Interpreting and integrating big data in non-coding RNA research

In the last two decades, we have witnessed an impressive crescendo of non-coding RNA studies, due to both the development of high-throughput RNA-sequencing strategies and an ever-increasing awareness of the involvement of newly discovered ncRNA classes in complex regulatory networks. Together with excitement for the possibility to explore previously unknown layers of gene regulation, these advancements led to the realization of the need for shared criteria of data collection and analysis and for novel integrative perspectives and tools aimed at making biological sense of very large bodies of molecular information. In the last few years, efforts to respond to this need have been devoted mainly to the regulatory interactions involving ncRNAs as direct or indirect regulators of protein-coding mRNAs. Such efforts resulted in the development of new computational tools, allowing the exploitation of the information spread in numerous different ncRNA data sets to interpret transcriptome changes under physiological and pathological cell responses. While experimental validation remains essential to identify key RNA regulatory interactions, the integration of ncRNA big data, in combination with systematic literature mining, is proving to be invaluable in identifying potential new players, biomarkers and therapeutic targets in cancer and other diseases.