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High-Throughput 2018, 7(2), 17; https://doi.org/10.3390/ht7020017

A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis

Data Analytics Research Center, Department of Medical and Surgical Sciences, University “Magna Græcia” of Catanzaro, Viale Europa, 88100 Catanzaro, Italy
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Received: 31 March 2018 / Revised: 21 May 2018 / Accepted: 7 June 2018 / Published: 14 June 2018
(This article belongs to the Special Issue Applications of Microarrays in Diagnostics)
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Abstract

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. View Full-Text
Keywords: single nucleotide polymorphisms; multiple analysis pipeline; pharmacogenomics; overall survival curves; data mining; statistical analysis single nucleotide polymorphisms; multiple analysis pipeline; pharmacogenomics; overall survival curves; data mining; statistical analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Agapito, G.; Guzzi, P.H.; Cannataro, M. A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis. High-Throughput 2018, 7, 17.

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