Recognizing drosha processing sites by a two-step prediction model with structure and sequence information

Xingchi Hu, Yanhong Zhou, Chuang Ma
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引用次数: 1

Abstract

Drosha is a class of RNase III enzyme plays important roles in the microRNA (miRNA) generation by cleaving primary miRNAs to release hairpin-shaped miRNA precursors. Accurately predicting the Drosha cleavage positions (i.e., processing sites) is helpful for the identification of miRNAs and the understanding of miRNA biogenesis mechanisms. In this study, we presented a Drosha processing site predictor, termed DroshaPSP, with a two-step prediction model by integrating structure and sequence features. Testing results on the Drosophila melanogaster miRNA data showed that DroshaPSP obtained a sensitivity of 0.859, a specificity of 0.999, and a Matthew's Correlation Coefficient of 0.864. We also found that the Shannon entropy is a powerful structure feature for DroshaPSP to distinguish true Drosha processing sites from the nearby pseudo processing sites effectively.
基于结构和序列信息的两步预测模型识别drosha加工位点
Drosha是一类RNase III酶,通过切割初级miRNA释放发夹状miRNA前体,在microRNA (miRNA)的生成中起重要作用。准确预测Drosha切割位置(即加工位点)有助于miRNA的鉴定和对miRNA生物发生机制的理解。在这项研究中,我们提出了一个Drosha加工位点预测器,称为DroshaPSP,具有两步预测模型,通过整合结构和序列特征。对黑腹果蝇miRNA数据的检测结果显示,DroshaPSP敏感性为0.859,特异性为0.999,马修相关系数为0.864。我们还发现Shannon熵是一个强大的结构特征,可以有效地区分真正的Drosha处理点和附近的伪处理点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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