Shao Mingyu, Sun Yanni, Zhou Shuigeng
School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, 220 Handan Road, Shanghai 200433, China; Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, USA.
Department of Computer Science and Engineering, Michigan State University, 428 S. Shaw Lane, East Lansing, 48824, USA.
PLoS One. 2015 Apr 29;10(4):e0125156. doi: 10.1371/journal.pone.0125156. eCollection 2015.
MicroRNAs are known to play important roles in the transcriptional and post-transcriptional regulation of gene expression. While intensive research has been conducted to identify miRNAs and their target genes in various genomes, there is only limited knowledge about how microRNAs are regulated. In this study, we construct a pipeline that can infer the regulatory relationships between transcription factors and microRNAs from ChIP-Seq data with high confidence. In particular, after identifying candidate peaks from ChIP-Seq data, we formulate the inference as a PU learning (learning from only positive and unlabeled examples) problem. Multiple features including the statistical significance of the peaks, the location of the peaks, the transcription factor binding site motifs, and the evolutionary conservation are derived from peaks for training and prediction. To further improve the accuracy of our inference, we also apply a mean reciprocal rank (MRR)-based method to the candidate peaks. We apply our pipeline to infer TF-miRNA regulatory relationships in mouse embryonic stem cells. The experimental results show that our approach provides very specific findings of TF-miRNA regulatory relationships.
已知微小RNA在基因表达的转录和转录后调控中发挥重要作用。虽然已经开展了大量研究来鉴定各种基因组中的微小RNA及其靶基因，但对于微小RNA如何被调控的了解仍然有限。在本研究中，我们构建了一个流程，能够从ChIP-Seq数据中高置信度地推断转录因子与微小RNA之间的调控关系。具体而言，在从ChIP-Seq数据中识别出候选峰之后，我们将该推断表述为一个PU学习（仅从正例和未标记示例中学习）问题。包括峰的统计显著性、峰的位置、转录因子结合位点基序以及进化保守性在内的多个特征从峰中提取出来用于训练和预测。为了进一步提高我们推断的准确性，我们还将基于平均倒数排名（MRR）的方法应用于候选峰。我们应用我们的流程来推断小鼠胚胎干细胞中的转录因子-微小RNA调控关系。实验结果表明，我们的方法提供了关于转录因子-微小RNA调控关系非常具体的发现。
