Recent advances in computational epigenetics

Abstract:

Over the last two decades, the importance of epigenetics for interpretation of diverse genetic and genomic data has become increasingly clear. The need for study of indirect (non-gene) factors determining gene characteristic behavior and organism function, together with analysis of outcomes which are nondeterministic, is now well recognized. Given the increasing availability of large-scale datasets, analysis has inevitably become richer, but also more complex, and the formation of structured hypotheses, together with questions designed to refine experiment, relies heavily on computational tools. In particular, the effort to explore the whole genomic–epigenomic landscape has motivated an interdisciplinary approach towards large-scale multivariable and combinatorial analysis as well as novel database developments. Exploration of heritable changes in phenotype relies not only on newer sophisticated sequencing methods but also on legacy data, revisited for their contribution to understanding of transcriptional regulation and disease. The challenges presented are nontrivial, not least in terms of interpretation across multiple scales from cell to organism, but the field is advancing rapidly. With an early initial focus on cancers, both in development of models and database provision, work is emerging on brain function and neural pathways, while newer targets again are the behavioral sciences, in which interest is now burgeoning. In the following article, key developments and advances are summarized and current methods and tools reviewed.

Keywords::

• epigenetics
• DNA methylation
• histone modifications
• computational modeling
• data analysis
• advances
• sequencing
• databases

Disclosure

The authors report no conflicts of interest in this work.