A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction.
A comparison of performance of plant miRNA target prediction tools and the characterization of features for genome-wide target prediction.
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DOI：
10.1186/1471-2164-15-348
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发表时间：
2014-05-08
期刊：
影响因子：
4.4
通讯作者：
Pandey SP
中科院分区：
文献类型：
--
作者：
Srivastava PK;Moturu TR;Pandey P;Baldwin IT;Pandey SP
深度测序使得大量微小RNA（miRNAs）和小干扰RNA（siRNAs）得以鉴定，这使得高通量靶标鉴定成为确定其功能的主要限制因素。在植物中，已经开发了几种预测靶标的工具，其中大多数是基于拟南芥数据集进行训练的。对于它们在拟南芥以外的物种中预测靶标的适用性尚未进行广泛而系统的评估。而且，也没有对它们在基因组水平上进行高通量靶标预测的适用性进行评估。
我们评估了11种计算工具在拟南芥和其他植物中鉴定全基因组靶标的性能，采用了优化分数阈值以评估靶标的程序。在预测拟南芥微小RNA - 信使RNA（miRNA - mRNA）相互作用中的“真阳性”靶标方面，Targetfinder效率最高[89%的“准确率”（预测的准确性），97%的“召回率”（敏感性）]。相比之下，在非拟南芥物种中，只有46%的真阳性相互作用被检测到，这表明“召回率”值较低。对于非拟南芥物种中真实的微小RNA - 信使RNA相互作用数据集，分数优化仅将“召回率”提高到70%（相应的“准确率”：65%）。结合Targetfinder和psRNATarget的结果可提供较高的真阳性覆盖率，而psRNATarget和Tapirhybrid输出结果的交集可提供高度“精确”的预测。所有可用工具从非拟南芥数据集中得出的大量“假阴性”预测表明拟南芥和其他物种之间微小RNA - 信使RNA相互作用特征存在差异。一部分微小RNA - 信使RNA相互作用在种子区域的特征以及匹配/错配的总数上存在显著差异。
尽管许多植物微小RNA靶标预测工具可能经过优化以在拟南芥中高特异性地预测靶标，但这种优化的阈值可能不适用于非拟南芥物种中的许多靶标。更重要的是，植物中可能存在微小RNA - 信使RNA相互作用的非常规特征，这表明存在微小RNA靶标识别的替代模式。纳入这些不同的特征将使下一代算法能够更好地识别靶标相互作用。
本文的在线版本（doi:10.1186/1471 - 2164 - 15 - 348）包含补充材料，授权用户可获取。
Deep-sequencing has enabled the identification of large numbers of miRNAs and siRNAs, making the high-throughput target identification a main limiting factor in defining their function. In plants, several tools have been developed to predict targets, majority of them being trained on Arabidopsis datasets. An extensive and systematic evaluation has not been made for their suitability for predicting targets in species other than Arabidopsis. Nor, these have not been evaluated for their suitability for high-throughput target prediction at genome level. We evaluated the performance of 11 computational tools in identifying genome-wide targets in Arabidopsis and other plants with procedures that optimized score-cutoffs for estimating targets. Targetfinder was most efficient [89% ‘precision’ (accuracy of prediction), 97% ‘recall’ (sensitivity)] in predicting ‘true-positive’ targets in Arabidopsis miRNA-mRNA interactions. In contrast, only 46% of true positive interactions from non-Arabidopsis species were detected, indicating low ‘recall’ values. Score optimizations increased the ‘recall’ to only 70% (corresponding ‘precision’: 65%) for datasets of true miRNA-mRNA interactions in species other than Arabidopsis. Combining the results of Targetfinder and psRNATarget delivers high true positive coverage, whereas the intersection of psRNATarget and Tapirhybrid outputs deliver highly ‘precise’ predictions. The large number of ‘false negative’ predictions delivered from non-Arabidopsis datasets by all the available tools indicate the diversity in miRNAs-mRNA interaction features between Arabidopsis and other species. A subset of miRNA-mRNA interactions differed significantly for features in seed regions as well as the total number of matches/mismatches. Although, many plant miRNA target prediction tools may be optimized to predict targets with high specificity in Arabidopsis, such optimized thresholds may not be suitable for many targets in non-Arabidopsis species. More importantly, non-conventional features of miRNA-mRNA interaction may exist in plants indicating alternate mode of miRNA target recognition. Incorporation of these divergent features would enable next-generation of algorithms to better identify target interactions. The online version of this article (doi:10.1186/1471-2164-15-348) contains supplementary material, which is available to authorized users.
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