A Systemic Analysis of Transcriptomic and Epigenomic Data To Reveal Regulation Patterns for Complex Disease

Abstract

Integrating diverse genomics data can provide a global view of the complex biological processes related to the human complex diseases. Although substantial efforts have been made to integrate different omics data, there are at least three challenges for multi-omics integration methods: (i) How to simultaneously consider the effects of various genomic factors, since these factors jointly influence the phenotypes; (ii) How to effectively incorporate the information from publicly accessible databases and omics datasets to fully capture the interactions among (epi)genomic factors from diverse omics data; and (iii) Until present, the combination of more than two omics datasets has been poorly explored. Current integration approaches are not sufficient to address all of these challenges together. We proposed a novel integrative analysis framework by incorporating sparse model, multivariate analysis, Gaussian graphical model, and network analysis to address these three challenges simultaneously. Based on this strategy, we performed a systemic analysis for glioblastoma multiforme (GBM) integrating genome-wide gene expression, DNA methylation, and miRNA expression data. We identified three regulatory modules of genomic factors associated with GBM survival time and revealed a global regulatory pattern for GBM by combining the three modules, with respect to the common regulatory factors. Our method can not only identify disease-associated dysregulated genomic factors from different omics, but more importantly, it can incorporate the information from publicly accessible databases and omics datasets to infer a comprehensive interaction map of all these dysregulated genomic factors. Our work represents an innovative approach to enhance our understanding of molecular genomic mechanisms underlying human complex diseases.

DNA methylation profiling identifies potentially significant epigenetically‑regulated genes in glioblastoma multiforme

“…Bioinformatics tools and algorithms assist the processing and analysis of high-throughput DNA methylation data (13,14). For example, a previous study used a joint analysis of DNA methylation and gene expression data of GBM to demonstrate that changes in DNA methylation can be associated with survival outcome (15).…”

Food allergy and omics

“…In addition, the integration of clinical (questionnaires, symptoms, family history, and demographic) and experimental (laboratory investigations, biochemical assays, and tissue pathology) data sets poses a challenge for simultaneous consideration of effects from all factors, as well as appropriate quality assessment, verification, filtering, and scaling of data. 97 Accurately modeling the interactions between different layers of a biological system is a major concern for data integrative ''systems biology'' strategies. The corresponding relationships between different layers, such as DNAm and gene expression, microRNA expression and protein coding, and others, should be considered not only independently but also together during an integrative process.…”

Integrative analysis of multi-omics data to detect the underlying molecular mechanisms for obesity in vivo in humans

“…With the advance of emerging high-throughput sequencing technology such as whole genome sequencing (WGS), RNA sequencing (RNA-Seq), reduced-representation bisulfite sequencing (RRBS) and liquid chromatography–mass spectrometry (LC–MS), multi-omics data including genomics, transcriptomics, epigenomics and metabolomics are rapidly generated and accumulated [ 3 ]. As a result, more and more researchers are currently working on the integration of comprehensive multi-omics data to discovery new and meaningful biological knowledge [ 4 , 5 ], but those studies focused on obesity are rare.…”