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            "name": "SLiM",
            "description": "Evolutionary simulation framework that combines a powerful engine for population genetic simulations with the capability of modeling arbitrarily complex evolutionary scenarios.  Includes a graphical modeling environment.",
            "homepage": "https://messerlab.org/slim/",
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                            "term": "DNA substitution modelling"
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                            "term": "Ecological modelling"
                        }
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                    "input": [],
                    "output": [],
                    "note": "Run individual-based evolutionary simulations with explicit genetics",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Desktop application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0610",
                    "term": "Ecology"
                },
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
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                    "uri": "http://edamontology.org/topic_0199",
                    "term": "Genetic variation"
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                    "uri": "http://edamontology.org/topic_3299",
                    "term": "Evolutionary biology"
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            "link": [
                {
                    "url": "https://messerlab.org/slim/",
                    "type": [
                        "Software catalogue"
                    ],
                    "note": "SLiM home page in the Messer Lab website"
                },
                {
                    "url": "https://github.com/MesserLab/SLiM",
                    "type": [
                        "Repository"
                    ],
                    "note": "GitHub repository for SLiM"
                },
                {
                    "url": "https://groups.google.com/g/slim-discuss",
                    "type": [
                        "Discussion forum"
                    ],
                    "note": "Discussion forum for SLiM questions"
                },
                {
                    "url": "https://groups.google.com/g/slim-announce",
                    "type": [
                        "Mailing list"
                    ],
                    "note": "Announcements mailing list"
                }
            ],
            "download": [
                {
                    "url": "http://benhaller.com/slim/SLiM.zip",
                    "type": "Source code",
                    "note": "A source archive for the command-line `slim` tool only.  Complete source code is on GitHub, but most platforms have an installer anyway; see the manual, chapter 2, for installation instructions.",
                    "version": null
                },
                {
                    "url": "https://github.com/MesserLab/SLiM/releases/latest",
                    "type": "Downloads page",
                    "note": "The GitHub page for the current release version, to obtain full source code.",
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "http://benhaller.com/slim/SLiM_Manual.pdf",
                    "type": [
                        "User manual"
                    ],
                    "note": "The manual for SLiM itself"
                },
                {
                    "url": "http://benhaller.com/slim/Eidos_Manual.pdf",
                    "type": [
                        "User manual"
                    ],
                    "note": "The manual for Eidos, the scripting language used by SLiM"
                },
                {
                    "url": "http://benhaller.com/slim/SLiMEidosRefSheets.zip",
                    "type": [
                        "Quick start guide"
                    ],
                    "note": "Quick reference sheets for SLiM and Eidos"
                }
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            "publication": [
                {
                    "doi": "10.1093/molbev/msy228",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": "B.C. Haller, P.W. Messer. (2019). SLiM 3: Forward genetic simulations beyond the Wright–Fisher Model. Molecular Biology and Evolution 36(3), 632–637.",
                    "metadata": {
                        "title": "SLiM 3: Forward Genetic Simulations Beyond the Wright-Fisher Model",
                        "abstract": "© The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.With the desire to model population genetic processes under increasingly realistic scenarios, forward genetic simulations have become a critical part of the toolbox of modern evolutionary biology. The SLiM forward genetic simulation framework is one of the most powerful and widely used tools in this area. However, its foundation in the Wright-Fisher model has been found to pose an obstacle to implementing many types of models; it is difficult to adapt the Wright-Fisher model, with its many assumptions, to modeling ecologically realistic scenarios such as explicit space, overlapping generations, individual variation in reproduction, density-dependent population regulation, individual variation in dispersal or migration, local extinction and recolonization, mating between subpopulations, age structure, fitness-based survival and hard selection, emergent sex ratios, and so forth. In response to this need, we here introduce SLiM 3, which contains two key advancements aimed at abolishing these limitations. First, the new non-Wright-Fisher or \"nonWF\" model type provides a much more flexible foundation that allows the easy implementation of all of the above scenarios and many more. Second, SLiM 3 adds support for continuous space, including spatial interactions and spatial maps of environmental variables. We provide a conceptual overview of these new features, and present several example models to illustrate their use.",
                        "date": "2019-03-01T00:00:00Z",
                        "citationCount": 143,
                        "authors": [
                            {
                                "name": "Haller B.C."
                            },
                            {
                                "name": "Messer P.W."
                            }
                        ],
                        "journal": "Molecular Biology and Evolution"
                    }
                },
                {
                    "doi": "10.1093/molbev/msy237",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Usage"
                    ],
                    "version": null,
                    "note": "B.C. Haller, P.W. Messer. (2019). Evolutionary modeling in SLiM 3 for beginners. Molecular Biology and Evolution 36(5), 1101–1109.",
                    "metadata": {
                        "title": "Evolutionary Modeling in SLiM 3 for Beginners",
                        "abstract": "© 2019 The Author(s).The SLiM forward genetic simulation framework has proved to be a powerful and flexible tool for population genetic modeling. However, as a complex piece of software with many features that allow simulating a diverse assortment of evolutionary models, its initial learning curve can be difficult. Here we provide a step-by-step demonstration of how to build a simple evolutionary model in SLiM 3, to help new users get started. We will begin with a panmictic neutral model, and build up to a model of the evolution of a polygenic quantitative trait under selection for an environmental phenotypic optimum.",
                        "date": "2019-05-01T00:00:00Z",
                        "citationCount": 1,
                        "authors": [
                            {
                                "name": "Haller B.C."
                            },
                            {
                                "name": "Messer P.W."
                            }
                        ],
                        "journal": "Molecular Biology and Evolution"
                    }
                },
                {
                    "doi": "10.1111/1755-0998.12968",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Method"
                    ],
                    "version": null,
                    "note": "B.C. Haller, J. Galloway, J. Kelleher, P.W. Messer, P.L. Ralph. (2019). Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes. Molecular Ecology Resources 19(2), 552–566.",
                    "metadata": {
                        "title": "Tree-sequence recording in SLiM opens new horizons for forward-time simulation of whole genomes",
                        "abstract": "© 2018 John Wiley & Sons LtdThere is an increasing demand for evolutionary models to incorporate relatively realistic dynamics, ranging from selection at many genomic sites to complex demography, population structure, and ecological interactions. Such models can generally be implemented as individual-based forward simulations, but the large computational overhead of these models often makes simulation of whole chromosome sequences in large populations infeasible. This situation presents an important obstacle to the field that requires conceptual advances to overcome. The recently developed tree-sequence recording method (Kelleher, Thornton, Ashander, & Ralph, 2018), which stores the genealogical history of all genomes in the simulated population, could provide such an advance. This method has several benefits: (1) it allows neutral mutations to be omitted entirely from forward-time simulations and added later, thereby dramatically improving computational efficiency; (2) it allows neutral burn-in to be constructed extremely efficiently after the fact, using “recapitation”; (3) it allows direct examination and analysis of the genealogical trees along the genome; and (4) it provides a compact representation of a population's genealogy that can be analysed in Python using the msprime package. We have implemented the tree-sequence recording method in SLiM 3 (a free, open-source evolutionary simulation software package) and extended it to allow the recording of non-neutral mutations, greatly broadening the utility of this method. To demonstrate the versatility and performance of this approach, we showcase several practical applications that would have been beyond the reach of previously existing methods, opening up new horizons for the modelling and exploration of evolutionary processes.",
                        "date": "2019-03-01T00:00:00Z",
                        "citationCount": 33,
                        "authors": [
                            {
                                "name": "Haller B.C."
                            },
                            {
                                "name": "Galloway J."
                            },
                            {
                                "name": "Kelleher J."
                            },
                            {
                                "name": "Messer P.W."
                            },
                            {
                                "name": "Ralph P.L."
                            }
                        ],
                        "journal": "Molecular Ecology Resources"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Philipp Messer",
                    "email": "messer@cornell.edu",
                    "url": "https://messerlab.org",
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                    "name": "Benjamin C. Haller",
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                    "url": "http://benhaller.com",
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                                "term": "Sequence alignment"
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                    "term": "Sequence analysis"
                }
            ],
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                    "url": "https://galaxy.pasteur.fr/tool_runner?tool_id=toolshed.pasteur.fr/repos/fmareuil/mafft/rbc_mafft/7.273.1",
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            "download": [
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                    "url": "https://mafft.cbrc.jp/alignment/software/",
                    "type": "Downloads page",
                    "note": null,
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            ],
            "documentation": [
                {
                    "url": "https://mafft.cbrc.jp/alignment/software/tips0.html",
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            "publication": [
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                    "doi": "10.1007/978-1-62703-646-7_8",
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                    "metadata": {
                        "title": "MAFFT: Iterative refinement and additional methods",
                        "abstract": "This chapter outlines several methods implemented in the MAFFT package. MAFFT is a popular multiple sequence alignment (MSA) program with various options for the progressive method, the iterative refinement method and other methods. We first outline basic usage of MAFFT and then describe recent practical extensions, such as dot plot and adjustment of direction in DNA alignment. We also refer to MUSCLE, another high-performance MSA program. © 2014 Springer Science+Business Media, LLC.",
                        "date": "2014-01-01T00:00:00Z",
                        "citationCount": 232,
                        "authors": [
                            {
                                "name": "Katoh K."
                            },
                            {
                                "name": "Standley D.M."
                            }
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                        "journal": "Methods in Molecular Biology"
                    }
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                {
                    "doi": "10.7490/f1000research.1114334.1",
                    "pmid": null,
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                    "version": null,
                    "note": null,
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                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Parallelization of MAFFT for large-scale multiple sequence alignments",
                        "abstract": "© The Author(s) 2018. Published by Oxford University Press.Summary: We report an update for the MAFFT multiple sequence alignment program to enable parallel calculation of large numbers of sequences. The G-INS-1 option of MAFFT was recently reported to have higher accuracy than other methods for large data, but this method has been impractical for most large-scale analyses, due to the requirement of large computational resources. We introduce a scalable variant, G-large-INS-1, which has equivalent accuracy to G-INS-1 and is applicable to 50 000 or more sequences.",
                        "date": "2018-07-15T00:00:00Z",
                        "citationCount": 238,
                        "authors": [
                            {
                                "name": "Nakamura T."
                            },
                            {
                                "name": "Yamada K.D."
                            },
                            {
                                "name": "Tomii K."
                            },
                            {
                                "name": "Katoh K."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1093/bib/bbx108",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Other"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "MAFFT online service: Multiple sequence alignment, interactive sequence choice and visualization",
                        "abstract": "© 2017 The Author. Published by Oxford University Press.This article describes several features in the MAFFT online service for multiple sequence alignment (MSA). As a result of recent advances in sequencing technologies, huge numbers of biological sequences are available and the need for MSAs with large numbers of sequences is increasing. To extract biologically relevant information from such data, sophistication of algorithms is necessary but not sufficient. Intuitive and interactive tools for experimental biologists to semiautomatically handle large data are becoming important. We are working on development of MAFFT toward these two directions. Here, we explain (i) the Web interface for recently developed options for large data and (ii) interactive usage to refine sequence data sets and MSAs.",
                        "date": "2018-03-27T00:00:00Z",
                        "citationCount": 1615,
                        "authors": [
                            {
                                "name": "Katoh K."
                            },
                            {
                                "name": "Rozewicki J."
                            },
                            {
                                "name": "Yamada K.D."
                            }
                        ],
                        "journal": "Briefings in Bioinformatics"
                    }
                }
            ],
            "credit": [
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                    "name": "Contact Form",
                    "email": null,
                    "url": "https://mafft.cbrc.jp/alignment/software/mailform.html",
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            },
            "owner": "jison",
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        },
        {
            "name": "Graphia",
            "description": "A platform for the graph-based visualisation and analysis of complex data.\n\nVisualisation tool for the creation and analysis of graphs.\n\nGraphia is a powerful open source visual analytics application developed to aid the interpretation of large and complex datasets.\n\nGraphia can create and visualise graphs from tables of numeric data and display the structures that result. It can also be used to visualise and analyse any data that is already in the form of a graph.",
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                            "term": "Genotyping"
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                            "term": "Expression correlation analysis"
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                            "term": "Deisotoping"
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            "owner": "Niclaskn",
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            "confidence_flag": "tool"
        },
        {
            "name": "msprime",
            "description": "Msprime is a Python package that simulates ancestral histories and DNA sequence data. Msprime uses backwards-in-time \"coalescent\" models which allows it to simulate data very efficiently; however, it is not as flexible as forwards-in-time simulators (e.g. SLiM, fwdpy11).",
            "homepage": "https://tskit.dev/msprime",
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            "version": [],
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                {
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                            "uri": "http://edamontology.org/operation_2426",
                            "term": "Modelling and simulation"
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                    ],
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                    "output": [],
                    "note": null,
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            ],
            "toolType": [
                "Library"
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            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3056",
                    "term": "Population genetics"
                }
            ],
            "operatingSystem": [],
            "language": [
                "Python"
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                {
                    "url": "https://github.com/tskit-dev/msprime",
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                },
                {
                    "url": "https://github.com/tskit-dev/msprime/discussions",
                    "type": [
                        "Discussion forum",
                        "Helpdesk"
                    ],
                    "note": null
                }
            ],
            "download": [],
            "documentation": [
                {
                    "url": "https://tskit.dev/msprime/docs/latest",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1371/journal.pcbi.1004842",
                    "pmid": null,
                    "pmcid": null,
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                        "Primary"
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                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes",
                        "abstract": "© 2016 Kelleher et al.A central challenge in the analysis of genetic variation is to provide realistic genome simulation across millions of samples. Present day coalescent simulations do not scale well, or use approximations that fail to capture important long-range linkage properties. Analysing the results of simulations also presents a substantial challenge, as current methods to store genealogies consume a great deal of space, are slow to parse and do not take advantage of shared structure in correlated trees. We solve these problems by introducing sparse trees and coalescence records as the key units of genealogical analysis. Using these tools, exact simulation of the coalescent with recombination for chromosome-sized regions over hundreds of thousands of samples is possible, and substantially faster than present-day approximate methods. We can also analyse the results orders of magnitude more quickly than with existing methods.",
                        "date": "2016-05-01T00:00:00Z",
                        "citationCount": 161,
                        "authors": [
                            {
                                "name": "Kelleher J."
                            },
                            {
                                "name": "Etheridge A.M."
                            },
                            {
                                "name": "McVean G."
                            }
                        ],
                        "journal": "PLoS Computational Biology"
                    }
                }
            ],
            "credit": [],
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            "owner": "castedo",
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        },
        {
            "name": "MoFi",
            "description": "MoFi annotates glycoprotein mass spectra by integrating hybrid data from the intact protein and glycopeptide level.",
            "homepage": "https://github.com/cdl-biosimilars/mofi/",
            "biotoolsID": "mofi",
            "biotoolsCURIE": "biotools:mofi",
            "version": [
                "1.1"
            ],
            "otherID": [],
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            "function": [],
            "toolType": [
                "Desktop application"
            ],
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            "operatingSystem": [
                "Windows",
                "Linux"
            ],
            "language": [
                "Python"
            ],
            "license": "MIT",
            "collectionID": [],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": null,
            "elixirPlatform": [],
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            "elixirCommunity": [],
            "link": [],
            "download": [
                {
                    "url": "https://github.com/cdl-biosimilars/mofi/releases/tag/v1.1",
                    "type": "Source code",
                    "note": null,
                    "version": "1.1"
                }
            ],
            "documentation": [],
            "publication": [
                {
                    "doi": "10.1021/acs.analchem.8b00019",
                    "pmid": "29624378",
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": "1.1",
                    "note": null,
                    "metadata": {
                        "title": "MoFi: A Software Tool for Annotating Glycoprotein Mass Spectra by Integrating Hybrid Data from the Intact Protein and Glycopeptide Level",
                        "abstract": "© 2018 American Chemical Society.Hybrid mass spectrometry (MS) is an emerging technique for characterizing glycoproteins, which typically display pronounced microheterogeneity. Since hybrid MS combines information from different experimental levels, it crucially depends on computational methods. Here, we describe a novel software tool, MoFi, which integrates hybrid MS data to assign glycans and other post-translational modifications (PTMs) in deconvoluted mass spectra of intact proteins. Its two-stage search algorithm first assigns monosaccharide/PTM compositions to each peak and then compiles a hierarchical list of glycan combinations compatible with these compositions. Importantly, the program only includes those combinations which are supported by a glycan library as derived from glycopeptide or released glycan analysis. By applying MoFi to mass spectra of rituximab, ado-trastuzumab emtansine, and recombinant human erythropoietin, we demonstrate how integration of bottom-up data may be used to refine information collected at the intact protein level. Accordingly, our software reveals that a single mass frequently can be explained by a considerable number of glycoforms. Yet, it simultaneously ranks proteoforms according to their probability, based on a score which is calculated from relative glycan abundances. Notably, glycoforms that comprise identical glycans may nevertheless differ in score if those glycans occupy different sites. Hence, MoFi exposes different layers of complexity that are present in the annotation of a glycoprotein mass spectrum.",
                        "date": "2018-05-01T00:00:00Z",
                        "citationCount": 14,
                        "authors": [
                            {
                                "name": "Skala W."
                            },
                            {
                                "name": "Wohlschlager T."
                            },
                            {
                                "name": "Senn S."
                            },
                            {
                                "name": "Huber G.E."
                            },
                            {
                                "name": "Huber C.G."
                            }
                        ],
                        "journal": "Analytical Chemistry"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Wolfgang Esser-Skala",
                    "email": "wolfgang.esser-skala@plus.ac.at",
                    "url": "https://wolfgang.esser-skala.at",
                    "orcidid": "https://orcid.org/0000-0002-7350-4045",
                    "gridid": null,
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "wolfgang.esser-skala",
            "additionDate": "2021-10-12T14:08:00.659281Z",
            "lastUpdate": "2021-10-12T14:15:34.971531Z",
            "editPermission": {
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        },
        {
            "name": "VarGenius",
            "description": "VarGenius is a platform for analysis of variants from DNA sequencing data. Currently it can be used for WES and Panels. Starting from fastq files it can execute the GATK Best Practices pipeline doing both single calling and joint calling. Then it executes Annovar for variant annotation and generates a readable output in tabular and XLS format. All the data extracted from the samples (variants, genotypes, etc..) are uploaded into a Postgres database which can be used for further downstream analyses.",
            "homepage": "https://github.com/frankMusacchia/VarGenius",
            "biotoolsID": "VarGenius",
            "biotoolsCURIE": "biotools:VarGenius",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [
                {
                    "biotoolsID": "vargenius-hzd",
                    "type": "includes"
                }
            ],
            "function": [],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2533",
                    "term": "DNA mutation"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
            "language": [
                "R",
                "Perl"
            ],
            "license": null,
            "collectionID": [],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [
                "Tools"
            ],
            "elixirNode": [
                "Italy"
            ],
            "elixirCommunity": [],
            "link": [
                {
                    "url": "https://groups.google.com/forum/#!forum/VarGenius",
                    "type": [
                        "Helpdesk"
                    ],
                    "note": null
                }
            ],
            "download": [
                {
                    "url": "https://github.com/frankMusacchia/VarGenius",
                    "type": "Source code",
                    "note": null,
                    "version": "1.0"
                }
            ],
            "documentation": [
                {
                    "url": "https://github.com/frankMusacchia/VarGenius/tree/master/GUIDE",
                    "type": [
                        "Installation instructions"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/s12859-018-2532-4",
                    "pmid": "30541431",
                    "pmcid": "PMC6291943",
                    "type": [
                        "Primary",
                        "Benchmarking study"
                    ],
                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "VarGenius executes cohort-level DNA-seq variant calling and annotation and allows to manage the resulting data through a PostgreSQL database",
                        "abstract": "© 2018 The Author(s).Background: Targeted resequencing has become the most used and cost-effective approach for identifying causative mutations of Mendelian diseases both for diagnostics and research purposes. Due to very rapid technological progress, NGS laboratories are expanding their capabilities to address the increasing number of analyses. Several open source tools are available to build a generic variant calling pipeline, but a tool able to simultaneously execute multiple analyses, organize, and categorize the samples is still missing. Results: Here we describe VarGenius, a Linux based command line software able to execute customizable pipelines for the analysis of multiple targeted resequencing data using parallel computing. VarGenius provides a database to store the output of the analysis (calling quality statistics, variant annotations, internal allelic variant frequencies) and sample information (personal data, genotypes, phenotypes). VarGenius can also perform the \"joint analysis\" of hundreds of samples with a single command, drastically reducing the time for the configuration and execution of the analysis. VarGenius executes the standard pipeline of the Genome Analysis Tool-Kit (GATK) best practices (GBP) for germinal variant calling, annotates the variants using Annovar, and generates a user-friendly output displaying the results through a web page. VarGenius has been tested on a parallel computing cluster with 52 machines with 120GB of RAM each. Under this configuration, a 50 M whole exome sequencing (WES) analysis for a family was executed in about 7h (trio or quartet); a joint analysis of 30 WES in about 24 h and the parallel analysis of 34 single samples from a 1 M panel in about 2 h. Conclusions: We developed VarGenius, a \"master\" tool that faces the increasing demand of heterogeneous NGS analyses and allows maximum flexibility for downstream analyses. It paves the way to a different kind of analysis, centered on cohorts rather than on singleton. Patient and variant information are stored into the database and any output file can be accessed programmatically. VarGenius can be used for routine analyses by biomedical researchers with basic Linux skills providing additional flexibility for computational biologists to develop their own algorithms for the comparison and analysis of data. The software is freely available at: https://github.com/frankMusacchia/VarGenius",
                        "date": "2018-12-12T00:00:00Z",
                        "citationCount": 8,
                        "authors": [
                            {
                                "name": "Musacchia F."
                            },
                            {
                                "name": "Ciolfi A."
                            },
                            {
                                "name": "Mutarelli M."
                            },
                            {
                                "name": "Bruselles A."
                            },
                            {
                                "name": "Castello R."
                            },
                            {
                                "name": "Pinelli M."
                            },
                            {
                                "name": "Basu S."
                            },
                            {
                                "name": "Banfi S."
                            },
                            {
                                "name": "Casari G."
                            },
                            {
                                "name": "Tartaglia M."
                            },
                            {
                                "name": "Nigro V."
                            },
                            {
                                "name": "Torella A."
                            },
                            {
                                "name": "Esposito G."
                            },
                            {
                                "name": "Cappuccio G."
                            },
                            {
                                "name": "Mancano G."
                            },
                            {
                                "name": "Maitz S."
                            },
                            {
                                "name": "Brunetti-Pierri N."
                            },
                            {
                                "name": "Parenti G."
                            },
                            {
                                "name": "Selicorni A."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "ELIXIR-ITA-TELETHON",
                    "email": "f.musacchia@tigem.it",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-9440-1080",
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                    "typeEntity": "Person",
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                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "FrankMusacchia",
            "additionDate": "2019-02-14T08:17:30Z",
            "lastUpdate": "2021-10-08T08:49:21.521364Z",
            "editPermission": {
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            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "mixomics",
            "description": "The tool offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection.",
            "homepage": "http://mixomics.org/",
            "biotoolsID": "mixomics",
            "biotoolsCURIE": "biotools:mixomics",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0337",
                            "term": "Visualisation"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3432",
                            "term": "Clustering"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3935",
                            "term": "Dimensionality reduction"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3960",
                            "term": "Principal component analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2082",
                                "term": "Matrix"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3751",
                                    "term": "DSV"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1772",
                                "term": "Score"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3751",
                                    "term": "DSV"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Library"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
                    "uri": "http://edamontology.org/topic_2269",
                    "term": "Statistics and probability"
                },
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
                },
                {
                    "uri": "http://edamontology.org/topic_3391",
                    "term": "Omics"
                },
                {
                    "uri": "http://edamontology.org/topic_0092",
                    "term": "Data visualisation"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
                },
                {
                    "uri": "http://edamontology.org/topic_0121",
                    "term": "Proteomics"
                },
                {
                    "uri": "http://edamontology.org/topic_3172",
                    "term": "Metabolomics"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
            "language": [
                "R"
            ],
            "license": "GPL-3.0",
            "collectionID": [],
            "maturity": "Mature",
            "cost": "Free of charge",
            "accessibility": "Open access",
            "elixirPlatform": [],
            "elixirNode": [],
            "elixirCommunity": [],
            "link": [],
            "download": [
                {
                    "url": "https://cran.r-project.org/web/packages/mixOmics/",
                    "type": "Binaries",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://qfab.org/biostatistics/mixOmics",
                    "type": [
                        "General"
                    ],
                    "note": null
                },
                {
                    "url": "https://cran.r-project.org/web/packages/mixOmics/mixOmics.pdf",
                    "type": [
                        "User manual"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/btp515",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "IntegrOmics: An R package to unravel relationships between two omics datasets",
                        "abstract": "Motivation: With the availability of many 'omics' data, such as transcriptomics, proteomics or metabolomics, the integrative or joint analysis of multiple datasets from different technology platforms is becoming crucial to unravel the relationships between different biological functional levels. However, the development of such an analysis is a major computational and technical challenge as most approaches suffer from high data dimensionality. New methodologies need to be developed and validated. Results: integrOmics efficiently performs integrative analyses of two types of 'omics' variables that are measured on the same samples. It includes a regularized version of canonical correlation analysis to enlighten correlations between two datasets, and a sparse version of partial least squares (PLS) regression that includes simultaneous variable selection in both datasets. The usefulness of both approaches has been demonstrated previously and successfully applied in various integrative studies. © The Author(s) 2009. Published by Oxford University Press.",
                        "date": "2009-01-01T00:00:00Z",
                        "citationCount": 256,
                        "authors": [
                            {
                                "name": "Le Cao K.-A."
                            },
                            {
                                "name": "Gonzalez I."
                            },
                            {
                                "name": "Dejean S."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1371/journal.pcbi.1005752",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "mixOmics: An R package for ‘omics feature selection and multiple data integration",
                        "abstract": "© 2017 Rohart et al.The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.",
                        "date": "2017-11-01T00:00:00Z",
                        "citationCount": 758,
                        "authors": [
                            {
                                "name": "Rohart F."
                            },
                            {
                                "name": "Gautier B."
                            },
                            {
                                "name": "Singh A."
                            },
                            {
                                "name": "Le Cao K.-A."
                            }
                        ],
                        "journal": "PLoS Computational Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "unimelb.edu.au",
                    "email": null,
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Institute",
                    "typeRole": [
                        "Provider"
                    ],
                    "note": null
                },
                {
                    "name": "Australia",
                    "email": "mixomics@math.univ-toulouse.fr",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                    "typeEntity": "Person",
                    "typeRole": [
                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "mv.schneider@unimelb.edu.au",
            "additionDate": "2016-10-02T22:18:58Z",
            "lastUpdate": "2021-09-30T10:49:02.511785Z",
            "editPermission": {
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                    "bayjan"
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            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        },
        {
            "name": "PADMet",
            "description": "Portable Database for Metabolism. It is a format to centralizes, in a new graph-based PADMet, all information about a metabolic network. It also provides methods to import, to update, to analyse and to export data, in a library.",
            "homepage": "https://github.com/AuReMe/padmet",
            "biotoolsID": "PADMet",
            "biotoolsCURIE": "biotools:PADMet",
            "version": [],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_2409",
                            "term": "Data handling"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3928",
                            "term": "Pathway analysis"
                        },
                        {
                            "uri": "http://edamontology.org/operation_3927",
                            "term": "Network analysis"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2600",
                                "term": "Pathway or network"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2585",
                                    "term": "SBML"
                                },
                                {
                                    "uri": "http://edamontology.org/format_1637",
                                    "term": "dat"
                                }
                            ]
                        }
                    ],
                    "output": [],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0602",
                    "term": "Molecular interactions, pathways and networks"
                },
                {
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            "download": [
                {
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                    "url": "https://padmet.readthedocs.io/",
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            "publication": [
                {
                    "doi": "10.1371/journal.pcbi.1006146",
                    "pmid": "29791443",
                    "pmcid": "PMC5988327",
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                    "version": null,
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                    "metadata": {
                        "title": "Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models",
                        "abstract": "© 2018 Aite et al.Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from “à la carte” pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.",
                        "date": "2018-05-01T00:00:00Z",
                        "citationCount": 27,
                        "authors": [
                            {
                                "name": "Aite M."
                            },
                            {
                                "name": "Chevallier M."
                            },
                            {
                                "name": "Frioux C."
                            },
                            {
                                "name": "Trottier C."
                            },
                            {
                                "name": "Got J."
                            },
                            {
                                "name": "Cortes M.P."
                            },
                            {
                                "name": "Mendoza S.N."
                            },
                            {
                                "name": "Carrier G."
                            },
                            {
                                "name": "Dameron O."
                            },
                            {
                                "name": "Guillaudeux N."
                            },
                            {
                                "name": "Latorre M."
                            },
                            {
                                "name": "Loira N."
                            },
                            {
                                "name": "Markov G.V."
                            },
                            {
                                "name": "Maass A."
                            },
                            {
                                "name": "Siegel A."
                            }
                        ],
                        "journal": "PLoS Computational Biology"
                    }
                },
                {
                    "doi": "10.1101/462556",
                    "pmid": null,
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": null
                },
                {
                    "doi": "10.3390/antiox8110564",
                    "pmid": "31744163",
                    "pmcid": null,
                    "type": [],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Genome–scale metabolic networks shed light on the carotenoid biosynthesis pathway in the brown algae saccharina japonica and cladosiphon okamuranus",
                        "abstract": "© 2019 by the authors. Licensee MDPI, Basel, Switzerland.Understanding growth mechanisms in brown algae is a current scientific and economic challenge that can benefit from the modeling of their metabolic networks. The sequencing of the genomes of Saccharina japonica and Cladosiphon okamuranus has provided the necessary data for the reconstruction of Genome–Scale Metabolic Networks (GSMNs). The same in silico method deployed for the GSMN reconstruction of Ectocarpus siliculosus to investigate the metabolic capabilities of these two algae, was used. Integrating metabolic profiling data from the literature, we provided functional GSMNs composed of an average of 2230 metabolites and 3370 reactions. Based on these GSMNs and previously published work, we propose a model for the biosynthetic pathways of the main carotenoids in these two algae. We highlight, on the one hand, the reactions and enzymes that have been preserved through evolution and, on the other hand, the specificities related to brown algae. Our data further indicate that, if abscisic acid is produced by Saccharina japonica, its biosynthesis pathway seems to be different in its final steps from that described in land plants. Thus, our work illustrates the potential of GSMNs reconstructions for formalizing hypotheses that can be further tested using targeted biochemical approaches.",
                        "date": "2019-11-01T00:00:00Z",
                        "citationCount": 5,
                        "authors": [
                            {
                                "name": "Negre D."
                            },
                            {
                                "name": "Aite M."
                            },
                            {
                                "name": "Belcour A."
                            },
                            {
                                "name": "Frioux C."
                            },
                            {
                                "name": "Brillet-Gueguen L."
                            },
                            {
                                "name": "Liu X."
                            },
                            {
                                "name": "Bordron P."
                            },
                            {
                                "name": "Godfroy O."
                            },
                            {
                                "name": "Lipinska A.P."
                            },
                            {
                                "name": "Leblanc C."
                            },
                            {
                                "name": "Siegel A."
                            },
                            {
                                "name": "Dittami S.M."
                            },
                            {
                                "name": "Corre E."
                            },
                            {
                                "name": "Markov G.V."
                            }
                        ],
                        "journal": "Antioxidants"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Méziane Aite",
                    "email": null,
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-9086-1485",
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                },
                {
                    "name": "Jeanne Got",
                    "email": "jeanne.got@irisa.fr",
                    "url": "http://www.irisa.fr/dyliss/jeanne.got",
                    "orcidid": "https://orcid.org/0000-0002-2310-0843",
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                        "Support"
                    ],
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                },
                {
                    "name": "Anne Siegel",
                    "email": "anne.siegel@irisa.fr",
                    "url": "http://www.irisa.fr/dyliss/anne.siegel",
                    "orcidid": "https://orcid.org/0000-0001-6542-1568",
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            ],
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            },
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        },
        {
            "name": "SPOT-RNA",
            "description": "RNA secondary structure predictor",
            "homepage": "http://sparks-lab.org/jaswinder/server/SPOT-RNA/",
            "biotoolsID": "SPOT-RNA",
            "biotoolsCURIE": "biotools:SPOT-RNA",
            "version": [],
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                            "uri": "http://edamontology.org/operation_0278",
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                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_3495",
                                "term": "RNA sequence"
                            },
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                                {
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                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_0880",
                                "term": "RNA secondary structure"
                            },
                            "format": []
                        }
                    ],
                    "note": null,
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_0097",
                    "term": "Nucleic acid structure analysis"
                }
            ],
            "operatingSystem": [
                "Linux"
            ],
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            "link": [
                {
                    "url": "http://sparks-lab.org/jaswinder/server/SPOT-RNA/",
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                    ],
                    "note": null
                }
            ],
            "download": [
                {
                    "url": "https://github.com/jaswindersingh2/SPOT-RNA",
                    "type": "Downloads page",
                    "note": null,
                    "version": null
                }
            ],
            "documentation": [
                {
                    "url": "https://github.com/jaswindersingh2/SPOT-RNA/blob/master/README.md",
                    "type": [
                        "User manual"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1038/s41467-019-13395-9",
                    "pmid": "31776342",
                    "pmcid": "PMC6881452",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning",
                        "abstract": "© 2019, The Author(s).The majority of our human genome transcribes into noncoding RNAs with unknown structures and functions. Obtaining functional clues for noncoding RNAs requires accurate base-pairing or secondary-structure prediction. However, the performance of such predictions by current folding-based algorithms has been stagnated for more than a decade. Here, we propose the use of deep contextual learning for base-pair prediction including those noncanonical and non-nested (pseudoknot) base pairs stabilized by tertiary interactions. Since only < 250 nonredundant, high-resolution RNA structures are available for model training, we utilize transfer learning from a model initially trained with a recent high-quality bpRNA dataset of > 10,000 nonredundant RNAs made available through comparative analysis. The resulting method achieves large, statistically significant improvement in predicting all base pairs, noncanonical and non-nested base pairs in particular. The proposed method (SPOT-RNA), with a freely available server and standalone software, should be useful for improving RNA structure modeling, sequence alignment, and functional annotations.",
                        "date": "2019-12-01T00:00:00Z",
                        "citationCount": 38,
                        "authors": [
                            {
                                "name": "Singh J."
                            },
                            {
                                "name": "Hanson J."
                            },
                            {
                                "name": "Paliwal K."
                            },
                            {
                                "name": "Zhou Y."
                            }
                        ],
                        "journal": "Nature Communications"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Jaswinder Singh",
                    "email": "jaswinder.singh3@griffithuni.edu.au",
                    "url": "http://sparks-lab.org",
                    "orcidid": "https://orcid.org/0000-0002-0478-5533",
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                    "typeEntity": "Person",
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                        "Primary contact"
                    ],
                    "note": "PhD Candidate at Signal Processing Lab, Griffith University"
                }
            ],
            "community": null,
            "owner": "jaswinder_singh",
            "additionDate": "2019-12-25T03:46:00Z",
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        },
        {
            "name": "STAR",
            "description": "Ultrafast universal RNA-seq data aligner",
            "homepage": "http://code.google.com/p/rna-star/",
            "biotoolsID": "star",
            "biotoolsCURIE": "biotools:star",
            "version": [],
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                            "uri": "http://edamontology.org/operation_0292",
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                        }
                    ],
                    "input": [],
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                }
            ],
            "toolType": [
                "Command-line tool"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_3170",
                    "term": "RNA-Seq"
                },
                {
                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Mac"
            ],
            "language": [
                "C++"
            ],
            "license": "GPL-3.0",
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            ],
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            "documentation": [
                {
                    "url": "https://github.com/alexdobin/STAR/releases",
                    "type": [
                        "General"
                    ],
                    "note": null
                }
            ],
            "publication": [
                {
                    "doi": "10.1093/bioinformatics/bts635",
                    "pmid": "23104886",
                    "pmcid": "PMC3530905",
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "STAR: Ultrafast universal RNA-seq aligner",
                        "abstract": "Motivation: Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases.Results: To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. © The Author(s) 2012. Published by Oxford University Press.",
                        "date": "2013-01-01T00:00:00Z",
                        "citationCount": 12650,
                        "authors": [
                            {
                                "name": "Dobin A."
                            },
                            {
                                "name": "Davis C.A."
                            },
                            {
                                "name": "Schlesinger F."
                            },
                            {
                                "name": "Drenkow J."
                            },
                            {
                                "name": "Zaleski C."
                            },
                            {
                                "name": "Jha S."
                            },
                            {
                                "name": "Batut P."
                            },
                            {
                                "name": "Chaisson M."
                            },
                            {
                                "name": "Gingeras T.R."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                },
                {
                    "doi": "10.1038/nmeth.4106",
                    "pmid": "27941783",
                    "pmcid": "PMC5792058",
                    "type": [
                        "Benchmarking study"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Simulation-based comprehensive benchmarking of RNA-seq aligners",
                        "abstract": "© 2017 Nature America, inc., part of springer Nature. All rights reserved.Alignment is the first step in most RNA-seq analysis pipelines, and the accuracy of downstream analyses depends heavily on it. Unlike most steps in the pipeline, alignment is particularly amenable to benchmarking with simulated data. We performed a comprehensive benchmarking of 14 common splice-aware aligners for base, read, and exon junction-level accuracy and compared default with optimized parameters. We found that performance varied by genome complexity, and accuracy and popularity were poorly correlated. The most widely cited tool underperforms for most metrics, particularly when using default settings.",
                        "date": "2017-02-01T00:00:00Z",
                        "citationCount": 115,
                        "authors": [
                            {
                                "name": "Baruzzo G."
                            },
                            {
                                "name": "Hayer K.E."
                            },
                            {
                                "name": "Kim E.J."
                            },
                            {
                                "name": "DI Camillo B."
                            },
                            {
                                "name": "Fitzgerald G.A."
                            },
                            {
                                "name": "Grant G.R."
                            }
                        ],
                        "journal": "Nature Methods"
                    }
                },
                {
                    "doi": "10.1093/bioinformatics/btu146",
                    "pmid": "24626854",
                    "pmcid": null,
                    "type": [
                        "Benchmarking study"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "Lacking alignments? The next-generation sequencing mapper segemehl revisited",
                        "abstract": "Motivation: Next-generation sequencing has become an important tool in molecular biology. Various protocols to investigate genomic, transcriptomic and epigenomic features across virtually all species and tissues have been devised. For most of these experiments, one of the first crucial steps of bioinformatic analysis is the mapping of reads to reference genomes. Results: Here, we present thorough benchmarks of our read aligner segemehl in comparison with other state-of-the-art methods. Furthermore, we introduce the tool lack to rescue unmapped RNA-seq reads which works in conjunction with segemehl and many other frequently used split-read aligners. © The Author 2014.",
                        "date": "2014-07-01T00:00:00Z",
                        "citationCount": 52,
                        "authors": [
                            {
                                "name": "Otto C."
                            },
                            {
                                "name": "Stadler P.F."
                            },
                            {
                                "name": "Hoffmann S."
                            }
                        ],
                        "journal": "Bioinformatics"
                    }
                }
            ],
            "credit": [
                {
                    "name": "rna-styar team",
                    "email": "dobin@cshl.edu",
                    "url": null,
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                    "typeEntity": "Person",
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                        "Primary contact"
                    ],
                    "note": null
                }
            ],
            "community": null,
            "owner": "seqwiki_import",
            "additionDate": "2017-01-13T13:16:43Z",
            "lastUpdate": "2021-09-28T07:28:50.397510Z",
            "editPermission": {
                "type": "group",
                "authors": [
                    "hmenager",
                    "animalandcropgenomics"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
            "elixir_badge": 0,
            "confidence_flag": null
        }
    ]
}