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ExposomeX: Integrative Exposomic Platform Expediates Discovery of "Exposure-Biology-Disease" Nexus

Wang, B.; Lan, C.; Zhang, G.; Ren, M.; Feng, Y.; Gao, N.; Lin, W.; Jiangtulu, B.; Liu, Z.; Shao, X.; Su, S.; Wang, Y.; Wang, H.; Zhao, F.; Peng, B.; Ji, X.; Chen, X.; Nian, M.; Fang, M.

2022-11-25 bioinformatics
10.1101/2022.11.23.517586 bioRxiv
Show abstract

Exposome has become the hotspot of next-generation health studies. To date, there is no available effective platform to standardize the analysis of exposomic data. In this study, we aim to propose one new framework of exposomic analysis and build up one novel integrated platform "ExposomeX" to expediate the discovery of the "Exposure-Biology-Disease" nexus. We have developed 13 standardized modules to accomplish six major functions including statistical learning (E-STAT), exposome database search (E-DB), mass spectrometry data processing (E-MS), meta-analysis (E-META), biological link via pathway integration and protein-protein interaction (E-BIO) and data visualization (E-VIZ). Using ExposomeX, we can effectively analyze the multiple-dimensional exposomics data and investigate the "Exposure-Biology-Disease" nexus by exploring mediation and interaction effects, understanding statistical and biological mechanisms, strengthening prediction performance, and automatically conducting meta-analysis based on well-established literature databases. The performance of ExposomeX has been well validated by re-analyzing two previous multi-omics studies. Additionally, ExposomeX can efficiently help discover new associations, as well as relevant in-depth biological pathways via protein-protein interaction and gene ontology network analysis. In sum, we have proposed a novel framework for standardized exposomic analysis, which can be accessed using both R and online interactive platform (http://www.exposomex.cn/).

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