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            "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.",
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                    "term": "Omics"
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                    "term": "Transcriptomics"
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                        "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."
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                            {
                                "name": "Dejean S."
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                        "journal": "Bioinformatics"
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                    "doi": "10.1371/journal.pcbi.1005752",
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                        "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."
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                            {
                                "name": "Gautier B."
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                            {
                                "name": "Singh A."
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                            {
                                "name": "Le Cao K.-A."
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                        "title": "ANGSD: Analysis of Next Generation Sequencing Data",
                        "abstract": "© 2014 Korneliussen et al.Background: High-throughput DNA sequencing technologies are generating vast amounts of data. Fast, flexible and memory efficient implementations are needed in order to facilitate analyses of thousands of samples simultaneously. Results: We present a multithreaded program suite called ANGSD. This program can calculate various summary statistics, and perform association mapping and population genetic analyses utilizing the full information in next generation sequencing data by working directly on the raw sequencing data or by using genotype likelihoods. Conclusions: The open source c/c++ program ANGSD is available at . The program is tested and validated on GNU/Linux systems. The program facilitates multiple input formats including BAM and imputed beagle genotype probability files. The program allow the user to choose between combinations of existing methods and can perform analysis that is not implemented elsewhere.",
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            "description": "The main focus lays on the integrated inference algorithms, concluding the proteins from a set of identified spectra. But it also allows you to inspect your peptide spectrum matches, calculate FDR values across different search engine results and visualize the correspondence between PSMs, peptides and proteins.",
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            "description": "An open source C++ library of efficient algorithms and data structures for the analysis of sequences with the focus on biological data. Our library applies a unique generic design that guarantees high performance, generality, extensibility, and integration with other libraries. The library is easy to use and simplifies the development of new software tools with a minimal loss of performance. Additionally, it includes several ready-made command line tools for common tasks in sequence analysis.",
            "homepage": "https://www.seqan.de",
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            "biotoolsCURIE": "biotools:seqan",
            "version": [
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                            "term": "Sequence analysis"
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                            "term": "Nucleic acid sequence analysis"
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                            "uri": "http://edamontology.org/operation_0292",
                            "term": "Sequence alignment"
                        },
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                            "uri": "http://edamontology.org/operation_3198",
                            "term": "Read mapping"
                        },
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                            "term": "Variant calling"
                        }
                    ],
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                                    "term": "FASTQ-like format (text)"
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                    "uri": "http://edamontology.org/topic_0797",
                    "term": "Comparative genomics"
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                    "term": "Metagenomics"
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                    "uri": "http://edamontology.org/topic_3308",
                    "term": "Transcriptomics"
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                    "uri": "http://edamontology.org/topic_0091",
                    "term": "Bioinformatics"
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                "KNIME",
                "de.NBI",
                "SeqAn"
            ],
            "maturity": "Mature",
            "cost": "Free of charge",
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            "link": [
                {
                    "url": "https://github.com/seqan",
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                        "Repository"
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                }
            ],
            "download": [
                {
                    "url": "http://packages.seqan.de/",
                    "type": "Source code",
                    "note": null,
                    "version": "2.4.0"
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                    "url": "http://packages.seqan.de/",
                    "type": "Binaries",
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                    "version": "2.4.0"
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                    "type": "Source code",
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                    "version": "3.0.3"
                }
            ],
            "documentation": [
                {
                    "url": "https://www.seqan.de",
                    "type": [
                        "Citation instructions"
                    ],
                    "note": null
                },
                {
                    "url": "https://docs.seqan.de",
                    "type": [
                        "API documentation",
                        "General"
                    ],
                    "note": "SeqAn 3.0.3 || API SeqAn 2.4.0"
                },
                {
                    "url": "https://seqan.readthedocs.org/en/master/index.html",
                    "type": [
                        "General"
                    ],
                    "note": "Tutorials SeqAn 2.4.0"
                }
            ],
            "publication": [
                {
                    "doi": "10.1186/1471-2105-9-11",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "SeqAn an efficient, generic C++ library for sequence analysis",
                        "abstract": "Background: The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome 1 would not have been possible without advanced assembly algorithms. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there is a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. Results: To remedy this trend we propose the use of SeqAn, a library of efficient data types and algorithms for sequence analysis in computational biology. SeqAn comprises implementations of existing, practical state-of-the-art algorithmic components to provide a sound basis for algorithm testing and development. In this paper we describe the design and content of SeqAn and demonstrate its use by giving two examples. In the first example we show an application of SeqAn as an experimental platform by comparing different exact string matching algorithms. The second example is a simple version of the well-known MUMmer tool rewritten in SeqAn. Results indicate that our implementation is very efficient and versatile to use. Conclusion: We anticipate that SeqAn greatly simplifies the rapid development of new bioinformatics tools by providing a collection of readily usable, well-designed algorithmic components which are fundamental for the field of sequence analysis. This leverages not only the implementation of new algorithms, but also enables a sound analysis and comparison of existing algorithms. © 2008 Döring et al; licensee BioMed Central Ltd.",
                        "date": "2008-01-09T00:00:00Z",
                        "citationCount": 185,
                        "authors": [
                            {
                                "name": "Doring A."
                            },
                            {
                                "name": "Weese D."
                            },
                            {
                                "name": "Rausch T."
                            },
                            {
                                "name": "Reinert K."
                            }
                        ],
                        "journal": "BMC Bioinformatics"
                    }
                },
                {
                    "doi": "10.1016/j.jbiotec.2017.07.017",
                    "pmid": null,
                    "pmcid": null,
                    "type": [
                        "Primary"
                    ],
                    "version": null,
                    "note": null,
                    "metadata": {
                        "title": "The SeqAn C++ template library for efficient sequence analysis: A resource for programmers",
                        "abstract": "© 2017 The Author(s)Background The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome (Venter et al., 2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there was a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. We previously addressed this by introducing the SeqAn library of efficient data types and algorithms in 2008 (Döring et al., 2008). Results The SeqAn library has matured considerably since its first publication 9 years ago. In this article we review its status as an established resource for programmers in the field of sequence analysis and its contributions to many analysis tools. Conclusions We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.",
                        "date": "2017-11-10T00:00:00Z",
                        "citationCount": 28,
                        "authors": [
                            {
                                "name": "Reinert K."
                            },
                            {
                                "name": "Dadi T.H."
                            },
                            {
                                "name": "Ehrhardt M."
                            },
                            {
                                "name": "Hauswedell H."
                            },
                            {
                                "name": "Mehringer S."
                            },
                            {
                                "name": "Rahn R."
                            },
                            {
                                "name": "Kim J."
                            },
                            {
                                "name": "Pockrandt C."
                            },
                            {
                                "name": "Winkler J."
                            },
                            {
                                "name": "Siragusa E."
                            },
                            {
                                "name": "Urgese G."
                            },
                            {
                                "name": "Weese D."
                            }
                        ],
                        "journal": "Journal of Biotechnology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Freie Universität Berlin",
                    "email": null,
                    "url": null,
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                },
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                    "name": "Center for Integrative Bioinformatics (CiBi)",
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                    "orcidid": null,
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                },
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                    ],
                    "note": null
                },
                {
                    "name": "Mailing list answered by the developers",
                    "email": "seqan-dev@lists.fu-berlin.de",
                    "url": null,
                    "orcidid": null,
                    "gridid": null,
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                }
            ],
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            "owner": "rrahn",
            "additionDate": "2016-01-19T16:16:51Z",
            "lastUpdate": "2021-05-31T13:04:42Z",
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                "authors": [
                    "eseiler"
                ]
            },
            "validated": 1,
            "homepage_status": 0,
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            "confidence_flag": null
        },
        {
            "name": "PredictSNP",
            "description": "A consensus classifier that combines six of the top performing tools for the prediction of the effects of mutation on protein function. The obtained results are provided together with annotations extracted from the Protein Mutant Database and the UniProt database.",
            "homepage": "https://loschmidt.chemi.muni.cz/predictsnp1",
            "biotoolsID": "predictsnp",
            "biotoolsCURIE": "biotools:predictsnp",
            "version": [
                "1.0"
            ],
            "otherID": [],
            "relation": [
                {
                    "biotoolsID": "mapp",
                    "type": "uses"
                },
                {
                    "biotoolsID": "nssnpanalyzer",
                    "type": "uses"
                },
                {
                    "biotoolsID": "panther",
                    "type": "uses"
                },
                {
                    "biotoolsID": "phd-snp",
                    "type": "uses"
                },
                {
                    "biotoolsID": "polyphen",
                    "type": "uses"
                },
                {
                    "biotoolsID": "polyphen-2",
                    "type": "uses"
                },
                {
                    "biotoolsID": "sift",
                    "type": "uses"
                },
                {
                    "biotoolsID": "snap",
                    "type": "uses"
                }
            ],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_0331",
                            "term": "Variant effect prediction"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2974",
                                "term": "Protein sequence (raw)"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_1929",
                                    "term": "FASTA"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_1277",
                                "term": "Protein features"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                },
                                {
                                    "uri": "http://edamontology.org/format_2330",
                                    "term": "Textual format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2291",
                                "term": "UniProt ID"
                            },
                            "format": []
                        }
                    ],
                    "note": "Prediction of the effect of amino acid substitution on protein function",
                    "cmd": null
                }
            ],
            "toolType": [
                "Command-line tool",
                "Web application"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2885",
                    "term": "DNA polymorphism"
                },
                {
                    "uri": "http://edamontology.org/topic_3063",
                    "term": "Medical informatics"
                },
                {
                    "uri": "http://edamontology.org/topic_3325",
                    "term": "Rare diseases"
                },
                {
                    "uri": "http://edamontology.org/topic_0078",
                    "term": "Proteins"
                },
                {
                    "uri": "http://edamontology.org/topic_3053",
                    "term": "Genetics"
                }
            ],
            "operatingSystem": [
                "Linux",
                "Windows",
                "Mac"
            ],
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                "JavaScript",
                "Java",
                "Python"
            ],
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            "collectionID": [
                "Czech Republic",
                "Rare Disease",
                "ELIXIR-CZ"
            ],
            "maturity": "Mature",
            "cost": "Free of charge (with restrictions)",
            "accessibility": "Open access",
            "elixirPlatform": [
                "Tools"
            ],
            "elixirNode": [
                "Czech Republic"
            ],
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            "link": [],
            "download": [
                {
                    "url": "https://loschmidt.chemi.muni.cz/predictsnp1/docs/predictsnp-1.0.tar.gz",
                    "type": "Binaries",
                    "note": null,
                    "version": null
                }
            ],
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                {
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            ],
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                {
                    "doi": "10.1371/journal.pcbi.1003440",
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                    "version": "1.0",
                    "note": null,
                    "metadata": {
                        "title": "PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations",
                        "abstract": "Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. © 2014 Bendl et al.",
                        "date": "2014-01-01T00:00:00Z",
                        "citationCount": 320,
                        "authors": [
                            {
                                "name": "Bendl J."
                            },
                            {
                                "name": "Stourac J."
                            },
                            {
                                "name": "Salanda O."
                            },
                            {
                                "name": "Pavelka A."
                            },
                            {
                                "name": "Wieben E.D."
                            },
                            {
                                "name": "Zendulka J."
                            },
                            {
                                "name": "Brezovsky J."
                            },
                            {
                                "name": "Damborsky J."
                            }
                        ],
                        "journal": "PLoS Computational Biology"
                    }
                }
            ],
            "credit": [
                {
                    "name": "Jaroslav Bendl",
                    "email": "jaroslav.bendl@gmail.com",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0001-9989-2720",
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                    "name": "Jan Stourac",
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