Zhang Lu, Qin Cheng, Mei Junpu, Chen Xiaocui, Wu Zhiming, Luo Xirong, Cheng Jiaowen, Tang Xiangqun, Hu Kailin, Li Shuai C
Department of Computer Science, City University of Hong KongHong Kong, China.
Pepper Institute, Zunyi Academy of Agricultural SciencesZunyi, China.
Front Plant Sci. 2017 Apr 10;8:495. doi: 10.3389/fpls.2017.00495. eCollection 2017.
The microRNA (miRNA) can regulate the transcripts that are involved in eukaryotic cell proliferation, differentiation, and metabolism. Especially for plants, our understanding of miRNA targets, is still limited. Early attempts of prediction on sequence alignments have been plagued by enormous false positives. It is helpful to improve target prediction specificity by incorporating the other data sources such as the dependency between miRNA and transcript expression or even cleaved transcripts by miRNA regulations, which are referred to as trans-omics data. In this paper, we developed MiRTrans (Prediction of MiRNA targets by Trans-omics data) to explore miRNA targets by incorporating miRNA sequencing, transcriptome sequencing, and degradome sequencing. MiRTrans consisted of three major steps. First, the target transcripts of miRNAs were predicted by scrutinizing their sequence characteristics and collected as an initial potential targets pool. Second, false positive targets were eliminated if the expression of miRNA and its targets were weakly correlated by lasso regression. Third, degradome sequencing was utilized to capture the miRNA targets by examining the cleaved transcripts that regulated by miRNAs. Finally, the predicted targets from the second and third step were combined by Fisher's combination test. MiRTrans was applied to identify the miRNA targets for spp. (i.e., pepper). It can generate more functional miRNA targets than sequence-based predictions by evaluating functional enrichment. MiRTrans identified 58 miRNA-transcript pairs with high confidence from 18 miRNA families conserved in eudicots. Most of these targets were transcription factors; this lent support to the role of miRNA as key regulator in pepper. To our best knowledge, this work is the first attempt to investigate the miRNA targets of pepper, as well as their regulatory networks. Surprisingly, only a small proportion of miRNA-transcript pairs were shared between degradome sequencing and expression dependency predictions, suggesting that miRNA targets predicted by a single technology alone may be prone to report false negatives.
微小RNA（miRNA）可调控参与真核细胞增殖、分化和代谢的转录本。特别是对于植物而言，我们对miRNA靶标的了解仍然有限。早期基于序列比对的预测尝试一直受到大量假阳性结果的困扰。通过整合其他数据源，如miRNA与转录本表达之间的相关性，甚至是miRNA调控产生的切割转录本（即跨组学数据），有助于提高靶标预测的特异性。在本文中，我们开发了MiRTrans（通过跨组学数据预测miRNA靶标），通过整合miRNA测序、转录组测序和降解组测序来探索miRNA靶标。MiRTrans包括三个主要步骤。首先，通过仔细研究miRNA的序列特征来预测其靶标转录本，并将其收集为初始潜在靶标池。其次，如果miRNA与其靶标的表达通过套索回归呈弱相关，则消除假阳性靶标。第三，利用降解组测序通过检查受miRNA调控的切割转录本来捕获miRNA靶标。最后，通过Fisher组合检验将第二步和第三步预测的靶标进行合并。MiRTrans被应用于鉴定 spp.（即辣椒）的miRNA靶标。通过评估功能富集，它能够比基于序列的预测产生更多具有功能的miRNA靶标。MiRTrans从真双子叶植物中保守的18个miRNA家族中高可信度地鉴定出58个miRNA-转录本对。这些靶标大多数是转录因子；这支持了miRNA在辣椒中作为关键调节因子的作用。据我们所知，这项工作是首次尝试研究辣椒的miRNA靶标及其调控网络。令人惊讶的是，降解组测序和表达相关性预测之间仅共享了一小部分miRNA-转录本对，这表明仅通过单一技术预测的miRNA靶标可能容易出现假阴性结果。
