Cantarella Simona, Di Nisio Elena, Carnevali Davide, Dieci Giorgio, Montanini Barbara
Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy.
Department of Biology and Biotechnology 'Charles Darwin', La Sapienza University of Rome, 00185 Rome, Italy.
Emerg Top Life Sci. 2019 Aug 16;3(4):343-355. doi: 10.1042/ETLS20190004.
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.
在过去二十年中，由于高通量RNA测序策略的发展以及人们越来越意识到新发现的非编码RNA类别参与复杂调控网络，我们见证了非编码RNA研究令人瞩目的蓬勃发展。伴随着探索以前未知的基因调控层面的可能性所带来的兴奋，这些进展促使人们认识到需要有共享的数据收集和分析标准，以及新颖的综合观点和工具，以便从大量分子信息中解读出生物学意义。在过去几年里，为满足这一需求所做的努力主要集中在涉及非编码RNA作为蛋白质编码mRNA的直接或间接调节因子的调控相互作用上。这些努力促成了新计算工具的开发，能够利用众多不同非编码RNA数据集中传播的信息来解读生理和病理细胞反应下的转录组变化。虽然实验验证对于确定关键的RNA调控相互作用仍然至关重要，但事实证明，将非编码RNA大数据与系统的文献挖掘相结合，对于识别癌症和其他疾病中的潜在新参与者、生物标志物和治疗靶点具有极高价值。
