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            "name": "SAM",
            "description": "Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials.",
            "homepage": "https://cran.r-project.org/web/packages/SAMprior",
            "biotoolsID": "sam_clinical",
            "biotoolsCURIE": "biotools:sam_clinical",
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                            "uri": "http://edamontology.org/operation_2422",
                            "term": "Data retrieval"
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                            "uri": "http://edamontology.org/operation_0335",
                            "term": "Formatting"
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                            "uri": "http://edamontology.org/operation_3436",
                            "term": "Aggregation"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3379",
                    "term": "Preclinical and clinical studies"
                },
                {
                    "uri": "http://edamontology.org/topic_2269",
                    "term": "Statistics and probability"
                },
                {
                    "uri": "http://edamontology.org/topic_3325",
                    "term": "Rare diseases"
                }
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                "C",
                "Python",
                "R"
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                    "url": "https://github.com/pengyang0411/SAMprior",
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                        "Repository"
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            "publication": [
                {
                    "doi": "10.1111/BIOM.13927",
                    "pmid": "37721513",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials",
                        "abstract": "Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factors. SAM priors are data-driven and self-adapting, favoring the informative (noninformative) prior component when there is little (substantial) evidence of prior-data conflict. Consequently, SAM priors achieve dynamic information borrowing. We demonstrate that SAM priors exhibit desirable properties in both finite and large samples and achieve information-borrowing consistency. Moreover, SAM priors are easy to compute, data-driven, and calibration-free, mitigating the risk of data dredging. Numerical studies show that SAM priors outperform existing methods in adopting prior-data conflicts effectively. We developed R package “SAMprior” and web application that are freely available at CRAN and www.trialdesign.org to facilitate the use of SAM priors.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Yang P."
                            },
                            {
                                "name": "Zhao Y."
                            },
                            {
                                "name": "Nie L."
                            },
                            {
                                "name": "Vallejo J."
                            },
                            {
                                "name": "Yuan Y."
                            }
                        ],
                        "journal": "Biometrics"
                    }
                },
                {
                    "doi": "10.1093/BIB/BBAD320",
                    "pmid": "37668049",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Sequence Alignment/Map format: A comprehensive review of approaches and applications",
                        "abstract": "The Sequence Alignment/Map (SAM) format file is the text file used to record alignment information. Alignment is the core of sequencing analysis, and downstream tasks accept mapping results for further processing. Given the rapid development of the sequencing industry today, a comprehensive understanding of the SAM format and related tools is necessary to meet the challenges of data processing and analysis. This paper is devoted to retrieving knowledge in the broad field of SAM. First, the format of SAM is introduced to understand the overall process of the sequencing analysis. Then, existing work is systematically classified in accordance with generation, compression and application, and the involved SAM tools are specifically mined. Lastly, a summary and some thoughts on future directions are provided.",
                        "date": "2023-09-01T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Liu Y."
                            },
                            {
                                "name": "Shen X."
                            },
                            {
                                "name": "Gong Y."
                            },
                            {
                                "name": "Liu Y."
                            },
                            {
                                "name": "Song B."
                            },
                            {
                                "name": "Zeng X."
                            }
                        ],
                        "journal": "Briefings in Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Xiangxiang Zeng",
                    "email": "Zengzxeng@hnu.edu.cn",
                    "url": null,
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            "name": "DeepCCI",
            "description": "A deep learning framework for identifying cell-cell interactions from single-cell rna sequencing data.",
            "homepage": "https://github.com/JiangBioLab/DeepCCI",
            "biotoolsID": "deepcci",
            "biotoolsCURIE": "biotools:deepcci",
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0314",
                            "term": "Gene expression profiling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0313",
                            "term": "Expression profile clustering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3435",
                            "term": "Standardisation and normalisation"
                        }
                    ],
                    "input": [],
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                    "note": null,
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            ],
            "toolType": [
                "Command-line tool"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_0099",
                    "term": "RNA"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
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                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
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                {
                    "doi": "10.1093/BIOINFORMATICS/BTAD596",
                    "pmid": "37740953",
                    "pmcid": "PMC10558043",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data",
                        "abstract": "Motivation: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. Results: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data.",
                        "date": "2023-10-01T00:00:00Z",
                        "citationCount": 3,
                        "authors": [
                            {
                                "name": "Yang W."
                            },
                            {
                                "name": "Wang P."
                            },
                            {
                                "name": "Luo M."
                            },
                            {
                                "name": "Cai Y."
                            },
                            {
                                "name": "Xu C."
                            },
                            {
                                "name": "Xue G."
                            },
                            {
                                "name": "Jin X."
                            },
                            {
                                "name": "Cheng R."
                            },
                            {
                                "name": "Que J."
                            },
                            {
                                "name": "Pang F."
                            },
                            {
                                "name": "Yang Y."
                            },
                            {
                                "name": "Nie H."
                            },
                            {
                                "name": "Jiang Q."
                            },
                            {
                                "name": "Liu Z."
                            },
                            {
                                "name": "Xu Z."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Qinghua Jiang",
                    "email": "qhjiang@hit.edu.cn",
                    "url": null,
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                    "name": "Zhigang Liu",
                    "email": null,
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                },
                {
                    "name": "Zhaochun Xu",
                    "email": "jdzxuzhaochun@163.com",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-1799-5529",
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        {
            "name": "Tcbf",
            "description": "A novel user-friendly tool for pan-3 d genome analysis of topologically associating domain in eukaryotic organisms.",
            "homepage": "https://github.com/TcbfGroup/Tcbf",
            "biotoolsID": "tcbf",
            "biotoolsCURIE": "biotools:tcbf",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3182",
                            "term": "Genome alignment"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
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            "toolType": [
                "Workflow"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
                },
                {
                    "uri": "http://edamontology.org/topic_0204",
                    "term": "Gene regulation"
                },
                {
                    "uri": "http://edamontology.org/topic_3175",
                    "term": "Structural variation"
                },
                {
                    "uri": "http://edamontology.org/topic_0780",
                    "term": "Plant biology"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
            "language": [
                "Python",
                "R"
            ],
            "license": "MIT",
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            "publication": [
                {
                    "doi": "10.1093/BIOINFORMATICS/BTAD576",
                    "pmid": "37725346",
                    "pmcid": "PMC10539074",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Tcbf: A novel user-friendly tool for pan-3D genome analysis of topologically associating domain in eukaryotic organisms",
                        "abstract": "Summary: TAD boundaries are essential for organizing the chromatin spatial structure and regulating gene expression in eukaryotes. However, for large-scale pan-3D genome research, identifying conserved and specific TAD boundaries across different species or individuals is computationally challenging. Here, we present Tcbf, a rapid and powerful Python/R tool that integrates gene synteny blocks and homologous sequences to automatically detect conserved and specific TAD boundaries among multiple species, which can efficiently analyze huge genome datasets, greatly reduce the computational burden and enable pan-3D genome research.",
                        "date": "2023-09-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "He X."
                            },
                            {
                                "name": "Huang X."
                            },
                            {
                                "name": "Long Y."
                            },
                            {
                                "name": "Liu Z."
                            },
                            {
                                "name": "Chang X."
                            },
                            {
                                "name": "Zhang X."
                            },
                            {
                                "name": "Wang M."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Maojun Wang",
                    "email": "mjwang@mail.hzau.edu.cn",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-4791-3742",
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        {
            "name": "FunTaxIS-lite",
            "description": "A simple and light solution to investigate protein functions in all living organisms.",
            "homepage": "https://www.medcomp.medicina.unipd.it/funtaxis-lite",
            "biotoolsID": "funtaxis_lite",
            "biotoolsCURIE": "biotools:funtaxis_lite",
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3695",
                            "term": "Filtering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_2421",
                            "term": "Database search"
                        }
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                    "input": [],
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                    "note": null,
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            "toolType": [
                "Web application",
                "Command-line tool"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0637",
                    "term": "Taxonomy"
                },
                {
                    "uri": "http://edamontology.org/topic_0089",
                    "term": "Ontology and terminology"
                },
                {
                    "uri": "http://edamontology.org/topic_0621",
                    "term": "Model organisms"
                }
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                "Mac",
                "Linux",
                "Windows"
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                "Python",
                "Shell"
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            "link": [
                {
                    "url": "https://github.com/MedCompUnipd/FunTaxIS-lite",
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                        "Repository"
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            "publication": [
                {
                    "doi": "10.1093/BIOINFORMATICS/BTAD549",
                    "pmid": "37672040",
                    "pmcid": "PMC10500080",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "FunTaxIS-lite: a simple and light solution to investigate protein functions in all living organisms",
                        "abstract": "Motivation: Defining the full domain of protein functions belonging to an organism is a complex challenge that is due to the huge heterogeneity of the taxonomy, where single or small groups of species can bear unique functional characteristics. FunTaxIS-lite provides a solution to this challenge by determining taxon-based constraints on Gene Ontology (GO) terms, which specify the functions that an organism can or cannot perform. The tool employs a set of rules to generate and spread the constraints across both the taxon hierarchy and the GO graph. Results: The taxon-based constraints produced by FunTaxIS-lite extend those provided by the Gene Ontology Consortium by an average of 300%. The implementation of these rules significantly reduces errors in function predictions made by automatic algorithms and can assist in correcting inconsistent protein annotations in databases.",
                        "date": "2023-09-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Bianca F."
                            },
                            {
                                "name": "Ispano E."
                            },
                            {
                                "name": "Gazzola E."
                            },
                            {
                                "name": "Lavezzo E."
                            },
                            {
                                "name": "Fontana P."
                            },
                            {
                                "name": "Toppo S."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
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                {
                    "name": "Stefano Toppo",
                    "email": "stefano.toppo@unipd.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-0246-3119",
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        {
            "name": "BiocMAP",
            "description": "Bioconductor-friendly, GPU-accelerated pipeline for bisulfite-sequencing data.",
            "homepage": "http://research.libd.org/BiocMAP/",
            "biotoolsID": "biocmap",
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                    "operation": [
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                            "uri": "http://edamontology.org/operation_3186",
                            "term": "Bisulfite mapping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3198",
                            "term": "Read mapping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3919",
                            "term": "Methylation calling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3809",
                            "term": "DMR identification"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
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                "Workflow"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3674",
                    "term": "Methylated DNA immunoprecipitation"
                },
                {
                    "uri": "http://edamontology.org/topic_0769",
                    "term": "Workflows"
                },
                {
                    "uri": "http://edamontology.org/topic_3295",
                    "term": "Epigenetics"
                },
                {
                    "uri": "http://edamontology.org/topic_0654",
                    "term": "DNA"
                },
                {
                    "uri": "http://edamontology.org/topic_3419",
                    "term": "Psychiatry"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
            "language": [
                "R",
                "Python"
            ],
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            "link": [
                {
                    "url": "https://github.com/LieberInstitute/BiocMAP",
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                        "Repository"
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                    "note": null
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            ],
            "download": [],
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            "publication": [
                {
                    "doi": "10.1186/S12859-023-05461-3",
                    "pmid": "37704947",
                    "pmcid": "PMC10498615",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BiocMAP: a Bioconductor-friendly, GPU-accelerated pipeline for bisulfite-sequencing data",
                        "abstract": "Background: Bisulfite sequencing is a powerful tool for profiling genomic methylation, an epigenetic modification critical in the understanding of cancer, psychiatric disorders, and many other conditions. Raw data generated by whole genome bisulfite sequencing (WGBS) requires several computational steps before it is ready for statistical analysis, and particular care is required to process data in a timely and memory-efficient manner. Alignment to a reference genome is one of the most computationally demanding steps in a WGBS workflow, taking several hours or even days with commonly used WGBS-specific alignment software. This naturally motivates the creation of computational workflows that can utilize GPU-based alignment software to greatly speed up the bottleneck step. In addition, WGBS produces raw data that is large and often unwieldy; a lack of memory-efficient representation of data by existing pipelines renders WGBS impractical or impossible to many researchers. Results: We present BiocMAP, a Bioconductor-friendly methylation analysis pipeline consisting of two modules, to address the above concerns. The first module performs computationally-intensive read alignment using Arioc, a GPU-accelerated short-read aligner. Since GPUs are not always available on the same computing environments where traditional CPU-based analyses are convenient, the second module may be run in a GPU-free environment. This module extracts and merges DNA methylation proportions—the fractions of methylated cytosines across all cells in a sample at a given genomic site. Bioconductor-based output objects in R utilize an on-disk data representation to drastically reduce required main memory and make WGBS projects computationally feasible to more researchers. Conclusions: BiocMAP is implemented using Nextflow and available at http://research.libd.org/BiocMAP/ . To enable reproducible analysis across a variety of typical computing environments, BiocMAP can be containerized with Docker or Singularity, and executed locally or with the SLURM or SGE scheduling engines. By providing Bioconductor objects, BiocMAP’s output can be integrated with powerful analytical open source software for analyzing methylation data.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Eagles N.J."
                            },
                            {
                                "name": "Wilton R."
                            },
                            {
                                "name": "Jaffe A.E."
                            },
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                            "term": "Protein modelling"
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                    "uri": "http://edamontology.org/topic_0078",
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                    "metadata": {
                        "title": "pLM-BLAST: distant homology detection based on direct comparison of sequence representations from protein language models",
                        "abstract": "Motivation: The detection of homology through sequence comparison is a typical first step in the study of protein function and evolution. In this work, we explore the applicability of protein language models to this task. Results: We introduce pLM-BLAST, a tool inspired by BLAST, that detects distant homology by comparing single-sequence representations (embeddings) derived from a protein language model, ProtT5. Our benchmarks reveal that pLM-BLAST maintains a level of accuracy on par with HHsearch for both highly similar sequences (with >50% identity) and markedly divergent sequences (with <30% identity), while being significantly faster. Additionally, pLM-BLAST stands out among other embedding-based tools due to its ability to compute local alignments. We show that these local alignments, produced by pLM-BLAST, often connect highly divergent proteins, thereby highlighting its potential to uncover previously undiscovered homologous relationships and improve protein annotation.",
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                            {
                                "name": "Kaminski K."
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                            {
                                "name": "Ludwiczak J."
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                            {
                                "name": "Pawlicki K."
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                            {
                                "name": "Alva V."
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                            {
                                "name": "Dunin-Horkawicz S."
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                    "metadata": {
                        "title": "ePlatypus: an ecosystem for computational analysis of immunogenomics data",
                        "abstract": "Motivation: The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Results: Here, we present the ePlatypus computational immunology ecosystem for immunogenomics data analysis, with a focus on adaptive immune repertoires and single-cell sequencing. ePlatypus is an open-source web-based platform and provides programming tutorials and an integrative database that helps elucidate signatures of B and T cell clonal selection. Furthermore, the ecosystem links novel and established bioinformatics pipelines relevant for single-cell immune repertoires and other aspects of computational immunology such as predicting ligand–receptor interactions, structural modeling, simulations, machine learning, graph theory, pseudotime, spatial transcriptomics, and phylogenetics. The ePlatypus ecosystem helps extract deeper insight in computational immunology and immunogenomics and promote open science.",
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                            {
                                "name": "Agrafiotis A."
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                            {
                                "name": "Kreiner V."
                            },
                            {
                                "name": "Kuhn R."
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                            {
                                "name": "Shlesinger D."
                            },
                            {
                                "name": "Manero-Carranza M."
                            },
                            {
                                "name": "Khodaverdi K."
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                            {
                                "name": "Kladis E."
                            },
                            {
                                "name": "Perea A.D."
                            },
                            {
                                "name": "Maassen-Veeters D."
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                            {
                                "name": "Glanzer W."
                            },
                            {
                                "name": "Massery S."
                            },
                            {
                                "name": "Guerci L."
                            },
                            {
                                "name": "Hong K.-L."
                            },
                            {
                                "name": "Han J."
                            },
                            {
                                "name": "Stiklioraitis K."
                            },
                            {
                                "name": "D'Arcy V.M."
                            },
                            {
                                "name": "Dizerens R."
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                            {
                                "name": "Kilchenmann S."
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                            {
                                "name": "Stalder L."
                            },
                            {
                                "name": "Nissen L."
                            },
                            {
                                "name": "Vogelsanger B."
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                            {
                                "name": "Anzbock S."
                            },
                            {
                                "name": "Laslo D."
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                            {
                                "name": "Bakker S."
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                            {
                                "name": "Kondorosy M."
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                            {
                                "name": "Venerito M."
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                            {
                                "name": "Garcia A.S."
                            },
                            {
                                "name": "Feller I."
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                            {
                                "name": "Oxenius A."
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                            {
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                            "term": "Expression correlation analysis"
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                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
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                    "term": "Genotype and phenotype"
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                    "uri": "http://edamontology.org/topic_0749",
                    "term": "Transcription factors and regulatory sites"
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                    "metadata": {
                        "title": "MuDCoD: Multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing",
                        "abstract": "Motivation: With the wide availability of single-cell RNA-seq (scRNA-seq) technology, population-scale scRNA-seq datasets across multiple individuals and time points are emerging. While the initial investigations of these datasets tend to focus on standard analysis of clustering and differential expression, leveraging the power of scRNA-seq data at the personalized dynamic gene co-expression network level has the potential to unlock subject and/or time-specific network-level variation, which is critical for understanding phenotypic differences. Community detection from co-expression networks of multiple time points or conditions has been well-studied; however, none of the existing settings included networks from multiple subjects and multiple time points simultaneously. To address this, we develop Multi-subject Dynamic Community Detection (MuDCoD) for multi-subject community detection in personalized dynamic gene networks from scRNA-seq. MuDCoD builds on the spectral clustering framework and promotes information sharing among the networks of the subjects as well as networks at different time points. It clusters genes in the personalized dynamic gene networks and reveals gene communities that are variable or shared not only across time but also among subjects. Results: Evaluation and benchmarking of MuDCoD against existing approaches reveal that MuDCoD effectively leverages apparent shared signals among networks of the subjects at individual time points, and performs robustly when there is no or little information sharing among the networks. Applications to population-scale scRNA-seq datasets of human-induced pluripotent stem cells during dopaminergic neuron differentiation and CD4+ T cell activation indicate that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid in the exploration of subject-specific biological processes that vary across time. Availability and implementation: MuDCoD is publicly available at https://github.com/bo1929/MuDCoD as a Python package. Implementation includes simulation and real-data experiments together with extensive documentation.",
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                        "authors": [
                            {
                                "name": "Sapci A.O.B."
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                            {
                                "name": "Lu S."
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                            {
                                "name": "Yan S."
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                            {
                                "name": "Ay F."
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                            {
                                "name": "Tastan O."
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                            {
                                "name": "Keles S."
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                        ],
                        "journal": "Bioinformatics"
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            "credit": [
                {
                    "name": "Oznur Tastan",
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                    "name": "Sündüz Keleş",
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        {
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            "description": "Compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny.",
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                {
                    "operation": [
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                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3928",
                            "term": "Pathway analysis"
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                            "uri": "http://edamontology.org/operation_2489",
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                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
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                {
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                    "term": "Cheminformatics"
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                    "metadata": {
                        "title": "MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny",
                        "abstract": "Background: Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. Results: To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein–protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. Conclusions: MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN .",
                        "date": "2023-12-01T00:00:00Z",
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                        "authors": [
                            {
                                "name": "Hosseini-Gerami L."
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                            {
                                "name": "Hernansaiz Ballesteros R."
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                            {
                                "name": "Liu A."
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                            {
                                "name": "Broughton H."
                            },
                            {
                                "name": "Collier D.A."
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                            {
                                "name": "Bender A."
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                        ],
                        "journal": "BMC Bioinformatics"
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                    "name": "Layla Hosseini-Gerami",
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        {
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            "description": "A comprehensive repository of full-length isoforms across human cancers and tissues.",
            "homepage": "http://www.FLIBase.org",
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                            "term": "Alternative splicing prediction"
                        },
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                            "uri": "http://edamontology.org/operation_3800",
                            "term": "RNA-Seq quantification"
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                    "term": "Oncology"
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                    "doi": "10.1093/NAR/GKAD745",
                    "pmid": "37697439",
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                        "title": "FLIBase: a comprehensive repository of full-length isoforms across human cancers and tissues",
                        "abstract": "Regulatory processes at the RNA transcript le v el pla y a crucial role in generating transcriptome diversity and proteome composition in human cells, impacting both ph y siological and pathological states. This study introduces FLIBase ( www.FLIBase.org ), a specialized database that focuses on annotating full-length isoforms using long-read sequencing techniques. We collected and integrated long-read (351 samples) and short-read (12 469 samples) RNA sequencing data from diverse normal and cancerous human tissues and cells. The current version of FLIBase comprises a total of 983 789 full-length spliced isoforms, identified through long-read sequences and verified using short-read e x on-e x on splice junctions. Of these, 188 248 isoforms have been annotated, while 795 541 isoforms remain unannotated. By overcoming the limitations of short-read RNA sequencing methods, FLIB ase pro vides an accurate and comprehensiv e representation of full-length transcripts. T hese comprehensiv e annotations empo w er researchers to undertak e v arious do wnstream analy ses and in v estigations. Importantly, FLIB ase e xhibits a significant advantage in identifying a substantial number of previously unannotated isoforms and tumor-specific RNA transcripts. These tumor-specific RNA transcripts ha v e the potential to serv e as a source of immunogenic recurrent neoantigens. T his remarkable disco v ery holds tremendous promise f or adv ancing the de v elopment of tailored RNA-based diagnostic and therapeutic strategies f or v arious types of human cancer.",
                        "date": "2024-01-05T00:00:00Z",
                        "citationCount": 2,
                        "authors": [
                            {
                                "name": "Shi Q."
                            },
                            {
                                "name": "Li X."
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                            {
                                "name": "Liu Y."
                            },
                            {
                                "name": "Chen Z."
                            },
                            {
                                "name": "He X."
                            }
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                        "journal": "Nucleic Acids Research"
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            "credit": [
                {
                    "name": "Xianghuo He",
                    "email": "xhhe@fudan.edu.cn",
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