Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems

BMC Genomics

Methodology Article

Sean Geoffrey Byars¹  Michael Inouye²  Bin Zhang³  Zeyneb Kurt⁴  Yuqi Zhao⁴  Le Shu⁴  Xia Yang⁵  Ville-Petteri Mäkinen⁶  Aldons J. Lusis⁷  Matteo Pellegrini⁸  Luz D. Orozco⁸  Samuli Ripatti⁹  Taru Tukiainen⁹  Johannes Kettunen⁹ 

[1] Center for Systems Genomics, University of Melbourne, Melbourne, Australia;School of BioSciences, University of Melbourne, Melbourne, Australia;Center for Systems Genomics, University of Melbourne, Melbourne, Australia;School of BioSciences, University of Melbourne, Melbourne, Australia;Department of Pathology, University of Melbourne, Melbourne, Australia;Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA;Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA;Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA;Insitute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA, USA;Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA;South Australian Health and Medical Research Institute, Adelaide, Australia;School of Biological Sciences, University of Adelaide, Adelaide, Australia;Computational Medicine, Faculty of Medicine, University of Oulu and Biocenter Oulu, Oulu, Finland;Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA;Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA;Institute for Molecular Medicine, Helsinki, Finland;

关键词: Mergeomics;    Integrative genomics;    Multidimensional data integration;    Functional genomics;    Gene networks;    Key drivers;    Cholesterol;    Blood glucose;   

DOI  :  10.1186/s12864-016-3198-9

 received in 2016-08-25, accepted in 2016-10-25,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundComplex diseases are characterized by multiple subtle perturbations to biological processes. New omics platforms can detect these perturbations, but translating the diverse molecular and statistical information into testable mechanistic hypotheses is challenging. Therefore, we set out to create a public tool that integrates these data across multiple datasets, platforms, study designs and species in order to detect the most promising targets for further mechanistic studies.ResultsWe developed Mergeomics, a computational pipeline consisting of independent modules that 1) leverage multi-omics association data to identify biological processes that are perturbed in disease, and 2) overlay the disease-associated processes onto molecular interaction networks to pinpoint hubs as potential key regulators. Unlike existing tools that are mostly dedicated to specific data type or settings, the Mergeomics pipeline accepts and integrates datasets across platforms, data types and species. We optimized and evaluated the performance of Mergeomics using simulation and multiple independent datasets, and benchmarked the results against alternative methods. We also demonstrate the versatility of Mergeomics in two case studies that include genome-wide, epigenome-wide and transcriptome-wide datasets from human and mouse studies of total cholesterol and fasting glucose. In both cases, the Mergeomics pipeline provided statistical and contextual evidence to prioritize further investigations in the wet lab. The software implementation of Mergeomics is freely available as a Bioconductor R package.ConclusionMergeomics is a flexible and robust computational pipeline for multidimensional data integration. It outperforms existing tools, and is easily applicable to datasets from different studies, species and omics data types for the study of complex traits.

【 授权许可】

CC BY   
© The Author(s). 2016

【 图 表 】

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