An Jiyuan, Choi Kwok Pui, Wells Christine A, Chen Yi-Ping Phoebe
The National Centre for Adult Stem Cell Research, The Eskitis Institute for Cell and Molecular Therapies, Griffith University, Nathan, QLD 4111, Australia.
J Bioinform Comput Biol. 2010 Feb;8(1):99-115. doi: 10.1142/s0219720010004574.
Current miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups. Our procedure involves a sequence of statistical tests: (1) identify genes that are highly probable miRNA targets; (2) determine for each such gene, the minimum number of miRNAs that co-regulate it with high probability; (3) find, for each such gene, the combination of the determined minimum size of miRNAs that co-regulate it with the lowest p-value; and (4) discover for each such combination of miRNAs, the group of genes that are co-regulated by these miRNAs with the lowest p-value computed based on GO term annotations of the genes.
Our method identifies 4, 3 and 2-term miRNA groups that co-regulate gene groups of size at least 3 in human. Our result suggests some interesting hypothesis on the functional role of several miRNAs through a "guilt by association" reasoning. For example, miR-130, miR-19 and miR-101 are known neurodegenerative diseases associated miRNAs. Our 3-term miRNA table shows that miR-130/19/101 form a co-regulating group of rank 22 (p-value =1.16 x 10(-2)). Since miR-144 is co-regulating with miR-130, miR-19 and miR-101 of rank 4 (p-value = 1.16 x 10(-2)) in our 4-term miRNA table, this suggests hsa-miR-144 may be neurodegenerative diseases related miRNA.
This work identifies highly probable co-regulating miRNAs, which are refined from the prediction by computational tools using (1) signal-to-noise ratio to get high accurate regulating miRNAs for every gene, and (2) Gene Ontology to obtain functional related co-regulating miRNA groups. Our result has partly been supported by biological experiments. Based on prediction by TargetScanS, we found highly probable target gene groups in the Supplementary Information. This result might help biologists to find small set of miRNAs for genes of interest rather than huge amount of miRNA set.
当前的miRNA靶标预测工具普遍存在假阳性率高的问题。这使得对miRNA和靶基因的共调控组的鉴定变得不可靠。在本研究中，我们描述了一种鉴定高度可能的共调控miRNA及其相应的共调控基因组的方法。我们的方法涉及一系列统计测试：（1）鉴定高度可能是miRNA靶标的基因；（2）对于每个这样的基因，确定以高概率共同调控它的miRNA的最小数量；（3）对于每个这样的基因，找到共同调控它的具有最低p值的确定最小数量的miRNA的组合；（4）对于每个这样的miRNA组合，发现由这些miRNA共同调控的基因组，这些基因组是根据基因的GO术语注释计算出的具有最低p值的。
我们的方法鉴定出在人类中共同调控至少3个基因的4个、3个和2个miRNA组成的组。我们的结果通过“关联有罪”推理对几种miRNA的功能作用提出了一些有趣的假设。例如，miR-130、miR-19和miR-101是已知与神经退行性疾病相关的miRNA。我们的3个miRNA表显示，miR-130/19/101形成了一个共调控组，排名第22（p值 = 1.16×10^(-2)）。由于在我们的4个miRNA表中，miR-144与排名第4的miR-130、miR-19和miR-101共同调控（p值 = 1.16×10^(-2)），这表明hsa-miR-144可能是与神经退行性疾病相关的miRNA。
这项工作鉴定出了高度可能的共调控miRNA，这些miRNA是通过计算工具的预测进行优化得到的，使用（1）信噪比来为每个基因获得高精度的调控miRNA，以及（2）基因本体论来获得功能相关的共调控miRNA组。我们的结果部分得到了生物学实验的支持。基于TargetScanS的预测，我们在补充信息中发现了高度可能的靶基因组。这一结果可能有助于生物学家为感兴趣的基因找到一小部分miRNA，而不是大量的miRNA集合。
