Intragenomic matching reveals a huge potential for miRNA-mediated regulation in plants.
Intragenomic matching reveals a huge potential for miRNA-mediated regulation in plants.
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DOI：
10.1371/journal.pcbi.0030238
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发表时间：
2007-11
影响因子：
4.3
通讯作者：
Krogh A
中科院分区：
文献类型：
--
作者：
Lindow M;Jacobsen A;Nygaard S;Mang Y;Krogh A
微小RNA（miRNAs）是重要的转录后调节因子，但就miRNA基因的数量及其靶标而言，这种调节的程度尚不确定。我们利用一种基于潜在miRNAs及其靶标在基因组内匹配的算法，结合对miRNA前体的支持向量机分类，探索了miRNAs在三种植物基因组（拟南芥、毛果杨和水稻）中的调节潜力。我们发现，基因组内匹配结合有监督的学习方法包含足够的信息，能够可靠地对miRNA候选物进行计算机预测，而无需跨物种保守性。利用这种方法，我们分别在拟南芥、毛果杨和水稻中鉴定出约1200个、约2500个和约2100个能够与潜在靶标mRNA广泛碱基配对的miRNA候选基因。这是目前这些植物中已注释的miRNAs数量的五倍多。这些候选基因中有许多来自重复区域，但它们似乎包含了被miRNA机制正确加工所必需的特征。保守性分析表明，只有少数候选基因在物种间是保守的。我们得出结论，在所研究植物的基因组中，存在着由miRNAs介导的调节相互作用的巨大潜力。我们假设，其中一些相互作用可能在特殊环境条件下实现，而另一些在生物分化并适应新生态位时很容易被利用。
微小RNA（miRNAs）是通过与mRNA互补碱基配对来调节基因表达的小分子RNA。在植物中，这种碱基配对在miRNAs的整个长度上几乎是完美的。这种长的互补区域使得对已知miRNAs的靶标进行计算机预测相对容易。为了预测新的miRNA基因，我们利用这一点并反转了靶标预测：我们不是为已知的miRNAs预测靶标，而是为所有已知的mRNAs预测新的miRNA候选物。因为靶标和miRNA候选物之间的匹配是该方法不可或缺的，所以有可能在不依赖进化保守性的情况下实现良好的预测，这与大多数其他当前的方法不同。这意味着我们能够预测对一种生物特异的新的miRNAs。有趣的是，这有助于解释具有非常相似的蛋白质编码基因但表型差异很大的物种之间的差异。此外，结果表明，这些新的miRNA候选物中有许多来自基因组重复区域，如转座子，这表明重复序列/转座子在基因表达调节中可能具有积极作用。
microRNAs (miRNAs) are important post-transcriptional regulators, but the extent of this regulation is uncertain, both with regard to the number of miRNA genes and their targets. Using an algorithm based on intragenomic matching of potential miRNAs and their targets coupled with support vector machine classification of miRNA precursors, we explore the potential for regulation by miRNAs in three plant genomes: Arabidopsis thaliana, Populus trichocarpa, and Oryza sativa. We find that the intragenomic matching in conjunction with a supervised learning approach contains enough information to allow reliable computational prediction of miRNA candidates without requiring conservation across species. Using this method, we identify ∼1,200, ∼2,500, and ∼2,100 miRNA candidate genes capable of extensive base-pairing to potential target mRNAs in A. thaliana, P. trichocarpa, and O. sativa, respectively. This is more than five times the number of currently annotated miRNAs in the plants. Many of these candidates are derived from repeat regions, yet they seem to contain the features necessary for correct processing by the miRNA machinery. Conservation analysis indicates that only a few of the candidates are conserved between the species. We conclude that there is a large potential for miRNA-mediated regulatory interactions encoded in the genomes of the investigated plants. We hypothesize that some of these interactions may be realized under special environmental conditions, while others can readily be recruited when organisms diverge and adapt to new niches. microRNAs (miRNAs) are small RNA molecules that regulate gene expression by complementary basepairing to mRNAs. In plants, this base-pairing is almost perfect along the whole length of miRNAs. This long stretch of complementarity makes it relatively easy to make computational predictions of the targets for known miRNAs. To predict novel miRNA genes, we take advantage of this and reverse the target prediction: instead of predicting targets for known miRNAs, we predict novel miRNA candidates for all known mRNAs. Because matching between target and miRNA candidates is integral to the method, it is possible to achieve good predictions without having to rely on evolutionary conservation, as most other current methods do. This means that we can predict new miRNAs that are specific to an organism. Interestingly, this could help explain the difference between species that have very similar protein-coding genes, but highly different phenotypes. Furthermore, it turns out that many of these new miRNA candidates derive from genomic repeat regions such as transposons, which points to a possible active role for repeats/transposons in the regulation of gene expression.
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影响因子：
56.9
作者：
Cheng, J;Kapranov, P;Gingeras, TR
通讯作者：
Gingeras, TR
影响因子：
4.5
作者：
Maeda N;Kasukawa T;Oyama R;Gough J;Frith M;Engström PG;Lenhard B;Aturaliya RN;Batalov S;Beisel KW;Bult CJ;Fletcher CF;Forrest AR;Furuno M;Hill D;Itoh M;Kanamori-Katayama M;Katayama S;Katoh M;Kawashima T;Quackenbush J;Ravasi T;Ring BZ;Shibata K;Sugiura K;Takenaka Y;Teasdale RD;Wells CA;Zhu Y;Kai C;Kawai J;Hume DA;Carninci P;Hayashizaki Y
通讯作者：
Hayashizaki Y
影响因子：
3.7
作者：
Fahlgren N;Howell MD;Kasschau KD;Chapman EJ;Sullivan CM;Cumbie JS;Givan SA;Law TF;Grant SR;Dangl JL;Carrington JC
通讯作者：
Carrington JC
影响因子：
30.8
作者：
Bentwich, I;Avniel, A;Bentwich, Z
通讯作者：
Bentwich, Z
影响因子：
11.6
作者：
Llave, C;Kasschau, KD;Carrington, JC
通讯作者：
Carrington, JC