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        {
            "name": "VirusHound-I",
            "description": "Prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation.",
            "homepage": "https://www.biochemintelli.com/VirusHound-I",
            "biotoolsID": "virushound-i",
            "biotoolsCURIE": "biotools:virushound-i",
            "version": [],
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            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3461",
                            "term": "Virulence prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0252",
                            "term": "Peptide immunogenicity prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0416",
                            "term": "Epitope mapping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3431",
                            "term": "Deposition"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
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            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_3948",
                    "term": "Immunoinformatics"
                },
                {
                    "uri": "http://edamontology.org/topic_3930",
                    "term": "Immunogenetics"
                },
                {
                    "uri": "http://edamontology.org/topic_2830",
                    "term": "Immunoproteins and antigens"
                },
                {
                    "uri": "http://edamontology.org/topic_0202",
                    "term": "Pharmacology"
                }
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            "link": [
                {
                    "url": "https://github.com/jfbldevs/virushound-I",
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            "publication": [
                {
                    "doi": "10.1093/BIB/BBAD434",
                    "pmid": "38033292",
                    "pmcid": "PMC10753651",
                    "type": [],
                    "version": null,
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                    "metadata": {
                        "title": "VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation",
                        "abstract": "Throughout evolution, pathogenic viruses have developed different strategies to evade the response of the adaptive immune system. To carry out successful replication, some pathogenic viruses encode different proteins that manipulate the molecular mechanisms of host cells. Currently, there are different bioinformatics tools for virus research; however, none of them focus on predicting viral proteins that evade the adaptive system. In this work, we have developed a novel tool based on machine and deep learning for predicting this type of viral protein named VirusHound-I. This tool is based on a model developed with the multilayer perceptron algorithm using the dipeptide composition molecular descriptor. In this study, we have also demonstrated the robustness of our strategy for data augmentation of the positive dataset based on generative antagonistic networks. During the 10-fold cross-validation step in the training dataset, the predictive model showed 0.947 accuracy, 0.994 precision, 0.943 F1 score, 0.995 specificity, 0.896 sensitivity, 0.894 kappa, 0.898 Matthew’s correlation coefficient and 0.989 AUC. On the other hand, during the testing step, the model showed 0.964 accuracy, 1.0 precision, 0.967 F1 score, 1.0 specificity, 0.936 sensitivity, 0.929 kappa, 0.931 Matthew’s correlation coefficient and 1.0 AUC. Taking this model into account, we have developed a tool called VirusHound-I that makes it possible to predict viral proteins that evade the host’s adaptive immune system. We believe that VirusHound-I can be very useful in accelerating studies on the molecular mechanisms of evasion of pathogenic viruses, as well as in the discovery of therapeutic targets.",
                        "date": "2024-01-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Beltran J.F."
                            },
                            {
                                "name": "Belen L.H."
                            },
                            {
                                "name": "Farias J.G."
                            },
                            {
                                "name": "Zamorano M."
                            },
                            {
                                "name": "Lefin N."
                            },
                            {
                                "name": "Miranda J."
                            },
                            {
                                "name": "Parraguez-Contreras F."
                            }
                        ],
                        "journal": "Briefings in Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Jorge F Beltrán",
                    "email": "beltran.lissabet.jf@gmail.com",
                    "url": null,
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                    "name": "Fernanda Parraguez-Contreras",
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        {
            "name": "JSONWP",
            "description": "Static website generator for protein bioinformatics research.\n\nIt is a JS application that creates a website based on the JSON data.",
            "homepage": "http://jsonwp.onrender.com",
            "biotoolsID": "jsonwp",
            "biotoolsCURIE": "biotools:jsonwp",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_1812",
                            "term": "Parsing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2422",
                            "term": "Data retrieval"
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                    ],
                    "input": [],
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                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
                {
                    "uri": "http://edamontology.org/topic_0091",
                    "term": "Bioinformatics"
                },
                {
                    "uri": "http://edamontology.org/topic_3372",
                    "term": "Software engineering"
                }
            ],
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                "Linux",
                "Windows"
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                "JavaScript"
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            "link": [
                {
                    "url": "https://github.com/MesihK/react-json-wpbuilder",
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            "publication": [
                {
                    "doi": "10.1093/BIOADV/VBAD154",
                    "pmid": "37904893",
                    "pmcid": "PMC10613403",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "JSONWP: a static website generator for protein bioinformatics research",
                        "abstract": "Motivation: Presenting the integrated results of bioinformatics research can be challenging and requires sophisticated visualization components, which can be time-consuming to develop. This article presents a new way to effectively communicate research findings. Results: We have developed a static web page generator, JSONWP, which is specifically designed for protein bioinformatics research. Utilizing React (a JavaScript library used to build interactive and dynamic user interfaces for web applications), we have integrated publicly available bioinformatics visualization components to provide standardized access to these components. JSON (or JavaScript Object Notation, is a lightweight textual data format often used to structure and exchange information between different software tools.) is used as the input source due to its ability to represent nearly all types of data using key and value pairs. This allows researchers to use their preferred programming language to create a JSON representation, which can then be converted into a website by JSONWP. No server or domain is required to host the website, as only the publicly accessible JSON file is required. Conclusions: Overall, JSONWP provides a useful new tool for bioinformatics researchers to effectively communicate their findings. The open-source implementation is located at https://github.com/MesihK/react-json-wpbuilder, and the tool can be used at jsonwp.onrender.com.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Kilinc M."
                            },
                            {
                                "name": "Jia K."
                            },
                            {
                                "name": "Jernigan R.L."
                            }
                        ],
                        "journal": "Bioinformatics Advances"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Robert L Jernigan",
                    "email": "jernigan@iastate.edu",
                    "url": null,
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                },
                {
                    "name": "Mesih Kilinc",
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        {
            "name": "Cell4D",
            "description": "Cell4D is a Linux C++-based graphical spatial stochatic cell simulator capable of simulating a wide variety of cellular pathways. Molecules are simulated as particles within a user-defined simulation space under a Smoluchowski-based reaction-diffusion system on a static time-step basis.",
            "homepage": "https://github.com/ParkinsonLab/cell4d",
            "biotoolsID": "cell4d",
            "biotoolsCURIE": "biotools:cell4d",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3928",
                            "term": "Pathway analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2426",
                            "term": "Modelling and simulation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0153",
                    "term": "Lipids"
                },
                {
                    "uri": "http://edamontology.org/topic_0749",
                    "term": "Transcription factors and regulatory sites"
                }
            ],
            "operatingSystem": [],
            "language": [
                "C++"
            ],
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            "cost": "Free of charge",
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            "publication": [
                {
                    "doi": "10.1186/s12859-024-05739-0",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Cell4D: a general purpose spatial stochastic simulator for cellular pathways",
                        "abstract": "Background: With the generation of vast compendia of biological datasets, the challenge is how best to interpret ‘omics data alongside biochemical and other small-scale experiments to gain meaningful biological insights. Key to this challenge are computational methods that enable domain-users to generate novel hypotheses that can be used to guide future experiments. Of particular interest are flexible modeling platforms, capable of simulating a diverse range of biological systems with low barriers of adoption to those with limited computational expertise. Results: We introduce Cell4D, a spatial-temporal modeling platform combining a robust simulation engine with integrated graphics visualization, a model design editor, and an underlying XML data model capable of capturing a variety of cellular functions. Cell4D provides an interactive visualization mode, allowing intuitive feedback on model behavior and exploration of novel hypotheses, together with a non-graphics mode, compatible with high performance cloud compute solutions, to facilitate generation of statistical data. To demonstrate the flexibility and effectiveness of Cell4D, we investigate the dynamics of CEACAM1 localization in T-cell activation. We confirm the importance of Ca2+ microdomains in activating calmodulin and highlight a key role of activated calmodulin on the surface expression of CEACAM1. We further show how lymphocyte-specific protein tyrosine kinase can help regulate this cell surface expression and exploit spatial modeling features of Cell4D to test the hypothesis that lipid rafts regulate clustering of CEACAM1 to promote trans-binding to neighbouring cells. Conclusions: Through demonstrating its ability to test and generate hypotheses, Cell4D represents an effective tool to help integrate knowledge across diverse, large and small-scale datasets.",
                        "date": "2024-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Chan D."
                            },
                            {
                                "name": "Cromar G.L."
                            },
                            {
                                "name": "Taj B."
                            },
                            {
                                "name": "Parkinson J."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "John Parkinson",
                    "email": "jparkin@sickkids.ca",
                    "url": null,
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                },
                {
                    "name": "Donny Chan",
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                    "orcidid": null,
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            "owner": "Pub2Tools",
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        },
        {
            "name": "JeasyTFM",
            "description": "Open-source software package for the analysis of large 2D TFM data within ImageJ.",
            "homepage": "http://questpharma.u-strasbg.fr/JEasyTFM.html",
            "biotoolsID": "jeasytfm",
            "biotoolsCURIE": "biotools:jeasytfm",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3359",
                            "term": "Splitting"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3096",
                            "term": "Editing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3443",
                            "term": "Image analysis"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Library"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3382",
                    "term": "Imaging"
                },
                {
                    "uri": "http://edamontology.org/topic_3934",
                    "term": "Cytometry"
                },
                {
                    "uri": "http://edamontology.org/topic_3318",
                    "term": "Physics"
                },
                {
                    "uri": "http://edamontology.org/topic_0108",
                    "term": "Protein expression"
                }
            ],
            "operatingSystem": [],
            "language": [],
            "license": null,
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            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
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            "elixirNode": [],
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            "link": [],
            "download": [],
            "documentation": [
                {
                    "url": "http://questpharma.u-strasbg.fr/JEasyTFM_manual.html",
                    "type": [
                        "User manual"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/BIOADV/VBAD156",
                    "pmid": "37928344",
                    "pmcid": "PMC10625472",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "JEasyTFM: an open-source software package for the analysis of large 2D TFM data within ImageJ",
                        "abstract": "Motivation: Cells adhering to the extracellular matrix can sense and respond to a wide variety of chemical and physical features of the adhesive surface. Traction force microscopy (TFM) allows determining the tensile forces exerted by the cells on their substrate with high resolution. Results: To allow broad access of this techniques to cell biology laboratories we developed JeasyTFM, an open-source ImageJ package able to process multi-color and multi-position time-lapse pictures thus suitable for the automatic analysis of large TFM data.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Carl P."
                            },
                            {
                                "name": "Ronde P."
                            }
                        ],
                        "journal": "Bioinformatics Advances"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Philippe Carl",
                    "email": "philippe.carl@unistra.fr",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-8063-3294",
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                },
                {
                    "name": "Philippe Rondé",
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                    "url": null,
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            "owner": "Pub2Tools",
            "additionDate": "2024-04-30T12:35:46.193814Z",
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        },
        {
            "name": "syntenyPlotteR",
            "description": "User-friendly R package to visualize genome synteny, ideal for both experienced and novice bioinformaticians.",
            "homepage": "https://farre-lab.github.io/syntenyPlotteR/",
            "biotoolsID": "syntenyplotter",
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
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                        {
                            "uri": "http://edamontology.org/operation_3216",
                            "term": "Scaffolding"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0525",
                            "term": "Genome assembly"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Library"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_3175",
                    "term": "Structural variation"
                },
                {
                    "uri": "http://edamontology.org/topic_0196",
                    "term": "Sequence assembly"
                },
                {
                    "uri": "http://edamontology.org/topic_3299",
                    "term": "Evolutionary biology"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "R"
            ],
            "license": null,
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            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1093/BIOADV/VBAD161",
                    "pmid": "38023328",
                    "pmcid": "PMC10660287",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "syntenyPlotteR: a user-friendly R package to visualize genome synteny, ideal for both experienced and novice bioinformaticians",
                        "abstract": "Motivation: The rapid increase in the number of chromosome-scale genome assemblies has renewed interest in chromosome evolution studies. The visualization of syntenic relationships between genomes is a crucial initial step in the study of chromosome rearrangements and evolution. There are few tools available that serve this purpose, and they can be difficult to learn. Moreover, these tools are limited in the number of species comparisons that can be visualized and the size of chromosome rearrangements identified. Thus, the development of novel visualization tools is in strong need. Results: Here, we present syntenyPlotteR, an R package developed to visualize homologous synteny blocks in a pairwise or multispecies manner. This package contains three functions that allow users to generate publication-quality representations of syntenic relationships easily and quickly between genomes of interest.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Quigley S."
                            },
                            {
                                "name": "Damas J."
                            },
                            {
                                "name": "Larkin D.M."
                            },
                            {
                                "name": "Farre M."
                            }
                        ],
                        "journal": "Bioinformatics Advances"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Marta Farré",
                    "email": "m.farre-belmonte@kent.ac.uk",
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                },
                {
                    "name": "Sarah Quigley",
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            "owner": "Pub2Tools",
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        },
        {
            "name": "DegronMD",
            "description": "DegronMD: protein-targeted degradation, mutation and drug response to degrons.",
            "homepage": "https://bioinfo.uth.edu/degronmd",
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                    "term": "Protein targeting and localisation"
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                    "metadata": {
                        "title": "DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons",
                        "abstract": "Protein-Targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as degrons. The human genome encodes over 600 E3 ligases; however, only a small number of targeted degron instances have been identified so far. In this study, we introduced DegronMD, an open knowledgebase designed for the investigation of degrons, their associated dysfunctional events, and drug responses. We revealed that degrons are evolutionarily conserved and tend to occur near the sites of protein translational modifications, particularly in the regions of disordered structure and higher solvent accessibility. Through pattern recognition and machine learning techniques, we constructed the degrome landscape across the human proteome, yielding over 18,000 new degrons for targeted protein degradation. Furthermore, dysfunction of degrons disrupts the degradation process and leads to the abnormal accumulation of proteins; this process is associated with various types of human cancers. Based on the estimated phenotypic changes induced by somatic mutations, we systematically quantified and assessed the impact of mutations on degron function in pan-cancers; these results helped to build a global mutational map on human degrome, including 89,318 actionable mutations that may induce the dysfunction of degrons and disrupt protein degradation pathways. Multiomics integrative analysis unveiled over 400 drug resistance events associated with the mutations in functional degrons. DegronMD, accessible at https://bioinfo.uth.edu/degronmd, is a useful resource to explore the biological mechanisms, infer protein degradation, and assist with drug discovery and design on degrons.",
                        "date": "2023-12-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Xu H."
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                            {
                                "name": "Hu R."
                            },
                            {
                                "name": "Zhao Z."
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                        "journal": "Molecular Biology and Evolution"
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                    "term": "Metabolomics"
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                    "metadata": {
                        "title": "ProbioMinServer: an integrated platform for assessing the safety and functional properties of potential probiotic strains",
                        "abstract": "Motivation: ProbioMinServer is a platform designed to help researchers access information on probiotics regarding a wide variety of characteristics, such as safety (e.g. antimicrobial resistance, virulence, pathogenic, plasmid, and prophage genes) and functionality (e.g. functional classes, carbohydrate-active enzyme, and metabolite gene cluster profile). Because probiotics are functional foods, their safety and functionality are a crucial part of health care. Genomics has become a crucial methodology for investigating the safety and functionality of probiotics in food and feed. This shift is primarily attributed to the growing affordability of next-generation sequencing technologies. However, no integrated platform is available for simultaneously evaluating probiotic strain safety, investigating probiotic functionality, and identifying known phylogenetically related strains. Results: Thus, we constructed a new platform, ProbioMinServer, which incorporates these functions. ProbioMinServer accepts whole-genome sequence files in the FASTA format. If the query genome belongs to the 25 common probiotic species collected in our database, the server performs a database search and analyzes the core-genome multilocus sequence typing. Front-end applications were implemented in JavaScript with a bootstrap framework, and back-end programs were implemented using PHP, Perl, and Python. ProbioMinServer can help researchers quickly and easily retrieve information on the safety and functionality of various probiotics.",
                        "date": "2023-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Liu Y.-Y."
                            },
                            {
                                "name": "Hsu C.-Y."
                            },
                            {
                                "name": "Yang Y.-C."
                            },
                            {
                                "name": "Huang C.-H."
                            },
                            {
                                "name": "Chen C.-C."
                            }
                        ],
                        "journal": "Bioinformatics Advances"
                    }
                }
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                {
                    "name": "Chih-Chieh Chen",
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                    "name": "Yen-Yi Liu",
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                            "uri": "http://edamontology.org/operation_0239",
                            "term": "Sequence motif recognition"
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                            "uri": "http://edamontology.org/operation_0314",
                            "term": "Gene expression profiling"
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                            "uri": "http://edamontology.org/operation_3450",
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                    "term": "Neurobiology"
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                    "uri": "http://edamontology.org/topic_3334",
                    "term": "Neurology"
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                    "uri": "http://edamontology.org/topic_0634",
                    "term": "Pathology"
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                    "term": "Transcriptomics"
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                    "term": "Transcription factors and regulatory sites"
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                    "metadata": {
                        "title": "SCAN: Spatiotemporal Cloud Atlas for Neural cells",
                        "abstract": "The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer’s disease, Parkinson’s disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.",
                        "date": "2024-01-05T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Deng Y."
                            },
                            {
                                "name": "Lu Y."
                            },
                            {
                                "name": "Li M."
                            },
                            {
                                "name": "Shen J."
                            },
                            {
                                "name": "Qin S."
                            },
                            {
                                "name": "Zhang W."
                            },
                            {
                                "name": "Zhang Q."
                            },
                            {
                                "name": "Shen Z."
                            },
                            {
                                "name": "Li C."
                            },
                            {
                                "name": "Jia T."
                            },
                            {
                                "name": "Chen P."
                            },
                            {
                                "name": "Peng L."
                            },
                            {
                                "name": "Chen Y."
                            },
                            {
                                "name": "Zhang W."
                            },
                            {
                                "name": "Liu H."
                            },
                            {
                                "name": "Zhang L."
                            },
                            {
                                "name": "Rong L."
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                            {
                                "name": "Wang X."
                            },
                            {
                                "name": "Chen D."
                            }
                        ],
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                    }
                }
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                {
                    "name": "Liangming Zhang",
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                            "term": "Gene expression profiling"
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                    "term": "Cell biology"
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                    "metadata": {
                        "title": "LncPCD: a manually curated database of experimentally supported associations between lncRNA-mediated programmed cell death and diseases",
                        "abstract": "Programmed cell death (PCD) refers to controlled cell death that is conducted to keep the internal environment stable. Long noncoding RNAs (lncRNAs) participate in the progression of PCD in a variety of diseases. However, no specialized online repository is available to collect and store the associations between lncRNA-mediated PCD and diseases. Here, we developed LncPCD, a comprehensive database that provides information on experimentally supported associations of lncRNA-mediated PCD with diseases. The current version of LncPCD documents 6666 associations between five common types of PCD (apoptosis, autophagy, ferroptosis, necroptosis and pyroptosis) and 1222 lncRNAs in 331 diseases.We also manually curated a wealth of information: (1) 7 important lncRNA regulatory mechanisms, (2) 310 PCD-associated cell types in three species, (3) detailed information on lncRNA subcellular locations and (4) clinical applications for lncRNA-mediated PCD in diseases. Additionally, 10 single-cell sequencing datasets were integrated into LncPCD to characterize the dynamics of lncRNAs in diseases. Overall, LncPCD is an extremely useful resource for understanding the functions and mechanisms of lncRNA-mediated PCD in diseases.",
                        "date": "2023-01-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "He N."
                            },
                            {
                                "name": "Li D."
                            },
                            {
                                "name": "Xu F."
                            },
                            {
                                "name": "Jin J."
                            },
                            {
                                "name": "Li L."
                            },
                            {
                                "name": "Tian L."
                            },
                            {
                                "name": "Chen B."
                            },
                            {
                                "name": "Li X."
                            },
                            {
                                "name": "Ning S."
                            },
                            {
                                "name": "Wang L."
                            },
                            {
                                "name": "Wang J."
                            }
                        ],
                        "journal": "Database"
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                }
            ],
            "credit": [
                {
                    "name": "Shangwei Ning",
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                    "metadata": {
                        "title": "ATLA S: prot ein flexibility description fromãtomistic molecular dynamics simulations",
                        "abstract": "Dynamical behaviour is one of the most crucial protein characteristics. Despite theãdvances in the field of protein str uct ure resolutionãnd predic- tion,ãnalysisãnd prediction of protein dynamic properties remainsã major challenge, mostly due to the lowãccessibility of dataãnd its diversityãnd heterogeneity. Toãddress this issue, we present ATLA S ,ã database of standardisedãll-atom molecular dynamics simulations,ãccompanied by theirãnalysis in the form of interactive diagramsãnd trajectory visualisation. ATLAS offersã large-scale viewãnd valuable insights on protein dynamics forã largeãnd representative set of proteins, by combining data obtained through molecular dynamics simulations with information e xtracted from e xperimental str uct ures. Users can easilyãnalyse dynamic properties of functional protein regions, suchãs domain limits (hinge positions)ãnd residues in v olv ed in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment suchãs chameleon subsequencesãnd Dual Personality Fragments. The ATLAS database is freelyã vãilableãt https:// www.dsimb.inserm.fr/ ATLAS .",
                        "date": "2024-01-05T00:00:00Z",
                        "citationCount": 3,
                        "authors": [
                            {
                                "name": "Meersche Y.V."
                            },
                            {
                                "name": "Cretin G."
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                                "name": "Gheeraert A."
                            },
                            {
                                "name": "Gelly J.-C."
                            },
                            {
                                "name": "Galochkina T."
                            }
                        ],
                        "journal": "Nucleic Acids Research"
                    }
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            "credit": [
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                    "name": "Jean-Christophe Gelly",
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