Computational dissection of Arabidopsis smRNAome leads to discovery of novel microRNAs and short interfering RNAs associated with transcription start sites.
Computational dissection of Arabidopsis smRNAome leads to discovery of novel microRNAs and short interfering RNAs associated with transcription start sites.
复制标题
DOI：
10.1016/j.ygeno.2011.01.006
复制
发表时间：
2011-04
期刊：
影响因子：
4.4
通讯作者：
Liu, X. Shirley
中科院分区：
文献类型：
--
作者：
Wang, Xiangfeng;Laurie, John D.;Liu, Tao;Wentz, Jacqueline;Liu, X. Shirley
通过高通量测序（smRNA-Seq）对小RNA进行分析揭示了RNA世界的复杂性。在这里，我们描述了一个计算方案，通过整合拟南芥中的smRNA-Seq数据集解剖植物smRNAome。我们的分析方法首先定义从头开始的基因组位点，产生smRNAs作为基本单位，然后利用主成分分析（PCA）来预测新的miRNA。对候选基因的推定前体进行二级结构预测，发现了一组由降解的编码基因反向复制形成的长发夹双链RNA（lh-dsRNAs）。这些基因残余物产生主要为21-和22-nt长的miRNA样小RNA，其依赖于DCL 1但不依赖于RDR 2和DCL 2/3/4，并且与AGO 1相关。此外，我们还发现了两类转录起始位点相关的（TSSa-）RNA，分别位于TSSs下游约100 ~ 200 bp处的正义（+）和反义（-）RNA，但它们分别被差异地掺入AGO 1和AGO 4中。
The profiling of small RNAs by high throughput sequencing (smRNA-Seq) has revealed the complexity of the RNA world. Here, we describe a computational scheme for dissecting the plant smRNAome by integrating smRNA-Seq datasets in Arabidopsis thaliana. Our analytical approach first defines ab initio the genomic loci that produce smRNAs as basic units, then utilizes principal component analysis (PCA) to predict novel miRNAs. Secondary structure prediction of candidates’ putative precursors discovered a group of long hairpin double-stranded RNAs (lh-dsRNAs) formed by inverted duplications of decayed coding genes. These gene remnants produce miRNA-like small RNAs which are predominantly 21- and 22-nt long, dependent of DCL1 but independent of RDR2 and DCL2/3/4, and associated with AGO1. Additionally, we found two classes of transcription start site associated- (TSSa-) RNAs located at sense (+) and antisense (−) approximately 100 ~ 200 bp downstream of TSSs, but are differentially incorporated into AGO1 and AGO4, respectively.
登录
查看更多内容
影响因子：
56.9
作者：
Kapranov, Philipp;Cheng, Jill;Gingeras, Thomas R.
通讯作者：
Gingeras, Thomas R.
影响因子：
14.9
作者：
Hawkins PG;Santoso S;Adams C;Anest V;Morris KV
通讯作者：
Morris KV
影响因子：
64.5
作者：
Mi, Shijun;Cai, Tao;Qi, Yijun
通讯作者：
Qi, Yijun
影响因子：
30.8
作者：
Allen, E;Xie, ZX;Carrington, JC
通讯作者：
Carrington, JC
影响因子：
30.8
作者：
Henderson, Ian R.;Zhang, Xiaoyu;Jacobsen, Steven E.
通讯作者：
Jacobsen, Steven E.