Computational and Experimental Methods to Decipher the Epigenetic Code

A multi-layered set of epigenetic marks, including post-translational modifications of histones and methylation of DNA, is finely tuned to define the epigenetic state of chromatin in any given cell type under specific conditions. Recently, the knowledge about the combinations of epigenetic marks occurring in the genome of different cell types under various conditions is rapidly increasing. Computational methods were developed for the identification of these states, unraveling the combinatorial nature of epigenetic marks and their association to genomic functional elements and transcriptional states. Nevertheless, the precise rules defining the interplay between all these marks remain poorly characterized. In this perspective we review the current state of this research field, illustrating the power and the limitations of current approaches. Finally, we sketch future avenues of research illustrating how the adoption of specific experimental designs coupled with available experimental approaches could be critical for a significant progress in this area

Integrated Systems for NGS Data Management and Analysis: Open Issues and Available Solutions

Next-generation sequencing (NGS) technologies have deeply changed our understanding of cellular processes by delivering an astonishing amount of data at affordable prices; nowadays, many biology laboratories have already accumulated a large number of sequenced samples. However, managing and analyzing these data poses new challenges, which may easily be underestimated by research groups devoid of IT and quantitative skills. In this perspective, we identify five issues that should be carefully addressed by research groups approaching NGS technologies. In particular, the five key issues to be considered concern: 1) adopting a laboratory management system (LIMS) and safeguard the resulting raw data structure in downstream analyses; 2) monitoring the flow of the data and standardizing input and output directories and file names, even when multiple analysis protocols are used on the same data; 3) ensuring complete traceability of the analysis performed; 4) enabling non-experienced users to run analyses through a graphical user interface (GUI) acting as a front-end for the pipelines; 5) relying on standard metadata to annotate the datasets, and when possible using controlled vocabularies, ideally derived from biomedical ontologies. Finally, we discuss the currently available tools in the light of these issues, and we introduce HTS-flow, a new workflow management system (WMS) conceived to address the concerns we raised. HTS-flow is able to retrieve information from a LIMS database, manages data analyses through a simple GUI, outputs data in standard locations and allows the complete traceability of datasets, accompanying metadata and analysis scripts