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pyGenClean: efficient tool for genetic data clean up before association testing
Summary: Genetic association studies making use of high-throughput genotyping arrays need to process large amounts of data in the order of millions of markers per experiment. The first step of any analysis with genotyping arrays is typically the conduct of a thorough data clean up and quality contro...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694635/ https://www.ncbi.nlm.nih.gov/pubmed/23652425 http://dx.doi.org/10.1093/bioinformatics/btt261 |
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author | Lemieux Perreault, Louis-Philippe Provost, Sylvie Legault, Marc-André Barhdadi, Amina Dubé, Marie-Pierre |
author_facet | Lemieux Perreault, Louis-Philippe Provost, Sylvie Legault, Marc-André Barhdadi, Amina Dubé, Marie-Pierre |
author_sort | Lemieux Perreault, Louis-Philippe |
collection | PubMed |
description | Summary: Genetic association studies making use of high-throughput genotyping arrays need to process large amounts of data in the order of millions of markers per experiment. The first step of any analysis with genotyping arrays is typically the conduct of a thorough data clean up and quality control to remove poor quality genotypes and generate metrics to inform and select individuals for downstream statistical analysis. We have developed pyGenClean, a bioinformatics tool to facilitate and standardize the genetic data clean up pipeline with genotyping array data. In conjunction with a source batch-queuing system, the tool minimizes data manipulation errors, accelerates the completion of the data clean up process and provides informative plots and metrics to guide decision making for statistical analysis. Availability and implementation: pyGenClean is an open source Python 2.7 software and is freely available, along with documentation and examples, from http://www.statgen.org. Contact: louis-philippe.lemieux.perreault@umontreal.ca or marie-pierre.dube@statgen.org |
format | Online Article Text |
id | pubmed-3694635 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-36946352013-06-27 pyGenClean: efficient tool for genetic data clean up before association testing Lemieux Perreault, Louis-Philippe Provost, Sylvie Legault, Marc-André Barhdadi, Amina Dubé, Marie-Pierre Bioinformatics Applications Notes Summary: Genetic association studies making use of high-throughput genotyping arrays need to process large amounts of data in the order of millions of markers per experiment. The first step of any analysis with genotyping arrays is typically the conduct of a thorough data clean up and quality control to remove poor quality genotypes and generate metrics to inform and select individuals for downstream statistical analysis. We have developed pyGenClean, a bioinformatics tool to facilitate and standardize the genetic data clean up pipeline with genotyping array data. In conjunction with a source batch-queuing system, the tool minimizes data manipulation errors, accelerates the completion of the data clean up process and provides informative plots and metrics to guide decision making for statistical analysis. Availability and implementation: pyGenClean is an open source Python 2.7 software and is freely available, along with documentation and examples, from http://www.statgen.org. Contact: louis-philippe.lemieux.perreault@umontreal.ca or marie-pierre.dube@statgen.org Oxford University Press 2013-07-01 2013-05-06 /pmc/articles/PMC3694635/ /pubmed/23652425 http://dx.doi.org/10.1093/bioinformatics/btt261 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Notes Lemieux Perreault, Louis-Philippe Provost, Sylvie Legault, Marc-André Barhdadi, Amina Dubé, Marie-Pierre pyGenClean: efficient tool for genetic data clean up before association testing |
title | pyGenClean: efficient tool for genetic data clean up before association testing |
title_full | pyGenClean: efficient tool for genetic data clean up before association testing |
title_fullStr | pyGenClean: efficient tool for genetic data clean up before association testing |
title_full_unstemmed | pyGenClean: efficient tool for genetic data clean up before association testing |
title_short | pyGenClean: efficient tool for genetic data clean up before association testing |
title_sort | pygenclean: efficient tool for genetic data clean up before association testing |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3694635/ https://www.ncbi.nlm.nih.gov/pubmed/23652425 http://dx.doi.org/10.1093/bioinformatics/btt261 |
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