Author(s): Devin C Koestler [*] aff1 , Meaghan Jones aff2 , Michael Kobor aff2
DNA methylation; epigenetic silencing; integrative genomics; protein-protein interaction (PPI) networks
In this commentary we highlight a recently proposed statistical methodology called Functional Epigenetic Modules for the integrative analysis of DNA methylation and gene expression data. We begin by providing a high-level overview of this technique, along with a discussion of the perceived strengths and limitations of this methodology. This serves as the jumping off point for dialogue on some of the frequently encountered challenges in the analysis of data from integrative 'omic studies; particularly those involving the collection of both DNA methylation and gene expression data. We shed light on these challenges and offer our perspective on potential solutions.
Integrating 'omics data
According to PubMed, published research studies involving integrated analysis of multiple 'omic data types have witnessed a nearly 10-fold increase over the past decade. The reasons for the surge in integrative genomics studies are both biological - fueled by an increasing understanding that the development and progression of complex diseases are due to the confluence of alterations in the genome, epigenome, transcriptome, proteome, etc. (and their complex interactions) - and logistical - while large-scale integrative 'omic studies were once economically infeasible, the declining cost of high-throughput technologies for interrogating the 'ome have opened the door for such studies to become a reality. Leveraging multiple 'omic data types for improving our understanding complex disease cannot be better exemplified than through the efforts of The Cancer Genome Atlas (TCGA), which since 2005 has sought to comprehensively catalog the entire spectrum of genomic and proteomic changes involved in human cancers [1 ]. However, procurement of multiple 'omic data types is only the beginning of a continuum that involves the transformation of such data into information that improves our capacity to better diagnose, treat and prevent complex human diseases. While the assimilation of various types of 'omic data provides the raw material for improved understanding, the 'more data' philosophy that is at the core of integrative genomics studies has the tendency to detract from the significance of statistical/bioinformatics tools that facilitate the transformation of this raw material into new knowledge. Hence, with the rapid increase in studies involving the collection of multiple 'omic data types comes the companion need for novel and innovative statistical/bioinformatics tools for integrated analyses. Thus, left with the question of 'more data' or 'better methods', the truth is that both are necessary elements in our ability to better understand the molecular basis of complex disease and, ultimately, improve human health. Here however, our objective is to emphasize the importance and offer our perspective on what is needed moving forward to develop 'better methods' for integrative genomics studies.
In this commentary we focus our attention on a recently published article by Jiao et al ., which describes a systems-level framework for the integrative analysis of DNA methylation and gene expression data [2 ]. Our choice to focus on this specific technique as the basis of this commentary is...