文献类型： 外文期刊
作者： Kuang, Zheng 1 ; Wang, Ying 1 ; Li, Lei 2 ; Yang, Xiaozeng 1 ;
作者机构： 1.Beijing Acad Agr & Forestry Sci, Beijing Agrobiotechnol Res Ctr, Beijing 100097, Peoples R China
2.Peking Univ, Sch Adv Agr Sci, Peking Tsinghua Ctr Life Sci, Beijing 100871, Peoples R China
3.Peking Univ, Sch Life Sci, Beijing 100871, Peoples R China
期刊名称：BIOINFORMATICS （ 影响因子：6.937； 五年影响因子：8.47 ）
ISSN： 1367-4803
年卷期： 2019 年 35 卷 14 期
页码：
收录情况： SCI
摘要： Motivation Two major challenges arise when employing next-generation sequencing methods to comprehensively identify microRNAs (miRNAs) in plants: (i) how to minimize the false-positive inheritable to computational predictions and (ii) how to minimize the computational time required for analyzing the miRNA transcriptome in plants with complex and large genomes. Results We updated miRDeep-P to miRDeep-P2 (miRDP2) by employing a new filtering strategy and overhauling the algorithm. miRDP2 has been tested against miRNA transcriptomes in plants with increasing genome sizes that included Arabidopsis, rice, tomato, maize and wheat. Compared with miRDeep-P and several other computational tools, miRDP2 processes next-generation sequencing data with superior speed. By incorporating newly updated plant miRNA annotation criteria and developing a new scoring system, the accuracy of miRDP2 outperformed other programs. Taken together, our results demonstrate miRDP2 as a fast and accurate tool for analyzing the miRNA transcriptome in plants. Availability and implementation The miRDP2 are freely available from https://sourceforge.net/projects/mirdp2/. Supplementary information Supplementary data are available at Bioinformatics online.
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