ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.
Universitat Pompeu Fabra (UPF), Barcelona, Spain.
Bioinformatics. 2019 Dec 15;35(24):5344-5345. doi: 10.1093/bioinformatics/btz526.
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.
rexposome project is available at https://isglobal-brge.github.io/rexposome/.
Supplementary data are available at Bioinformatics online.
基因组学极大地提高了我们对某些人类疾病分子起源的理解。尽管如此，我们的健康也受到一生中经历的各种暴露（称为“暴露组”）的累积影响。高维暴露组的研究为研究环境对疾病病因的贡献提供了新的范例。然而，目前缺乏用于管理、可视化和分析暴露组的生物信息学工具。分析数据应包括与健康结果的关联以及与组学层面的整合。我们提供了一个名为 rexposome project 的通用框架，该框架是在 R/Bioconductor 架构中开发的，包括面向对象的类和方法，用于在疾病关联研究中利用高维暴露组数据，包括与各种高通量数据类型的整合。通过分析一个包含暴露组数据、三个与呼吸疾病相关的健康结果以及与转录组和甲基组整合的真实数据集，说明了该软件包的实用性。
rexposome project 可在 https://isglobal-brge.github.io/rexposome/ 获得。
补充数据可在生物信息学在线获得。
