EpiGeNet: A Graph Database of Interdependencies Between Genetic and Epigenetic Events in Colorectal Cancer

Abstract

The development of colorectal cancer (CRC)-the third most common cancer type-has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.

Recent advances in computational epigenetics

“…[116][117][118] Pooling summary-level genomewide and epigenome-wide studies may provide powerful new insights, 119 with combined query criteria highlighting multiple levels of control, which can apply to even a single change, as noted. 38 Furthermore, the previous focus of the epi-informatic approach, on DNA methylation and histone modification and the patterns that apply in various disease manifestations, has now extended to the integration of data within a scaffold network such as that for protein interaction, which specifies correlation between methylation and gene expression. Abnormal epigenetic marks may appear in cells of different types, with increased phenotypic plasticity associated with these anomalies and crucially linked to network properties, which can offer insight for diagnostics and therapy.…”

“…[34][35][36] Moreover, the reversible nature and faster dynamics of epigenetic changes are of major interest in the targeting of intervention, providing key motivation for pharmaceutical development over the last decade. 37,38 The need, therefore, to understand epigenetic changes and their influence on disease has stimulated development of numerous computational approaches and tools, for application to data generation, mapping and management, as well as analysis and therapy. While the Human Genome Project (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) focused on sequencing all genes in human DNA (~20,000, with some three billion base-pairs), a similar largescale project, the Epigenomics Road Map (2008-date), is exploring specific patterns of epigenetic modifications, with the principal aim of creation of a map of the epigenome for multiple tissue types and cancers.…”

“…38,73,74 Of these, the Krembil Family Epigenetics Laboratory captures methylation data on human chromosomes 21 and 22 as well as information on male germ cells and DNA methylation in twins, while MethyLogiX DNA methylation database has similar coverage, targeted to late-onset Alzheimer's disease. In addition, a number of epigenetic databases have been developed for furthering investigation of different disease types.…”

“…In consequence, a novel graph database approach, which supports both integration and query speed-up, but also has wide-ranging context, has been proposed. 38 Nodes and edges in the graph database, respectively, represent concepts and associations, with the framework readily adaptable to highly interconnected data. Advantages include the use of graphical search algorithms and next-neighbor node-linked traversal searches, which give additional flexibility compared to the more conventional relational databases.…”

“…Examples of interactive query tools for integration and management of different medical and biological data types are given, together with Neo4j linkages for data management and analysis (Figure 1). 38 Neo4j-based frameworks have also been used to assess performance of in silico models of biological systems, notably computational and mathematical models of cancer 92,93 (and the BioModels database). 94 Moreover, FlockDB supports application to reset rather than traversal searches (based on adjacency graph storage), providing a platform, similar to that of, for example, GraphLab, MapReduce and Scope (amongst others), for scalable execution.…”