulatory sites"
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            ],
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            "publication": [
                {
                    "doi": "10.1371/JOURNAL.PCBI.1008887",
                    "pmid": "33872301",
                    "pmcid": "PMC8084329",
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                    "version": null,
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                    "metadata": {
                        "title": "MAUI (MBI Analysis User Interface)—An image processing pipeline for Multiplexed Mass Based Imaging",
                        "abstract": "© 2021 Baranski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Mass Based Imaging (MBI) technologies such as Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and Imaging Mass Cytometry (IMC) allow for the simultaneous measurement of the expression levels of 40 or more proteins in biological tissue, providing insight into cellular phenotypes and organization in situ. Imaging artifacts, resulting from the sample, assay or instrumentation complicate downstream analyses and require correction by domain experts. Here, we present MBI Analysis User Interface (MAUI), a series of graphical user interfaces that facilitate this data pre-processing, including the removal of channel crosstalk, noise and antibody aggregates. Our software streamlines these steps and accelerates processing by enabling real-time and interactive parameter tuning across multiple images.",
                        "date": "2021-04-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Baranski A."
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                            {
                                "name": "Milo I."
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                            {
                                "name": "Greenbaum S."
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                            {
                                "name": "Oliveria J.-P."
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                            {
                                "name": "Mrdjen D."
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                            {
                                "name": "Angelo M."
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                            {
                                "name": "Keren L."
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                        ],
                        "journal": "PLoS Computational Biology"
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                    "term": "Sequencing"
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                    "term": "Medical informatics"
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                    "metadata": {
                        "title": "MainzelHandler: A Library for a Simple Integration and Usage of the Mainzelliste",
                        "abstract": "Pseudonymization plays a vital role in medical research. In Germany, the Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF) has developed guidelines on how to create pseudonyms and how to handle personally identifiable information (PII) during this process. An open-source implementation of a pseudonymization service following these guidelines and therefore recommended by the TMF is the so-called \"Mainzelliste\". This web application supports a REST-API for (de-) pseudonymization. For security reasons, a complex session and tokening mechanism for each (de-) pseudonymization is required and a careful interaction between front- and backend to ensure a correct handling of PII. The objective of this work is the development of a library to simplify the integration and usage of the Mainzelliste's API in a TMF conform way. The frontend library uses JavaScript while the backend component is based on Java with an optional Spring Boot extension. The library is available under MIT open-source license from https://github.com/DanielPreciado-Marquez/MainzelHandler.",
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                            {
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                                "name": "Brix T.J."
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            "description": "LMME (Large Metabolic Model Explorer) is an add-on for the open-source software Visualisation and Analysis of Networks conTaining Experimental Data. (VANTED). It facilitates the exploration of large metabolic models by decomposing the large network into meaningful subsystems and by providing analytic techniques that may be applied successively.",
            "homepage": "http://www.cls.uni-konstanz.de/software/lmme",
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                            "term": "Metabolic network modelling"
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                            "term": "Network visualisation"
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                    "term": "Endocrinology and metabolism"
                },
                {
                    "uri": "http://edamontology.org/topic_0605",
                    "term": "Informatics"
                },
                {
                    "uri": "http://edamontology.org/topic_3070",
                    "term": "Biology"
                },
                {
                    "uri": "http://edamontology.org/topic_0202",
                    "term": "Pharmacology"
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                    "term": "Small molecules"
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            "name": "JEDi",
            "description": "JEDi (Java Essential Dynamics Inspector) is a powerful tool for inspecting the dynamics of proteins from trajectories derived from MD or Geometric simulation.",
            "homepage": "https://github.com/charlesdavid/JEDi",
            "biotoolsID": "jedi-md",
            "biotoolsCURIE": "biotools:jedi-md",
            "version": [],
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                            "uri": "http://edamontology.org/operation_3891",
                            "term": "Essential dynamics"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2939",
                            "term": "Principal component visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3960",
                            "term": "Principal component analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2476",
                            "term": "Molecular dynamics"
                        }
                    ],
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                    "output": [],
                    "note": null,
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0176",
                    "term": "Molecular dynamics"
                },
                {
                    "uri": "http://edamontology.org/topic_2269",
                    "term": "Statistics and probability"
                },
                {
                    "uri": "http://edamontology.org/topic_3382",
                    "term": "Imaging"
                }
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                    "metadata": {
                        "title": "JEDi: java essential dynamics inspector — a molecular trajectory analysis toolkit",
                        "abstract": "© 2021, The Author(s).Background: Principal component analysis (PCA) is commonly applied to the atomic trajectories of biopolymers to extract essential dynamics that describe biologically relevant motions. Although application of PCA is straightforward, specialized software to facilitate workflows and analysis of molecular dynamics simulation data to fully harness the power of PCA is lacking. The Java Essential Dynamics inspector (JEDi) software is a major upgrade from the previous JED software. Results: Employing multi-threading, JEDi features a user-friendly interface to control rapid workflows for interrogating conformational motions of biopolymers at various spatial resolutions and within subregions, including multiple chain proteins. JEDi has options for Cartesian-based coordinates (cPCA) and internal distance pair coordinates (dpPCA) to construct covariance (Q), correlation (R), and partial correlation (P) matrices. Shrinkage and outlier thresholding are implemented for the accurate estimation of covariance. The effect of rare events is quantified using outlier and inlier filters. Applying sparsity thresholds in statistical models identifies latent correlated motions. Within a hierarchical approach, small-scale atomic motion is first calculated with a separate local cPCA calculation per residue to obtain eigenresidues. Then PCA on the eigenresidues yields rapid and accurate description of large-scale motions. Local cPCA on all residue pairs creates a map of all residue-residue dynamical couplings. Additionally, kernel PCA is implemented. JEDi output gives high quality PNG images by default, with options for text files that include aligned coordinates, several metrics that quantify mobility, PCA modes with their eigenvalues, and displacement vector projections onto the top principal modes. JEDi provides PyMol scripts together with PDB files to visualize individual cPCA modes and the essential dynamics occurring within user-selected time scales. Subspace comparisons performed on the most relevant eigenvectors using several statistical metrics quantify similarity/overlap of high dimensional vector spaces. Free energy landscapes are available for both cPCA and dpPCA. Conclusion: JEDi is a convenient toolkit that applies best practices in multivariate statistics for comparative studies on the essential dynamics of similar biopolymers. JEDi helps identify functional mechanisms through many integrated tools and visual aids for inspecting and quantifying similarity/differences in mobility and dynamic correlations.",
                        "date": "2021-12-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "David C.C."
                            },
                            {
                                "name": "Avery C.S."
                            },
                            {
                                "name": "Jacobs D.J."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
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                    "name": "Charles C. David",
                    "email": "Charles.David@plantandfood.co.nz",
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        {
            "name": "Jasmine",
            "description": "JASMINE (Jointly Accurate Sv Merging with Intersample Network Edges) is an automated pipeline for alignment and SV calling in long-read datasets. The tool is used to merge structural variants (SVs) across samples. Each sample has a number of SV calls, consisting of position information (chromosome, start, end, length), type and strand information, and a number of other values. Jasmine represents the set of all SVs across samples as a network, and uses a modified minimum spanning forest algorithm to determine the best way of merging the variants such that each merged variants represents a set of analogous variants occurring in different samples.",
            "homepage": "https://github.com/mkirsche/Jasmine",
            "biotoolsID": "jasmine-sv",
            "biotoolsCURIE": "biotools:jasmine-sv",
            "version": [],
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            "function": [
                {
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                        {
                            "uri": "http://edamontology.org/operation_3196",
                            "term": "Genotyping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3227",
                            "term": "Variant calling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3211",
                            "term": "Genome indexing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Workflow"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3175",
                    "term": "Structural variation"
                },
                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_0769",
                    "term": "Workflows"
                },
                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Java",
                "Python"
            ],
            "license": "MIT",
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                    "url": "http://data.schatz-lab.org/jasmine/",
                    "type": [
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                    "url": "https://github.com/mkirsche/Jasmine/tree/master/pipeline",
                    "type": "Source code",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://github.com/mkirsche/Jasmine/wiki/Jasmine-User-Manual",
                    "type": [
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                    "note": null
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            ],
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                {
                    "doi": "10.1101/2021.05.27.445886",
                    "pmid": null,
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                    "type": [
                        "Primary"
                    ],
                    "version": null,
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                }
            ],
            "credit": [
                {
                    "name": "Sergey Aganezov",
                    "email": "sergeyaganezovjr@gmail.com",
                    "url": null,
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                },
                {
                    "name": "Michael C. Schatz",
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        },
        {
            "name": "PhosphoPredict",
            "description": "Novel bioinformatics approach to predict kinase-specific phosphorylation substrates and sites in the human proteome by combining informative protein sequence and functional features to build the prediction models using random forest (RF).",
            "homepage": "http://phosphopredict.erc.monash.edu/",
            "biotoolsID": "phosphopredict",
            "biotoolsCURIE": "biotools:phosphopredict",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3092",
                            "term": "Protein feature detection"
                        }
                    ],
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                    "output": [],
                    "note": null,
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            ],
            "toolType": [
                "Web application",
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
                {
                    "uri": "http://edamontology.org/topic_0821",
                    "term": "Enzymes"
                },
                {
                    "uri": "http://edamontology.org/topic_2815",
                    "term": "Human biology"
                },
                {
                    "uri": "http://edamontology.org/topic_3510",
                    "term": "Protein sites, features and motifs"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
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                "Java"
            ],
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            "documentation": [
                {
                    "url": "http://phosphopredict.erc.monash.edu/",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1038/s41598-017-07199-4",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PhosphoPredict: A bioinformatics tool for prediction of human kinase-specific phosphorylation substrates and sites by integrating heterogeneous feature selection",
                        "abstract": "© 2017 The Author(s).Protein phosphorylation is a major form of post-translational modification (PTM) that regulates diverse cellular processes. In silico methods for phosphorylation site prediction can provide a useful and complementary strategy for complete phosphoproteome annotation. Here, we present a novel bioinformatics tool, PhosphoPredict, that combines protein sequence and functional features to predict kinase-specific substrates and their associated phosphorylation sites for 12 human kinases and kinase families, including ATM, CDKs, GSK-3, MAPKs, PKA, PKB, PKC, and SRC. To elucidate critical determinants, we identified feature subsets that were most informative and relevant for predicting substrate specificity for each individual kinase family. Extensive benchmarking experiments based on both five-fold cross-validation and independent tests indicated that the performance of PhosphoPredict is competitive with that of several other popular prediction tools, including KinasePhos, PPSP, GPS, and Musite. We found that combining protein functional and sequence features significantly improves phosphorylation site prediction performance across all kinases. Application of PhosphoPredict to the entire human proteome identified 150 to 800 potential phosphorylation substrates for each of the 12 kinases or kinase families. PhosphoPredict significantly extends the bioinformatics portfolio for kinase function analysis and will facilitate high-throughput identification of kinase-specific phosphorylation sites, thereby contributing to both basic and translational research programs.",
                        "date": "2017-12-01T00:00:00Z",
                        "citationCount": 41,
                        "authors": [
                            {
                                "name": "Song J."
                            },
                            {
                                "name": "Wang H."
                            },
                            {
                                "name": "Wang J."
                            },
                            {
                                "name": "Leier A."
                            },
                            {
                                "name": "Marquez-Lago T."
                            },
                            {
                                "name": "Yang B."
                            },
                            {
                                "name": "Zhang Z."
                            },
                            {
                                "name": "Akutsu T."
                            },
                            {
                                "name": "Webb G.I."
                            },
                            {
                                "name": "Daly R.J."
                            }
                        ],
                        "journal": "Scientific Reports"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Support",
                    "email": "jiangning.song@monash.edu",
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            ],
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            "owner": "d.gabrielaitis",
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