Rhee S, Chae H, Kim S
Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
School of Informatics and Computing, Computer Science Department, Indiana University, Bloomington, IN, USA.
Methods. 2015 Jul 15;83:80-7. doi: 10.1016/j.ymeth.2015.04.003. Epub 2015 Apr 8.
miRNA is known to regulate up to several hundreds coding genes, thus the integrated analysis of miRNA and mRNA expression data is an important problem. Unfortunately, the integrated analysis is challenging since it needs to consider expression data of two different types, miRNA and mRNA, and target relationship between miRNA and mRNA is not clear, especially when microarray data is used. Fortunately, due to the low sequencing cost, small RNA and RNA sequencing are routinely processed and we may be able to infer regulation relationships between miRNAs and mRNAs more accurately by using sequencing data. However, no method is developed specifically for sequencing data. Thus we developed PlantMirnaT, a new miRNA-mRNA integrated analysis system. To fully leverage the power of sequencing data, three major features are developed and implemented in PlantMirnaT. First, we implemented a plant-specific short read mapping tool based on recent discoveries on miRNA target relationship in plant. Second, we designed and implemented an algorithm considering miRNA targets in the full intragenic region, not just 3' UTR. Lastly but most importantly, our algorithm is designed to consider quantity of miRNA expression and its distribution on target mRNAs. The new algorithm was used to characterize rice under drought condition using our proprietary data. Our algorithm successfully discovered that two miRNAs, miRNA1425-5p, miRNA 398b, that are involved in suppression of glucose pathway in a naturally drought resistant rice, Vandana. The system can be downloaded at https://sites.google.com/site/biohealthinformaticslab/resources.
已知miRNA可调控多达数百个编码基因，因此对miRNA和mRNA表达数据进行综合分析是一个重要问题。不幸的是，综合分析具有挑战性，因为它需要考虑两种不同类型的表达数据，即miRNA和mRNA，而且miRNA与mRNA之间的靶标关系并不明确，尤其是在使用微阵列数据时。幸运的是，由于测序成本较低，小RNA和RNA测序已成为常规操作，我们或许能够通过使用测序数据更准确地推断miRNA与mRNA之间的调控关系。然而，目前尚未开发出专门针对测序数据的方法。因此，我们开发了PlantMirnaT，一种新的miRNA-mRNA综合分析系统。为了充分利用测序数据的优势，PlantMirnaT开发并实现了三个主要特性。首先，我们基于植物中miRNA靶标关系的最新发现，实现了一种植物特异性短读映射工具。其次，我们设计并实现了一种算法，该算法考虑的是整个基因内区域而非仅3'UTR中的miRNA靶标。最后但同样重要的是，我们的算法旨在考虑miRNA表达量及其在靶标mRNA上的分布。我们使用自有数据，运用新算法对干旱条件下的水稻进行了特征分析。我们的算法成功发现，在天然抗旱水稻Vandana中，有两种miRNA，即miRNA1425 - 5p和miRNA 398b，参与了对葡萄糖途径的抑制。该系统可从https://sites.google.com/site/biohealthinformaticslab/resources下载。
