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鉴定细胞特异性 microRNA 转录起始位点。

Identifying cell-specific microRNA transcriptional start sites.

机构信息

The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210093, China Department of Bioinformatics, Medical School, George August University of Göttingen, Goldschmidtstrasse 1, Göttingen D-37077, Germany.

The State Key Laboratory of Pharmaceutical Biotechnology and Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology, NJU Advanced Institute for Life Sciences (NAILS), School of Life Science, Nanjing University, Nanjing 210093, China.

出版信息

Bioinformatics. 2016 Aug 15;32(16):2403-10. doi: 10.1093/bioinformatics/btw171. Epub 2016 Apr 19.


DOI:10.1093/bioinformatics/btw171
PMID:27153609
Abstract

MOTIVATION: Identification of microRNA (miRNA) transcriptional start sites (TSSs) is crucial to understand the transcriptional regulation of miRNA. As miRNA expression is highly cell specific, an automatic and systematic method that could identify miRNA TSSs accurately and cell specifically is in urgent requirement. RESULTS: A workflow to identify the TSSs of miRNAs was built by integrating the data of H3K4me3 and DNase I hypersensitive sites as well as combining the conservation level and sequence feature. By applying the workflow to the data for 54 cell lines from the ENCODE project, we successfully identified TSSs for 663 intragenic miRNAs and 620 intergenic miRNAs, which cover 84.2% (1283/1523) of all miRNAs recorded in miRBase 18. For these cell lines, we found 4042 alternative TSSs for intragenic miRNAs and 3186 alternative TSSs for intergenic miRNAs. Our method achieved a better performance than the previous non-cell-specific methods on miRNA TSSs. The cell-specific method developed by Georgakilas et al. gives 158 TSSs of higher accuracy in two cell lines, benefitting from the employment of deep-sequencing technique. In contrast, our method provided a much higher number of miRNA TSSs (7228) for a broader range of cell lines without the limitation of costly deep-sequencing data, thus being more applicable for various experimental cases. Analysis showed that upstream promoters at - 2 kb to - 200 bp of TSS are more conserved for independently transcribed miRNAs, while for miRNAs transcribed with host genes, their core promoters (-200 bp to 200 bp of TSS) are significantly conserved. AVAILABILITY AND IMPLEMENTATION: Predicted miRNA TSSs and promoters can be downloaded from supplementary files. CONTACT: jwang@nju.edu.cn or jlee@nju.edu.cn or edgar.wingender@bioinf.med.uni-goettingen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

摘要

动机:鉴定 microRNA(miRNA)转录起始位点(TSS)对于理解 miRNA 的转录调控至关重要。由于 miRNA 的表达具有高度的细胞特异性,因此需要一种能够准确且特异性地鉴定 miRNA TSS 的自动和系统方法。

结果:通过整合 H3K4me3 和 DNA 酶 I 超敏位点的数据,并结合保守水平和序列特征,构建了一种鉴定 miRNA TSS 的工作流程。通过将该工作流程应用于 ENCODE 项目中 54 种细胞系的数据,我们成功鉴定了 663 个内含子 miRNA 和 620 个基因间 miRNA 的 TSS,涵盖了 miRBase 18 中记录的所有 miRNA 的 84.2%(1283/1523)。对于这些细胞系,我们发现了 4042 个内含子 miRNA 的替代 TSS 和 3186 个基因间 miRNA 的替代 TSS。我们的方法在 miRNA TSS 上的性能优于以前的非细胞特异性方法。Georgakilas 等人开发的细胞特异性方法在两种细胞系中获得了 158 个更准确的 TSS,这得益于深度测序技术的应用。相比之下,我们的方法提供了更多的 miRNA TSS(7228 个),适用于更广泛的细胞系,而不受昂贵的深度测序数据的限制,因此更适用于各种实验情况。分析表明,TSS 上游-2kb 到-200bp 的启动子对于独立转录的 miRNA 更为保守,而对于与宿主基因转录的 miRNA,其核心启动子(TSS 的-200bp 到 200bp)则显著保守。

可用性和实现:预测的 miRNA TSS 和启动子可从补充文件中下载。

联系方式:jwang@nju.edu.cn 或 jlee@nju.edu.cn 或 edgar.wingender@bioinf.med.uni-goettingen.de

补充信息:补充数据可在 Bioinformatics 在线获取。

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