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Comprehensive study of the exposome and omic data using rexposome Bioconductor Packages.

Authors :
Hernandez-Ferrer C
Wellenius GA
Tamayo I
BasagaƱa X
Sunyer J
Vrijheid M
Gonzalez JR
Source :
Bioinformatics (Oxford, England) [Bioinformatics] 2019 Dec 15; Vol. 35 (24), pp. 5344-5345.
Publication Year :
2019

Abstract

Summary: Genomics has dramatically improved our understanding of the molecular origins of certain human diseases. Nonetheless, our health is also influenced by the cumulative impact of exposures experienced across the life course (termed 'exposome'). The study of the high-dimensional exposome offers a new paradigm for investigating environmental contributions to disease etiology. However, there is a lack of bioinformatics tools for managing, visualizing and analyzing the exposome. The analysis data should include both association with health outcomes and integration with omic layers. We provide a generic framework called rexposome project, developed in the R/Bioconductor architecture that includes object-oriented classes and methods to leverage high-dimensional exposome data in disease association studies including its integration with a variety of high-throughput data types. The usefulness of the package is illustrated by analyzing a real dataset including exposome data, three health outcomes related to respiratory diseases and its integration with the transcriptome and methylome.<br />Availability and Implementation: rexposome project is available at https://isglobal-brge.github.io/rexposome/.<br />Supplementary Information: Supplementary data are available at Bioinformatics online.<br /> (© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Subjects

Subjects :
Genomics
Humans
Exposome
Software

Details

Language :
English
ISSN :
1367-4811
Volume :
35
Issue :
24
Database :
MEDLINE
Journal :
Bioinformatics (Oxford, England)
Publication Type :
Academic Journal
Accession number :
31243429
Full Text :
https://doi.org/10.1093/bioinformatics/btz526