Fiscon Giulia, Conte Federica, Farina Lorenzo, Pellegrini Marco, Russo Francesco, Paci Paola
Institute for Systems Analysis and Computer Science Antonio Ruberti, National Research Council, Rome, Italy.
SysBio Centre for Systems Biology, Milan, Italy.
Methods Mol Biol. 2019;1970:169-181. doi: 10.1007/978-1-4939-9207-2_10.
MicroRNAs (miRNAs) are small noncoding RNAs (ncRNAs) involved in several biological processes and diseases. MiRNAs regulate gene expression at the posttranscriptional level, mostly downregulating their targets by binding specific regions of transcripts through imperfect sequence complementarity. Prediction of miRNA-binding sites is challenging, and target prediction algorithms are usually based on sequence complementarity. In the last years, it has been shown that by adding miRNA and protein coding gene expression, we are able to build tissue-, cell line-, or disease-specific networks improving our understanding of complex biological scenarios. In this chapter, we present an application of a recently published software named SWIM, that allows to identify key genes in a network of interactions by defining appropriate "roles" of genes according to their local/global positioning in the overall network. Furthermore, we show how the SWIM software can be used to build miRNA-disease networks, by applying the approach to tumor data obtained from The Cancer Genome Atlas (TCGA).
微小RNA(miRNA)是一类参与多种生物学过程和疾病的小型非编码RNA(ncRNA)。miRNA在转录后水平调控基因表达,主要通过与转录本的特定区域不完全互补结合来下调其靶标。miRNA结合位点的预测具有挑战性,并且靶标预测算法通常基于序列互补性。在过去几年中,研究表明通过添加miRNA和蛋白质编码基因表达,我们能够构建组织、细胞系或疾病特异性网络,从而增进我们对复杂生物学情况的理解。在本章中,我们介绍了一个最近发布的名为SWIM的软件的应用,该软件可以根据基因在整个网络中的局部/全局定位定义适当的“角色”,从而识别相互作用网络中的关键基因。此外,我们展示了如何通过将该方法应用于从癌症基因组图谱(TCGA)获得的肿瘤数据,使用SWIM软件构建miRNA-疾病网络。