Allmer Jens, Yousef Malik
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J Integr Bioinform. 2016 Dec 1;13(5):1-2. doi: 10.1515/jib-2016-302.
Editorial The term MicroRNA or its contraction miRNA currently appears in 21,215 titles of abstracts, published between 1997 and now, available on Pubmed (2016-21-22:12:59 EET). 4,108 of these were published in 2016 alone which signifies the importance of miRNA-related research. MicroRNAs can be detected experimentally using various techniques like directional cloning of endogenous small RNAs but they are time consuming [1]. Additionally, it is necessary for the miRNA and its mRNA target(s) to be co-expressed to infer a functional relationship which is difficult, if not impossible, to achieve [2]. Since experimental approaches are facing such difficulties, they have been complemented by computational approaches [3] thereby defining the field of computational miRNomics. Due to the rapid development in the discipline, it is important to assess the state-of-the-art. In this special issue, several areas of the field are investigated ranging from pre-miRNA detection via machine learning to application of differential expression analysis in plants. First, Saçar Demirci et al. discuss an approach to virus pre-miRNA detection using machine learning [4]. Such approaches are based on parameterization of miRNAs and Yousef et al. discuss how to select among such features [5]. A different computational perspective is provided by Kotipalli et al. who model the kinetics of miRNA genesis and targeting [6]. To fuel more refined future models for genesis and targeting, it is important to establish miRNA and target expression under varying conditions. Zhang et al. [7] and Kanke et al. [8] discuss two approaches to quantify miRNAs and other non-coding short RNAs. Diler et al., finally, discuss actual biological implications of differentially expressed miRNAs [9]. This special issue on computational miRNomics, thus, provides a trajectory from detection of pre-miRNAs to biological implications of differentially expressed miRNAs. Additional topics will be covered in the upcoming second volume of the special issue on computational miRNomics.
社论 “微小RNA” 或其缩写形式 “miRNA” 目前出现在1997年至今发表于PubMed（2016年21月22日欧洲东部时间12:59）上的21215篇摘要标题中。其中仅2016年就发表了4108篇，这表明了与miRNA相关研究的重要性。微小RNA可以通过多种实验技术进行检测，如内源性小RNA的定向克隆，但这些技术耗时较长[1]。此外，为了推断功能关系，需要miRNA及其mRNA靶标共同表达，而这即使并非不可能，也是很难实现的[2]。由于实验方法面临这些困难，因此已通过计算方法加以补充[3]，从而定义了计算微小RNA组学领域。鉴于该学科的快速发展，评估其当前的技术水平很重要。在本期特刊中，对该领域的几个方面进行了研究，范围从通过机器学习进行前体miRNA检测到差异表达分析在植物中的应用。首先，萨萨尔·德米尔西等人讨论了一种使用机器学习检测病毒前体miRNA的方法[4]。此类方法基于miRNA的参数化处理，尤瑟夫等人讨论了如何在这些特征中进行选择[5]。科蒂帕利等人提供了一种不同的计算视角，他们对miRNA生成和靶向的动力学进行了建模[6]。为了推动未来更精确的生成和靶向模型的发展，在不同条件下确定miRNA及其靶标的表达很重要。张等人[7]和坎克等人[8]讨论了两种定量miRNA和其他非编码短RNA的方法。最后，迪勒等人讨论了差异表达miRNA的实际生物学意义[9]。因此，本期关于计算微小RNA组学的特刊提供了一条从前体miRNA检测到差异表达miRNA生物学意义的轨迹。计算微小RNA组学特刊的第二卷将涵盖更多主题。
