Managing high-density genotyping data with Gigwa
Next generation sequencing technologies enabled high-density genotyping for large numbers of samples. Nowadays SNP calling pipelines produce up to millions of such markers, but which need to be filtered in various ways according to the type of analyses. One of the main challenges still lies in the management of an increasing volume of genotyping files that are difficult to handle for many applications. Here, we provide a practical guide for efficiently managing large genomic variation data using Gigwa, a user-friendly, scalable and versatile application that may be deployed either remotely on web servers or on a local machine.
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dig-cirad-fr-6003202023-03-06T11:41:23Z http://agritrop.cirad.fr/600320/ http://agritrop.cirad.fr/600320/ Managing high-density genotyping data with Gigwa. Sempere Guilhem, Larmande Pierre, Rouard Mathieu. 2022. In : Plant Bioinformatics: methods and protocols. Edwards David (ed.). Cham : Springer-Humana Press, 415-427. (Methods in Molecular Biology, 2443, 2443) ISBN 978-1-0716-2066-3https://doi.org/10.1007/978-1-0716-2067-0_21 <https://doi.org/10.1007/978-1-0716-2067-0_21> Researchers Managing high-density genotyping data with Gigwa Sempere, Guilhem Larmande, Pierre Rouard, Mathieu eng 2022 Springer Plant Bioinformatics: methods and protocols Next generation sequencing technologies enabled high-density genotyping for large numbers of samples. Nowadays SNP calling pipelines produce up to millions of such markers, but which need to be filtered in various ways according to the type of analyses. One of the main challenges still lies in the management of an increasing volume of genotyping files that are difficult to handle for many applications. Here, we provide a practical guide for efficiently managing large genomic variation data using Gigwa, a user-friendly, scalable and versatile application that may be deployed either remotely on web servers or on a local machine. book_section info:eu-repo/semantics/bookPart Chapter info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/600320/13/ID600320.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/978-1-0716-2067-0_21 10.1007/978-1-0716-2067-0_21 https://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=221586 info:eu-repo/semantics/altIdentifier/doi/10.1007/978-1-0716-2067-0_21 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/978-1-0716-2067-0_21 |
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Next generation sequencing technologies enabled high-density genotyping for large numbers of samples. Nowadays SNP calling pipelines produce up to millions of such markers, but which need to be filtered in various ways according to the type of analyses. One of the main challenges still lies in the management of an increasing volume of genotyping files that are difficult to handle for many applications. Here, we provide a practical guide for efficiently managing large genomic variation data using Gigwa, a user-friendly, scalable and versatile application that may be deployed either remotely on web servers or on a local machine. |
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Sempere, Guilhem Larmande, Pierre Rouard, Mathieu |
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Sempere, Guilhem Larmande, Pierre Rouard, Mathieu Managing high-density genotyping data with Gigwa |
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Sempere, Guilhem Larmande, Pierre Rouard, Mathieu |
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Sempere, Guilhem |
title |
Managing high-density genotyping data with Gigwa |
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Managing high-density genotyping data with Gigwa |
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Managing high-density genotyping data with Gigwa |
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Managing high-density genotyping data with Gigwa |
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Managing high-density genotyping data with Gigwa |
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managing high-density genotyping data with gigwa |
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Springer |
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http://agritrop.cirad.fr/600320/ http://agritrop.cirad.fr/600320/13/ID600320.pdf |
work_keys_str_mv |
AT sempereguilhem managinghighdensitygenotypingdatawithgigwa AT larmandepierre managinghighdensitygenotypingdatawithgigwa AT rouardmathieu managinghighdensitygenotypingdatawithgigwa |
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1762943330060075008 |