Abstract
Computational epigenomics: challenges and opportunities.
Highlights
Specialty section: This article was submitted to Bioinformatics and Computational Biology, a section of the journal
The contribution of scientists with computational skills is considered an essential component of research institutes investing in this research field (Bock and Lengauer, 2008). In this Research Topic, we collected a number of contributions in the field of computational epigenomics covering three main research areas: (i) computational analyses tackling important issues closely related to the experimental method used to generate epigenetic data (Flensburg et al, 2014; Ji et al, 2014; Mensaert et al, 2015), (ii) computational approaches useful to overcome pitfalls associated to the analysis of a given epigenetic layer (Barozzi et al, 2014; Cairns et al, 2014; Robinson et al, 2014), and (iii) studies on the integration of multiple epigenetic layers
Computational tools developed for the analysis of specific epigenetic data types, including DNA methylation and ChIP-seq of histone post-translational modifications, have to deal with the biases originated directly from the experimental methodology
Summary
Specialty section: This article was submitted to Bioinformatics and Computational Biology, a section of the journal. In this Research Topic, we collected a number of contributions in the field of computational epigenomics covering three main research areas: (i) computational analyses tackling important issues closely related to the experimental method used to generate epigenetic data (Flensburg et al, 2014; Ji et al, 2014; Mensaert et al, 2015), (ii) computational approaches useful to overcome pitfalls associated to the analysis of a given epigenetic layer (Barozzi et al, 2014; Cairns et al, 2014; Robinson et al, 2014), and (iii) studies on the integration of multiple epigenetic layers (de Pretis and Pelizzola, 2014; Fejes et al, 2014; Osella et al, 2014).
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