Ng Kwang Loong Stanley, Mishra Santosh K
Bioinformatics Institute, Singapore.
Bioinformatics. 2007 Jun 1;23(11):1321-30. doi: 10.1093/bioinformatics/btm026. Epub 2007 Jan 31.
MicroRNAs (miRNAs) are small ncRNAs participating in diverse cellular and physiological processes through the post-transcriptional gene regulatory pathway. Critically associated with the miRNAs biogenesis, the hairpin structure is a necessary feature for the computational classification of novel precursor miRNAs (pre-miRs). Though many of the abundant genomic inverted repeats (pseudo hairpins) can be filtered computationally, novel species-specific pre-miRs are likely to remain elusive.
miPred is a de novo Support Vector Machine (SVM) classifier for identifying pre-miRs without relying on phylogenetic conservation. To achieve significantly higher sensitivity and specificity than existing (quasi) de novo predictors, it employs a Gaussian Radial Basis Function kernel (RBF) as a similarity measure for 29 global and intrinsic hairpin folding attributes. They characterize a pre-miR at the dinucleotide sequence, hairpin folding, non-linear statistical thermodynamics and topological levels. Trained on 200 human pre-miRs and 400 pseudo hairpins, miPred achieves 93.50% (5-fold cross-validation accuracy) and 0.9833 (ROC score). Tested on the remaining 123 human pre-miRs and 246 pseudo hairpins, it reports 84.55% (sensitivity), 97.97% (specificity) and 93.50% (accuracy). Validated onto 1918 pre-miRs across 40 non-human species and 3836 pseudo hairpins, it yields 87.65% (92.08%), 97.75% (97.42%) and 94.38% (95.64%) for the mean (overall) sensitivity, specificity and accuracy. Notably, A.mellifera, A.geoffroyi, C.familiaris, E.Barr, H. Simplex virus, H.cytomegalovirus, O.aries, P.patens, R.lymphocryptovirus, Simian virus and Z.mays are unambiguously classified with 100.00% (sensitivity) and >93.75% (specificity).
Data sets, raw statistical results and source codes are available at http://web.bii.a-star.edu.sg/~stanley/Publications
微小RNA（miRNA）是一类小的非编码RNA，通过转录后基因调控途径参与多种细胞和生理过程。发夹结构与miRNA的生物合成密切相关，是对新型前体miRNA（pre-miR）进行计算分类的必要特征。尽管许多丰富的基因组反向重复序列（假发夹）可以通过计算进行过滤，但新的物种特异性pre-miR可能仍然难以捉摸。
miPred是一种从头开始的支持向量机（SVM）分类器，用于识别pre-miR，而不依赖于系统发育保守性。为了实现比现有的（准）从头预测器更高的灵敏度和特异性，它采用高斯径向基函数核（RBF）作为29种全局和内在发夹折叠属性的相似性度量。它们在二核苷酸序列、发夹折叠、非线性统计热力学和拓扑水平上表征pre-miR。在200个人类pre-miR和400个假发夹上进行训练，miPred的准确率达到93.50%（5折交叉验证准确率），ROC分数为0.9833。在其余123个人类pre-miR和246个假发夹上进行测试，其灵敏度为84.55%，特异性为97.97%，准确率为93.50%。在40种非人类物种的1918个pre-miR和3836个假发夹上进行验证，其平均（总体）灵敏度、特异性和准确率分别为87.65%（92.08%）、97.75%（97.42%）和94.38%（95.64%）。值得注意的是，意大利蜜蜂、白额卷尾猴、家犬、E.Barr病毒、单纯疱疹病毒、巨细胞病毒、绵羊、小立碗藓、淋巴隐病毒、猿猴病毒和玉米均被明确分类，灵敏度为100.00%，特异性>93.75%。
数据集、原始统计结果和源代码可在http://web.bii.a-star.edu.sg/~stanley/Publications获取
