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.
Highlights
The continuous improvements in experimental technologies allow spreading the use of genotyping analysis in several biological, medical and clinical areas [1,2]
To help researchers to perform survival, data mining and statistical analysis in an easy and fast way, we developed and implemented microPipe. microPipe is wrote by using Java 8.0 language
MicroPipe is a fast and efficient tool designed to assist the users in statistical analysis, in Kaplan–Meier curve computing/visualization, and in association rules mining from Drug Metabolism Enzymes and Transporters (DMET) Single Nucleotide Polymorphism (SNP) datasets. microPipe comes with an advanced computational engine, which allows to perform data analysis in parallel by taking advantage of the available hardware. microPipe’s engine is designed to exploit the multi-CPUs multi-Cores architectures to speed-up the computational analysis. microPipe automatically maps each phase of the analysis process to a single Core/CPU, and if the available number of available Cores/CPUs
Summary
The continuous improvements in experimental technologies allow spreading the use of genotyping analysis in several biological, medical and clinical areas [1,2]. We developed some tools to perform statistical and data mining analysis of DMET datasets. DMET-Analyzer [8] is a software tool able to perform statistical analysis of DMET SNP datasets to discriminate single SNPs involved in adverse drug reaction. OS-Analyzer (OSA) is a software tool for the analysis of DMET datasets annotated with clinical data such as overall survival (OS), progression-free survival (PFS), to graphically discriminate which SNP is related to a good or bad overall survival. To speed up and simplify the analysis of DMET SNP datasets, we present microPipe a novel tool to perform on the same input dataset statistical, survival and data mining analysis, integrating the functions of DMET-Analyzer, DMET-Miner, and OS-Analyzer. The main advantage of microPipe is the possibility to perform data mining, statistical and temporal analysis on the same dataset in a single execution.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
