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A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis

Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual chara...

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Autores principales: Agapito, Giuseppe, Guzzi, Pietro Hiram, Cannataro, Mario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023446/
https://www.ncbi.nlm.nih.gov/pubmed/29904017
http://dx.doi.org/10.3390/ht7020017
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author Agapito, Giuseppe
Guzzi, Pietro Hiram
Cannataro, Mario
author_facet Agapito, Giuseppe
Guzzi, Pietro Hiram
Cannataro, Mario
author_sort Agapito, Giuseppe
collection PubMed
description Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g., high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic softwares, is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based Single Nucleotide Polymorphism (SNP) genotyping data. A use case in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data. microPipe is available under Creative Commons license, and is freely downloadable for academic and not-for-profit institutions.
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spelling pubmed-60234462018-07-03 A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis Agapito, Giuseppe Guzzi, Pietro Hiram Cannataro, Mario High Throughput Article Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g., high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic softwares, is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based Single Nucleotide Polymorphism (SNP) genotyping data. A use case in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data. microPipe is available under Creative Commons license, and is freely downloadable for academic and not-for-profit institutions. MDPI 2018-06-14 /pmc/articles/PMC6023446/ /pubmed/29904017 http://dx.doi.org/10.3390/ht7020017 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Agapito, Giuseppe
Guzzi, Pietro Hiram
Cannataro, Mario
A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis
title A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis
title_full A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis
title_fullStr A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis
title_full_unstemmed A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis
title_short A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis
title_sort parallel software pipeline for dmet microarray genotyping data analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6023446/
https://www.ncbi.nlm.nih.gov/pubmed/29904017
http://dx.doi.org/10.3390/ht7020017
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