Analysis of antisense expression by whole genome tiling microarrays and siRNAs suggests mis-annotation of Arabidopsis orphan protein-coding genes.
Analysis of antisense expression by whole genome tiling microarrays and siRNAs suggests mis-annotation of Arabidopsis orphan protein-coding genes.
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DOI：
10.1371/journal.pone.0010710
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发表时间：
2010-05-26
期刊：
影响因子：
3.7
通讯作者：
Rock CD
中科院分区：
文献类型：
--
作者：
Richardson CR;Luo QJ;Gontcharova V;Jiang YW;Samanta M;Youn E;Rock CD
MicroRNA (miRNA) 和反式作用小干扰 RNA (tasi-RNA) 是分别由发夹二级结构或反义转录物生成的小（20-22 nt 长）RNA (smRNA)，它们通过与目标 mRNA 的 Watson-Crick 配对来调节基因表达，并通过与 RNA 干扰相关的机制改变表达。植物 miRNA 与其靶标的高序列同源性一直是 miRNA 预测算法的支柱，这些算法对其他领域的预测能力受到限制，因为 miRNA 互补性不太保守，但传递过程（反义 smRNA 的产生）在真核生物中很活跃。我们假设反义转录和相关 smRNA 是可以通过计算建模来发现基因的生物标志物。我们在公开的全基因组平铺阵列转录组数据和测序的 smRNA 文库（以及线虫）中探索了水稻 (Oryza sativa) 有义和反义基因表达，并发现了与拟南芥中发现的 MIRNA 基因相似的传递性证据。对反义转录本丰度、反义 EST 的存在以及与 smRNA 关联的统计分析表明，数百个拟南芥‘孤儿’假设基因是非编码 RNA。与这一假设一致，我们在先前注释的蛋白质编码基因的反义链上发现了一些 MIRNA 基因的新拟南芥同源物。应用支持向量机 (SVM)，利用结合的热力学能量加上正义/反义转录拓扑和 siRNA 丰度的新表达特征来构建 miRNA 靶点的预测模型。当针对目标进行训练时，SVM 可以预测“古老”（高度保守）类别的已验证拟南芥 MIRNA 基因，准确度为 84%，对于“新”快速进化的 MIRNA 基因，准确度为 76%。反义和 smRNA 表达特征和计算方法可以识别植物和其他潜在领域中的新型 miRNA 基因和其他非编码 RNA，从而深入了解反义转录、miRNA 进化和转录后基因调控。
MicroRNAs (miRNAs) and trans-acting small-interfering RNAs (tasi-RNAs) are small (20–22 nt long) RNAs (smRNAs) generated from hairpin secondary structures or antisense transcripts, respectively, that regulate gene expression by Watson-Crick pairing to a target mRNA and altering expression by mechanisms related to RNA interference. The high sequence homology of plant miRNAs to their targets has been the mainstay of miRNA prediction algorithms, which are limited in their predictive power for other kingdoms because miRNA complementarity is less conserved yet transitive processes (production of antisense smRNAs) are active in eukaryotes. We hypothesize that antisense transcription and associated smRNAs are biomarkers which can be computationally modeled for gene discovery. We explored rice (Oryza sativa) sense and antisense gene expression in publicly available whole genome tiling array transcriptome data and sequenced smRNA libraries (as well as C. elegans) and found evidence of transitivity of MIRNA genes similar to that found in Arabidopsis. Statistical analysis of antisense transcript abundances, presence of antisense ESTs, and association with smRNAs suggests several hundred Arabidopsis ‘orphan’ hypothetical genes are non-coding RNAs. Consistent with this hypothesis, we found novel Arabidopsis homologues of some MIRNA genes on the antisense strand of previously annotated protein-coding genes. A Support Vector Machine (SVM) was applied using thermodynamic energy of binding plus novel expression features of sense/antisense transcription topology and siRNA abundances to build a prediction model of miRNA targets. The SVM when trained on targets could predict the “ancient” (deeply conserved) class of validated Arabidopsis MIRNA genes with an accuracy of 84%, and 76% for “new” rapidly-evolving MIRNA genes. Antisense and smRNA expression features and computational methods may identify novel MIRNA genes and other non-coding RNAs in plants and potentially other kingdoms, which can provide insight into antisense transcription, miRNA evolution, and post-transcriptional gene regulation.
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