Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms
Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms
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DOI：
10.1261/rna.7290705
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发表时间：
2005-07-01
期刊：
影响因子：
4.5
通讯作者：
Saetrom, P
中科院分区：
文献类型：
--
作者：
Sætrom, O;Snove, O;Saetrom, P
我们提出了一种新的microRNA目标预测算法TargetBoost，并证明了该算法的稳定性，并且识别出比现有算法更多的真实目标。TargetBoost使用机器学习在低等生物中的一组经过验证的microRNA目标上创建加权序列基序，以捕获microRNA与其目标之间的结合特征。现有的算法要求候选者具有(1)microRNAs‘5’端与其靶标之间的近乎完美的互补性；(2)相对较高的热力学双链稳定性；(3)靶标3‘端非编码区中的多个靶点；以及(4)靶标在物种之间的进化保守性。大多数算法在种子步骤中使用前两个要求中的一个，并使用其他三个要求作为过滤器来提高方法的特异性。初始种子步骤决定了算法的敏感度，也影响了算法的专一性。由于所有算法都可能添加过滤器以增加特异性，因此我们建议在进行此类过滤之前应对各种方法进行比较。我们发现，TargetBoost的加权序列基序方法有利于同时使用双链稳定步骤和序列互补步骤。
We present a new microRNA target prediction algorithm called TargetBoost, and show that the algorithm is stable and identifies more true targets than do existing algorithms. TargetBoost uses machine learning on a set of validated microRNA targets in lower organisms to create weighted sequence motifs that capture the binding characteristics between microRNAs and their targets. Existing algorithms require candidates to have (1) near-perfect complementarity between microRNAs' 5' end and their targets; (2) relatively high thermodynamic duplex stability; (3) multiple target sites in the target's 3' UTR; and (4) evolutionary conservation of the target between species. Most algorithms use one of the two first requirements in a seeding step, and use the three others as filters to improve the method's specificity. The initial seeding step determines an algorithm's sensitivity and also influences its specificity. As all algorithms may add filters to increase the specificity, we propose that methods should be compared before such filtering. We show that TargetBoost's weighted sequence motif approach is favorable to using both the duplex stability and the sequence complementarity steps.