Research Topic

Computational epigenomics: challenges and opportunities

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Epigenetic marks, such as methylation of genomic DNA and histone post-translational modifications, are inheritable layers that act on the top of the genetic information and contribute to shaping the regulatory and transcriptional potential of the genome in uni- and multi-cellular organisms.
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Epigenetic marks, such as methylation of genomic DNA and histone post-translational modifications, are inheritable layers that act on the top of the genetic information and contribute to shaping the regulatory and transcriptional potential of the genome in uni- and multi-cellular organisms.

The availability of affordable and effective high-throughput sequencing technologies nowadays offer the possibility of charting entire epigenomes under multiple conditions, while generating data with unprecedented coverage, quality and resolution. At the same time, complementary methods are currently developed for targeted intervention, including profiling and precise manipulation of epigenetic marks. The combination of these powerful approaches is greatly improving our comprehension of epigenetic forces and our ability in reversing aberrant epigenetic patterns within clinical applications.

To this purpose, the development of statistical and computational methods tailored for epigenomics data is emerging as a field that is critical to cope with the analysis of these highly dimensional and complex datasets.

On one hand, the high number of epigenetic marks that can be interrogated and the numerous methods for profiling them result in a plethora of different data types, each associated with specific features and biases, thus requiring an effort in developing bespoke statistical and computational methods. On the other hand, the combinatorial nature of these marks and their crosstalk with other regulatory layers challenge computational scientists to advance our comprehension in these intricate epigenetic networks. Analytical methods have to be developed that are able to take advantage of the growing body of available data; collaborations with experimental groups have to be established for the design of smart perturbation experiments. Eventually, computational approaches are expected to shed light on the dynamic nature of epigenetic marks over differentiation, aging and disease conditions, and their responsiveness to environmental and nutritional cues.

We encourage the submission of papers presenting and discussing the current challenges in the analysis and integration of epigenomics data, and highlighting critical unresolved issues and future directions. Topics of interest include, but are not limited to computational methods for the analysis of:

• chromatin states and their variation over time and biological conditions
• cross-talk among epigenetics layers
• association and causality with other regulatory layers
• role of epigenetic readers, writers and regulators
• connection of epigenetic layers with transcriptional programs
• statistical methods for specific data types and/or experimental methodologies
• comparisons of epigenomic profiling platforms
• epigenetic patterns during differentiation and aging
• contribution of environmental and nutritional cues on the epigenome
• evolution of epigenetic layers
• association of epigenetic patterns to the genetic information
• data integration and visualization of epigenomics data
• identification of epigenetic patterns altered in disease.

Research papers, reviews and short communications on all topics related to the above issues are welcomed.


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