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        {
            "name": "FLARE",
            "description": "FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I.",
            "homepage": "https://github.com/YeoLab/FLARE",
            "biotoolsID": "flare_rna",
            "biotoolsCURIE": "biotools:flare_rna",
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3096",
                            "term": "Editing"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3432",
                            "term": "Clustering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3902",
                            "term": "RNA binding site prediction"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
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            ],
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                "Workflow"
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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_0769",
                    "term": "Workflows"
                },
                {
                    "uri": "http://edamontology.org/topic_3534",
                    "term": "Protein binding sites"
                },
                {
                    "uri": "http://edamontology.org/topic_3794",
                    "term": "RNA immunoprecipitation"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Python"
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            "publication": [
                {
                    "doi": "10.1186/S12859-023-05452-4",
                    "pmid": "37784060",
                    "pmcid": "PMC10544219",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "FLARE: a fast and flexible workflow for identifying RNA editing foci",
                        "abstract": "Background: Fusion of RNA-binding proteins (RBPs) to RNA base-editing enzymes (such as APOBEC1 or ADAR) has emerged as a powerful tool for the discovery of RBP binding sites. However, current methods that analyze sequencing data from RNA-base editing experiments are vulnerable to false positives due to off-target editing, genetic variation and sequencing errors. Results: We present FLagging Areas of RNA-editing Enrichment (FLARE), a Snakemake-based pipeline that builds on the outputs of the SAILOR edit site discovery tool to identify regions statistically enriched for RNA editing. FLARE can be configured to analyze any type of RNA editing, including C to U and A to I. We applied FLARE to C-to-U editing data from a RBFOX2-APOBEC1 STAMP experiment, to show that our approach attains high specificity for detecting RBFOX2 binding sites. We also applied FLARE to detect regions of exogenously introduced as well as endogenous A-to-I editing. Conclusions: FLARE is a fast and flexible workflow that identifies significantly edited regions from RNA-seq data. The FLARE codebase is available at https://github.com/YeoLab/FLARE .",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Kofman E."
                            },
                            {
                                "name": "Yee B."
                            },
                            {
                                "name": "Medina-Munoz H.C."
                            },
                            {
                                "name": "Yeo G.W."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Gene W. Yeo",
                    "email": "geneyeo@ucsd.edu",
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                    "name": "Eric Kofman",
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        {
            "name": "LZerD",
            "description": "Web server for protein-protein docking prediction using the LZerD algorithm.",
            "homepage": "https://lzerd.kiharalab.org",
            "biotoolsID": "lzerd_web",
            "biotoolsCURIE": "biotools:lzerd_web",
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            "function": [
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                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3899",
                            "term": "Protein-protein docking"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0474",
                            "term": "Protein structure prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0570",
                            "term": "Structure visualisation"
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                "Web application"
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                    "uri": "http://edamontology.org/topic_2275",
                    "term": "Molecular modelling"
                },
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
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                    "uri": "http://edamontology.org/topic_0080",
                    "term": "Sequence analysis"
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            "publication": [
                {
                    "doi": "10.1007/978-1-0716-3327-4_28",
                    "pmid": "37450159",
                    "pmcid": "PMC10561630",
                    "type": [],
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                    "metadata": {
                        "title": "Pairwise and Multi-chain Protein Docking Enhanced Using LZerD Web Server",
                        "abstract": "Interactions of proteins with other macromolecules have important structural and functional roles in the basic processes of living cells. To understand and elucidate the mechanisms of interactions, it is important to know the 3D structures of the complexes. Proteomes contain numerous protein-protein complexes, for which experimentally determined structures often do not exist. Computational techniques can be a practical alternative to obtain useful complex structure models. Here, we present a web server that provides access to the LZerD and Multi-LZerD protein docking tools, which can perform both pairwise and multi-chain docking. The web server is user-friendly, with options to visualize the distribution and structures of binding poses of top-scoring models. The LZerD web server is available at https://lzerd.kiharalab.org. This chapter dictates the algorithm and step-by-step procedure to model the monomeric structures with AttentiveDist, and also provides the detail of pairwise LZerD docking, and multi-LZerD. This also provided case studies for each of the three modules.",
                        "date": "2023-01-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Harini K."
                            },
                            {
                                "name": "Christoffer C."
                            },
                            {
                                "name": "Gromiha M.M."
                            },
                            {
                                "name": "Kihara D."
                            }
                        ],
                        "journal": "Methods in Molecular Biology"
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            "credit": [
                {
                    "name": "Daisuke Kihara",
                    "email": "dkihara@purdue.edu",
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                {
                    "name": "Kannan Harini",
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        {
            "name": "AMEND",
            "description": "AMEND (Active Module identification using Experimental data and Network Diffusion) is an algorithm designed to find a subset of connected nodes in a molecular interaction network that have large experimental values. It makes use of random walk with restart (RWR) to create node weights, and a heuristic approach for solving the Maximum-weight Connected Subgraph problem using these weights. The resulting subnetwork is then scored based on average experimental values and connectivity, and it is used as input into RWR for the next iteration. This process is performed iteratively until an optimal subnetwork (i.e., module) is found.",
            "homepage": "https://github.com/samboyd0/AMEND",
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            "biotoolsCURIE": "biotools:amend",
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                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3927",
                            "term": "Network analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3223",
                            "term": "Differential gene expression profiling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3501",
                            "term": "Enrichment analysis"
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            ],
            "toolType": [
                "Library"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0128",
                    "term": "Protein interactions"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_3518",
                    "term": "Microarray experiment"
                }
            ],
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                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "R"
            ],
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            "publication": [
                {
                    "doi": "10.1186/S12859-023-05376-Z",
                    "pmid": "37415126",
                    "pmcid": "PMC10324253",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "AMEND: active module identification using experimental data and network diffusion",
                        "abstract": "Background: Molecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein–protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions. Results: To address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets. Conclusion: The AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND .",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Boyd S.S."
                            },
                            {
                                "name": "Slawson C."
                            },
                            {
                                "name": "Thompson J.A."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Jeffrey A. Thompson",
                    "email": "jthompson21@kumc.edu",
                    "url": null,
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                {
                    "name": "Samuel S Boyd",
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                {
                    "name": "Chad Slawson",
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        {
            "name": "PyGellermann",
            "description": "Python tool to generate pseudorandom series for human and non-human animal behavioural experiments. It includes both a graphical user interface (GUI) as well as a simple Python API.",
            "homepage": "https://github.com/YannickJadoul/PyGellermann",
            "biotoolsID": "pygellermann",
            "biotoolsCURIE": "biotools:pygellermann",
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0230",
                            "term": "Sequence generation"
                        }
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                    "note": null,
                    "cmd": null
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            "toolType": [
                "Library",
                "Desktop application",
                "Command-line tool"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3500",
                    "term": "Zoology"
                },
                {
                    "uri": "http://edamontology.org/topic_3679",
                    "term": "Animal study"
                },
                {
                    "uri": "http://edamontology.org/topic_3361",
                    "term": "Laboratory techniques"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
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            "language": [
                "Python"
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            "license": "GPL-3.0",
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            "maturity": null,
            "cost": "Free of charge",
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            "publication": [
                {
                    "doi": "10.1186/S13104-023-06396-X",
                    "pmid": "37403146",
                    "pmcid": "PMC10320995",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments",
                        "abstract": "Objective: Researchers in animal cognition, psychophysics, and experimental psychology need to randomise the presentation order of trials in experimental sessions. In many paradigms, for each trial, one of two responses can be correct, and the trials need to be ordered such that the participant’s responses are a fair assessment of their performance. Specifically, in some cases, especially for low numbers of trials, randomised trial orders need to be excluded if they contain simple patterns which a participant could accidentally match and so succeed at the task without learning. Results: We present and distribute a simple Python software package and tool to produce pseudorandom sequences following the Gellermann series. This series has been proposed to pre-empt simple heuristics and avoid inflated performance rates via false positive responses. Our tool allows users to choose the sequence length and outputs a.csv file with newly and randomly generated sequences. This allows behavioural researchers to produce, in a few seconds, a pseudorandom sequence for their specific experiment. PyGellermann is available at https://github.com/YannickJadoul/PyGellermann .",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Jadoul Y."
                            },
                            {
                                "name": "Duengen D."
                            },
                            {
                                "name": "Ravignani A."
                            }
                        ],
                        "journal": "BMC Research Notes"
                    }
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            "credit": [
                {
                    "name": "Yannick Jadoul",
                    "email": "Yannick.Jadoul@mpi.nl",
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                {
                    "name": "Andrea Ravignani",
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        },
        {
            "name": "BamQuery",
            "description": "Proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens.",
            "homepage": "http://bamquery.iric.ca/",
            "biotoolsID": "bamquery",
            "biotoolsCURIE": "biotools:bamquery",
            "version": [],
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            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0252",
                            "term": "Peptide immunogenicity prediction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3435",
                            "term": "Standardisation and normalisation"
                        },
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                            "uri": "http://edamontology.org/operation_3223",
                            "term": "Differential gene expression profiling"
                        }
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            "topic": [
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                    "uri": "http://edamontology.org/topic_3922",
                    "term": "Proteogenomics"
                },
                {
                    "uri": "http://edamontology.org/topic_2830",
                    "term": "Immunoproteins and antigens"
                },
                {
                    "uri": "http://edamontology.org/topic_3930",
                    "term": "Immunogenetics"
                },
                {
                    "uri": "http://edamontology.org/topic_0154",
                    "term": "Small molecules"
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                    "term": "RNA-Seq"
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            "link": [
                {
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            "publication": [
                {
                    "doi": "10.1186/S13059-023-03029-1",
                    "pmid": "37582761",
                    "pmcid": "PMC10426134",
                    "type": [],
                    "version": null,
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                    "metadata": {
                        "title": "BamQuery: a proteogenomic tool to explore the immunopeptidome and prioritize actionable tumor antigens",
                        "abstract": "MHC-I-associated peptides deriving from non-coding genomic regions and mutations can generate tumor-specific antigens, including neoantigens. Quantifying tumor-specific antigens’ RNA expression in malignant and benign tissues is critical for discriminating actionable targets. We present BamQuery, a tool attributing an exhaustive RNA expression to MHC-I-associated peptides of any origin from bulk and single-cell RNA-sequencing data. We show that many cryptic and mutated tumor-specific antigens can derive from multiple discrete genomic regions, abundantly expressed in normal tissues. BamQuery can also be used to predict MHC-I-associated peptides immunogenicity and identify actionable tumor-specific antigens de novo.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 4,
                        "authors": [
                            {
                                "name": "Cuevas M.V.R."
                            },
                            {
                                "name": "Hardy M.-P."
                            },
                            {
                                "name": "Larouche J.-D."
                            },
                            {
                                "name": "Apavaloaei A."
                            },
                            {
                                "name": "Kina E."
                            },
                            {
                                "name": "Vincent K."
                            },
                            {
                                "name": "Gendron P."
                            },
                            {
                                "name": "Laverdure J.-P."
                            },
                            {
                                "name": "Durette C."
                            },
                            {
                                "name": "Thibault P."
                            },
                            {
                                "name": "Lemieux S."
                            },
                            {
                                "name": "Perreault C."
                            },
                            {
                                "name": "Ehx G."
                            }
                        ],
                        "journal": "Genome Biology"
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                }
            ],
            "credit": [
                {
                    "name": "Grégory Ehx",
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        },
        {
            "name": "SeQual-Stream",
            "description": "Big Data tool to perform quality control operations of NGS datasets in a scalable way, supporting single-end and paired-end reads in FASTQ/FASTA formats.",
            "homepage": "https://github.com/UDC-GAC/SeQual-Stream",
            "biotoolsID": "sequal-stream",
            "biotoolsCURIE": "biotools:sequal-stream",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3218",
                            "term": "Sequencing quality control"
                        },
                        {
                            "uri": "http://edamontology.org/operation_0282",
                            "term": "Genetic mapping"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3432",
                            "term": "Clustering"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3168",
                    "term": "Sequencing"
                },
                {
                    "uri": "http://edamontology.org/topic_0102",
                    "term": "Mapping"
                },
                {
                    "uri": "http://edamontology.org/topic_0654",
                    "term": "DNA"
                },
                {
                    "uri": "http://edamontology.org/topic_3297",
                    "term": "Biotechnology"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [
                "Java"
            ],
            "license": "AGPL-3.0",
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1186/S12859-023-05530-7",
                    "pmid": "37891497",
                    "pmcid": "PMC10612204",
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "SeQual-Stream: approaching stream processing to quality control of NGS datasets",
                        "abstract": "Background: Quality control of DNA sequences is an important data preprocessing step in many genomic analyses. However, all existing parallel tools for this purpose are based on a batch processing model, needing to have the complete genetic dataset before processing can even begin. This limitation clearly hinders quality control performance in those scenarios where the dataset must be downloaded from a remote repository and/or copied to a distributed file system for its parallel processing. Results: In this paper we present SeQual-Stream, a streaming tool that allows performing multiple quality control operations on genomic datasets in a fast, distributed and scalable way. To do so, our approach relies on the Apache Spark framework and the Hadoop Distributed File System (HDFS) to fully exploit the stream paradigm and accelerate the preprocessing of large datasets as they are being downloaded and/or copied to HDFS. The experimental results have shown significant improvements in the execution times of SeQual-Stream when compared to a batch processing tool with similar quality control features, providing a maximum speedup of 2.7 × when processing a dataset with more than 250 million DNA sequences, while also demonstrating good scalability features. Conclusion: Our solution provides a more scalable and higher performance way to carry out quality control of large genomic datasets by taking advantage of stream processing features. The tool is distributed as free open-source software released under the GNU AGPLv3 license and is publicly available to download at https://github.com/UDC-GAC/SeQual-Stream .",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Castellanos-Rodriguez O."
                            },
                            {
                                "name": "Exposito R.R."
                            },
                            {
                                "name": "Tourino J."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Óscar Castellanos-Rodríguez",
                    "email": "oscar.castellanos@udc.es",
                    "url": null,
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                },
                {
                    "name": "Roberto R. Expósito",
                    "email": null,
                    "url": null,
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                    "gridid": null,
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                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Juan Touriño",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                    "typeEntity": "Person",
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                }
            ],
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            "owner": "Pub2Tools",
            "additionDate": "2024-03-27T14:57:37.459010Z",
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        },
        {
            "name": "LAMPPrimerBank",
            "description": "Manually curated database of experimentally validated loop-mediated isothermal amplification primers for detection of respiratory pathogens.",
            "homepage": "https://lampprimerbank.mathematik.uni-marburg.de",
            "biotoolsID": "lampprimerbank",
            "biotoolsCURIE": "biotools:lampprimerbank",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0308",
                            "term": "PCR primer design"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3965",
                            "term": "Amplification detection"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3436",
                            "term": "Aggregation"
                        }
                    ],
                    "input": [],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Database portal"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0632",
                    "term": "Probes and primers"
                },
                {
                    "uri": "http://edamontology.org/topic_3322",
                    "term": "Respiratory medicine"
                },
                {
                    "uri": "http://edamontology.org/topic_0219",
                    "term": "Data submission, annotation and curation"
                },
                {
                    "uri": "http://edamontology.org/topic_3068",
                    "term": "Literature and language"
                },
                {
                    "uri": "http://edamontology.org/topic_3577",
                    "term": "Personalised medicine"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux",
                "Windows"
            ],
            "language": [],
            "license": null,
            "collectionID": [
                "COVID-19"
            ],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1007/S15010-023-02100-0",
                    "pmid": "37828369",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "LAMPPrimerBank, a manually curated database of experimentally validated loop-mediated isothermal amplification primers for detection of respiratory pathogens",
                        "abstract": "Purpose and methods: The emergence of coronavirus disease 2019 (COVID-19) has once again affirmed the significant threat of respiratory infections to global public health and the utmost importance of prompt diagnosis in managing and mitigating any pandemic. The nucleic acid amplification test (NAAT) is the primary detection method for most pathogens. Loop‐mediated isothermal amplification (LAMP) is a rapid, simple, sensitive, and specific epitome of isothermal NAAT performed using a set of four to six primers. Primer design is a fundamental step in LAMP assays, with several complexities and experimental screening requirements. To address this challenge, an online database is presented here. Its workflow comprises three steps: literature aggregation, data curation, and database and website implementation. Results: LAMPPrimerBank (https://lampprimerbank.mathematik.uni-marburg.de) is a manually curated database dedicated to experimentally validated LAMP primers, their peculiarities of assays, and accompanying literature, with a primary emphasis on respiratory pathogens. LAMPPrimerBank, with its user-friendly web interface and an open application programming interface, enables the accelerated and facile exploration, comparison, and exportation of LAMP primer sequences and their respective information from the massively scattered literature. LAMPPrimerBank currently comprises LAMP primers for diagnosing viral, bacterial, and fungal respiratory pathogens. Additionally, to address the challenge of false-positive results generated by nonspecific amplifications, LAMPPrimerBank computationally predicted and visualized the sizes of LAMP products for recorded primer sets in the database. Conclusion: LAMPPrimerBank, as a pioneering database in the rapidly expanding field of isothermal NAAT, endeavors to confront the two challenges of the LAMP: primer design and discrimination of false-positive results.",
                        "date": "2023-12-01T00:00:00Z",
                        "citationCount": 0,
                        "authors": [
                            {
                                "name": "Arabi-Jeshvaghani F."
                            },
                            {
                                "name": "Javadi-Zarnaghi F."
                            },
                            {
                                "name": "Lochel H.F."
                            },
                            {
                                "name": "Martin R."
                            },
                            {
                                "name": "Heider D."
                            }
                        ],
                        "journal": "Infection"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Fatemeh Javadi-Zarnaghi",
                    "email": "fa.javadi@sci.ui.ac.ir",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                },
                {
                    "name": "Fatemeh Arabi-Jeshvaghani",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [],
                    "note": null
                }
            ],
            "community": null,
            "owner": "Pub2Tools",
            "additionDate": "2024-03-27T12:55:23.440425Z",
            "lastUpdate": "2024-03-27T12:55:23.443140Z",
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                "authors": []
            },
            "validated": 0,
            "homepage_status": 0,
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            "confidence_flag": "tool"
        },
        {
            "name": "physher",
            "description": "A multi-algorithmic framework for phylogenetic inference",
            "homepage": "https://github.com/4ment/physher",
            "biotoolsID": "physher",
            "biotoolsCURIE": "biotools:physher",
            "version": [
                "2.0.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0547",
                            "term": "Phylogenetic inference (maximum likelihood and Bayesian methods)"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1383",
                                "term": "Nucleic acid sequence alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0872",
                                "term": "Phylogenetic tree"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1910",
                                    "term": "newick"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3293",
                    "term": "Phylogenetics"
                }
            ],
            "operatingSystem": [
                "Mac",
                "Linux"
            ],
            "language": [
                "C",
                "C++"
            ],
            "license": "GPL-2.0",
            "collectionID": [],
            "maturity": null,
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1186/s12862-014-0163-6",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Novel non-parametric models to estimate evolutionary rates and divergence times from heterochronous sequence data",
                        "abstract": "Background: Early methods for estimating divergence times from gene sequence data relied on the assumption of a molecular clock. More sophisticated methods were created to model rate variation and used auto-correlation of rates, local clocks, or the so called \"uncorrelated relaxed clock\" where substitution rates are assumed to be drawn from a parametric distribution. In the case of Bayesian inference methods the impact of the prior on branching times is not clearly understood, and if the amount of data is limited the posterior could be strongly influenced by the prior. Results: We develop a maximum likelihood method - Physher - that uses local or discrete clocks to estimate evolutionary rates and divergence times from heterochronous sequence data. Using two empirical data sets we show that our discrete clock estimates are similar to those obtained by other methods, and that Physher outperformed some methods in the estimation of the root age of an influenza virus data set. A simulation analysis suggests that Physher can outperform a Bayesian method when the real topology contains two long branches below the root node, even when evolution is strongly clock-like. Conclusions: These results suggest it is advisable to use a variety of methods to estimate evolutionary rates and divergence times from heterochronous sequence data. Physher and the associated data sets used here are available online at. © 2014 Fourment and Holmes; licensee BioMed Central Ltd.",
                        "date": "2014-07-24T00:00:00Z",
                        "citationCount": 18,
                        "authors": [
                            {
                                "name": "Fourment M."
                            },
                            {
                                "name": "Holmes E.C."
                            }
                        ],
                        "journal": "BMC Evolutionary Biology"
                    }
                },
                {
                    "doi": "10.1093/sysbio/syz046",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "19 Dubious Ways to Compute the Marginal Likelihood of a Phylogenetic Tree Topology",
                        "abstract": "The marginal likelihood of a model is a key quantity for assessing the evidence provided by the data in support of a model. The marginal likelihood is the normalizing constant for the posterior density, obtained by integrating the product of the likelihood and the prior with respect to model parameters. Thus, the computational burden of computing the marginal likelihood scales with the dimension of the parameter space. In phylogenetics, where we work with tree topologies that are high-dimensional models, standard approaches to computing marginal likelihoods are very slow. Here, we study methods to quickly compute the marginal likelihood of a single fixed tree topology. We benchmark the speed and accuracy of 19 different methods to compute the marginal likelihood of phylogenetic topologies on a suite of real data sets under the JC69 model. These methods include several new ones that we develop explicitly to solve this problem, as well as existing algorithms that we apply to phylogenetic models for the first time. Altogether, our results show that the accuracy of these methods varies widely, and that accuracy does not necessarily correlate with computational burden. Our newly developed methods are orders of magnitude faster than standard approaches, and in some cases, their accuracy rivals the best established estimators.",
                        "date": "2020-03-01T00:00:00Z",
                        "citationCount": 28,
                        "authors": [
                            {
                                "name": "Fourment M."
                            },
                            {
                                "name": "Magee A.F."
                            },
                            {
                                "name": "Whidden C."
                            },
                            {
                                "name": "Bilge A."
                            },
                            {
                                "name": "Matsen F.A."
                            },
                            {
                                "name": "Minin V.N."
                            }
                        ],
                        "journal": "Systematic Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Mathieu Fourment",
                    "email": "mathieu.fourment@uts.edu.au",
                    "url": "https://github.com/4ment",
                    "orcidid": "https://orcid.org/0000-0001-8153-9822",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact",
                        "Developer",
                        "Maintainer",
                        "Support"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "4ment",
            "additionDate": "2024-03-26T23:53:30.900328Z",
            "lastUpdate": "2024-03-26T23:54:56.150640Z",
            "editPermission": {
                "type": "private",
                "authors": []
            },
            "validated": 0,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "BioMaS (Bioinformatic analysis of Metagenomic amplicons)",
            "description": "BioMaS is a friendly pipeline for Meta-barcoding HTS data analysis specifically designed for users without particular computing skills.",
            "homepage": "http://galaxy.cloud.ba.infn.it:8080/root",
            "biotoolsID": "biomas_bioinformatic_analysis_of_metagenomic_amplicons",
            "biotoolsCURIE": "biotools:biomas_bioinformatic_analysis_of_metagenomic_amplicons",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3460",
                            "term": "Taxonomic classification"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1383",
                                "term": "Nucleic acid sequence alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2573",
                                    "term": "SAM"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3028",
                                "term": "Taxonomy"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1910",
                                    "term": "newick"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0232",
                            "term": "Sequence merging"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3494",
                                "term": "DNA sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1930",
                                    "term": "FASTQ"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3494",
                                "term": "DNA sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1930",
                                    "term": "FASTQ"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                },
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0496",
                            "term": "Global alignment"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3494",
                                "term": "DNA sequence"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1930",
                                    "term": "FASTQ"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1383",
                                "term": "Nucleic acid sequence alignment"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2573",
                                    "term": "SAM"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3391",
                    "term": "Omics"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Mac",
                "Windows"
            ],
            "language": [
                "Python",
                "Bash"
            ],
            "license": "Not licensed",
            "collectionID": [],
            "maturity": "Emerging",
            "cost": "Free of charge (with restrictions)",
            "accessibility": "Open access (with restrictions)",
            "elixirPlatform": [
                "Tools"
            ],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [
                "Marine Metagenomics"
            ],
            "link": [],
            "download": [],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1186/s12859-015-0595-z",
                    "pmid": "26130132",
                    "pmcid": "PMC4486701",
                    "type": [
                        "Method"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "BioMaS: A modular pipeline for Bioinformatic analysis of Metagenomic AmpliconS",
                        "abstract": "Background: Substantial advances in microbiology, molecular evolution and biodiversity have been carried out in recent years thanks to Metagenomics, which allows to unveil the composition and functions of mixed microbial communities in any environmental niche. If the investigation is aimed only at the microbiome taxonomic structure, a target-based metagenomic approach, here also referred as Meta-barcoding, is generally applied. This approach commonly involves the selective amplification of a species-specific genetic marker (DNA meta-barcode) in the whole taxonomic range of interest and the exploration of its taxon-related variants through High-Throughput Sequencing (HTS) technologies. The accessibility to proper computational systems for the large-scale bioinformatic analysis of HTS data represents, currently, one of the major challenges in advanced Meta-barcoding projects. Results: BioMaS (Bioinformatic analysis of Metagenomic AmpliconS) is a new bioinformatic pipeline designed to support biomolecular researchers involved in taxonomic studies of environmental microbial communities by a completely automated workflow, comprehensive of all the fundamental steps, from raw sequence data upload and cleaning to final taxonomic identification, that are absolutely required in an appropriately designed Meta-barcoding HTS-based experiment. In its current version, BioMaS allows the analysis of both bacterial and fungal environments starting directly from the raw sequencing data from either Roche 454 or Illumina HTS platforms, following two alternative paths, respectively. BioMaS is implemented into a public web service available at https://recasgateway.ba.infn.it/ and is also available in Galaxy at http://galaxy.cloud.ba.infn.it:8080 (only for Illumina data). Conclusion: BioMaS is a friendly pipeline for Meta-barcoding HTS data analysis specifically designed for users without particular computing skills. A comparative benchmark, carried out by using a simulated dataset suitably designed to broadly represent the currently known bacterial and fungal world, showed that BioMaS outperforms QIIME and MOTHUR in terms of extent and accuracy of deep taxonomic sequence assignments.",
                        "date": "2015-12-12T00:00:00Z",
                        "citationCount": 45,
                        "authors": [
                            {
                                "name": "Fosso B."
                            },
                            {
                                "name": "Santamaria M."
                            },
                            {
                                "name": "Marzano M."
                            },
                            {
                                "name": "Alonso-Alemany D."
                            },
                            {
                                "name": "Valiente G."
                            },
                            {
                                "name": "Donvito G."
                            },
                            {
                                "name": "Monaco A."
                            },
                            {
                                "name": "Notarangelo P."
                            },
                            {
                                "name": "Pesole G."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Bruno Fosso",
                    "email": "b.fosso@ibiom.cnr.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-2324-086X",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
                    ],
                    "note": "Researcher at CNR-IBIOM"
                },
                {
                    "name": "Graziano Pesole",
                    "email": "graziano.pesole@uniba.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0003-3663-0859",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": "Full Professor of Molecular Biology at University of Bari"
                },
                {
                    "name": "Monica Santamaria",
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