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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Integrating diverse omics data is crucial for understanding complex human diseases.
• Current multi-omics integration methods face challenges in simultaneously considering genomic factors, incorporating external data, and analyzing more than two omics datasets.

Purpose of the Study:

• To propose a novel integrative analysis framework addressing key challenges in multi-omics integration.
• To apply this framework to glioblastoma multiforme (GBM) by integrating gene expression, DNA methylation, and miRNA expression data.

Main Methods:

• Incorporation of sparse model, multivariate analysis, Gaussian graphical model, and network analysis.
• Systemic analysis of GBM using genome-wide gene expression, DNA methylation, and miRNA expression data.
• Identification of regulatory modules and global regulatory patterns.

Main Results:

• Identification of three regulatory modules associated with GBM survival time.
• Revelation of a global regulatory pattern for GBM by combining identified modules.
• Development of a method to infer a comprehensive interaction map of dysregulated genomic factors.

Conclusions:

• The proposed framework effectively addresses challenges in multi-omics integration.
• The study enhances understanding of molecular genomic mechanisms underlying complex diseases like GBM.
• This approach facilitates the identification of disease-associated genomic factors and their interactions.