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

Abstract

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3198-9) contains supplementary material, which is available to authorized users.

The Genetic Architecture of Diet‐Induced Hepatic Fibrosis in Mice

“…We identified pathways associated with NASH/fibrosis in both species using the MSEA pipeline as described under Materials and Methods. Briefly, this method tests for overrepresentation of disease‐related GWAS signals among the eQTLs mapped to individual pathways compared to random sets of genes . At an FDR of <5%, 60% of the mouse fibrosis causal mechanisms were identified in causal human NASH subnetworks (Fig.…”

“…Comparison between causal pathways associated with human NASH and mouse liver fibrosis. Pathways were identified using the MSEA algorithm . Abbreviations: BCR, B‐cell receptor; HIV, human immunodeficiency virus; MAPK, mitogen‐activated protein kinase; PPAR, peroxisome proliferator‐activated receptor; TGF, transforming growth factor.…”

“…Pathways were identified using the MSEA algorithm. (17,18) Abbreviations: BCR, B-cell receptor; HIV, human immunodeficiency virus; MAPK, mitogen-activated protein kinase; PPAR, peroxisome proliferator-activated receptor; TGF, transforming growth factor. strong candidates.…”

“…Marker set enrichment analysis (MSEA) was employed to identify causal biological pathways of NASH/fibrosis in our mouse model and a human bariatric surgery cohort. (17,18) Genes in each canonical pathway or coexpression module were mapped to single-nucleotide polymorphisms (SNPs) using liver eQTLs and then integrated with GWAS of mouse liver fibrosis or human NASH to capture strong, moderate, and subtle genetic signals. Genotyping of the obese patients was performed using the Multi-Ethnic Genotyping Array.…”

Analysis of Pan-Omics Data in Human Interactome Network (APODHIN)

“…pathfindR [16] finds active sub networks for genes in omics data and perform pathway enrichment analysis. R package Mergeomics [17] provides a pipeline to identify important pathways and key drivers in biological systems.…”