Nazarov Petr V, Kreis Stephanie
Multiomics Data Science Research Group, Department of Oncology & Quantitative Biology Unit, Luxembourg Institute of Health (LIH), Strassen L-1445, Luxembourg.
Signal Transduction Group, Department of Life Sciences and Medicine, University of Luxembourg, Belvaux L-4367, Luxembourg.
Comput Struct Biotechnol J. 2021 Jan 26;19:1154-1162. doi: 10.1016/j.csbj.2021.01.029. eCollection 2021.
Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.
诸如RNA测序（RNAseq）等先进的测序技术提供了生成大量数据的方法，包括编码RNA（mRNA）以及miRNA、lncRNA、piRNA等非编码RNA和许多其他RNA种类的全转录组表达水平。仅代表一种RNA种类的数据集分析已经很成熟，并且有各种工具和流程可用。然而，要更系统地了解不同参与者如何共同调节一个基因或一组基因的表达，需要一种更复杂的数据分析方法。为了全面理解复杂的转录网络，需要整合代表不同RNA种类的数据集。在这篇综述中，我们将重点关注作为关键转录后调节因子的miRNA，总结当前用于miRNA：靶基因预测的计算方法以及解决全面准确剖析miRNA组-靶基因组相互作用问题的新数据驱动方法。
