Discovering microRNAs from deep sequencing data using miRDeep
Discovering microRNAs from deep sequencing data using miRDeep
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DOI：
10.1038/nbt1394
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发表时间：
2008-04-01
影响因子：
46.9
通讯作者：
Rajewsky, Nikolaus
中科院分区：
文献类型：
--
作者：
Friedlaender, Marc R.;Chen, Wei;Rajewsky, Nikolaus
高度平行的测序技术以前所未有的深度检测小RNA的能力表明它们在系统地鉴定microRNA（miRNAs）方面的价值。然而，从单个深度测序运行的大量测序转录物中鉴定miRNA仍然是一个重大挑战。在这里，我们提出了一种算法，miRDeep，它使用的概率模型的miRNA生物发生评分的位置和频率的兼容性测序RNA的二级结构的miRNA前体。我们使用已发表的秀丽隐杆线虫数据和我们通过深度测序人类和狗RNA生成的数据证明了其准确性和鲁棒性。miRDeep报告了230种以前未注释的miRNAs，其中4种新的C.通过北方印迹分析来验证线虫miRNA。
The capacity of highly parallel sequencing technologies to detect small RNAs at unprecedented depth suggests their value in systematically identifying microRNAs ( miRNAs). However, the identification of miRNAs from the large pool of sequenced transcripts from a single deep sequencing run remains a major challenge. Here, we present an algorithm, miRDeep, which uses a probabilistic model of miRNA biogenesis to score compatibility of the position and frequency of sequenced RNA with the secondary structure of the miRNA precursor. We demonstrate its accuracy and robustness using published Caenorhabditis elegans data and data we generated by deep sequencing human and dog RNAs. miRDeep reports altogether similar to 230 previously unannotated miRNAs, of which four novel C. elegans miRNAs are validated by northern blot analysis.