Carles Hernandez-Ferrer1,2,3, Gregory A Wellenius4, Ibon Tamayo1,2,3, Xavier Basagaña1,2,3, Jordi Sunyer1,2,3,5, Martine Vrijheid1,2,3, Juan R Gonzalez1,2,3. 1. ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain. 2. Universitat Pompeu Fabra (UPF), Barcelona, Spain. 3. CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain. 4. Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA. 5. IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain.
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. AVAILABILITY AND IMPLEMENTATION: rexposome project is available at https://isglobal-brge.github.io/rexposome/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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. AVAILABILITY AND IMPLEMENTATION: rexposome project is available at https://isglobal-brge.github.io/rexposome/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Martine Vrijheid; Xavier Basagaña; Juan R Gonzalez; Vincent W V Jaddoe; Genon Jensen; Hector C Keun; Rosemary R C McEachan; Joana Porcel; Valerie Siroux; Morris A Swertz; Cathrine Thomsen; Gunn Marit Aasvang; Sandra Andrušaitytė; Karine Angeli; Demetris Avraam; Ferran Ballester; Paul Burton; Mariona Bustamante; Maribel Casas; Leda Chatzi; Cécile Chevrier; Natacha Cingotti; David Conti; Amélie Crépet; Payam Dadvand; Liesbeth Duijts; Esther van Enckevort; Ana Esplugues; Serena Fossati; Ronan Garlantezec; María Dolores Gómez Roig; Regina Grazuleviciene; Kristine B Gützkow; Mònica Guxens; Sido Haakma; Ellen V S Hessel; Lesley Hoyles; Eleanor Hyde; Jana Klanova; Jacob D van Klaveren; Andreas Kortenkamp; Laurent Le Brusquet; Ivonne Leenen; Aitana Lertxundi; Nerea Lertxundi; Christos Lionis; Sabrina Llop; Maria-Jose Lopez-Espinosa; Sarah Lyon-Caen; Lea Maitre; Dan Mason; Sandrine Mathy; Edurne Mazarico; Tim Nawrot; Mark Nieuwenhuijsen; Rodney Ortiz; Marie Pedersen; Josep Perelló; Míriam Pérez-Cruz; Claire Philippat; Pavel Piler; Costanza Pizzi; Joane Quentin; Lorenzo Richiardi; Adrian Rodriguez; Theano Roumeliotaki; José Manuel Sabin Capote; Leonardo Santiago; Susana Santos; Alexandros P Siskos; Katrine Strandberg-Larsen; Nikos Stratakis; Jordi Sunyer; Arthur Tenenhaus; Marina Vafeiadi; Rebecca C Wilson; John Wright; Tiffany Yang; Remy Slama Journal: Environ Epidemiol Date: 2021-10-01
Authors: Marta Vives-Usano; Carles Hernandez-Ferrer; Léa Maitre; Carlos Ruiz-Arenas; Sandra Andrusaityte; Eva Borràs; Ángel Carracedo; Maribel Casas; Leda Chatzi; Muireann Coen; Xavier Estivill; Juan R González; Regina Grazuleviciene; Kristine B Gutzkow; Hector C Keun; Chung-Ho E Lau; Solène Cadiou; Johanna Lepeule; Dan Mason; Inés Quintela; Oliver Robinson; Eduard Sabidó; Gillian Santorelli; Per E Schwarze; Alexandros P Siskos; Rémy Slama; Marina Vafeiadi; Eulàlia Martí; Martine Vrijheid; Mariona Bustamante Journal: BMC Med Date: 2020-08-19 Impact factor: 8.775