外文摘要：MicroRNAs (miRNAs) are of significance in tuning and buffering gene expression. Despite abundant analysis tools that have been developed in the last two decades, plant miRNA identification from next-generation sequencing (NGS) data remains challenging. Here, we show that we can train a convolutional neural network to accurately identify plant miRNAs from NGS data. Based on our methods, we also present a user-friendly pure Java-based software package called Small RNA-related Intelligent and Convenient Analysis Tools (SRICATs). SRICATs encompasses all the necessary steps for plant miRNA analysis. Our results indicate that SRICATs outperforms currently popular software tools on the test data from five plant species. For non-commercial users, SRICATs is freely available at https://sourceforge.net/projects/sricats.
外文关键词：deep learning;Plant;microRNA;Java;SRICATs
作者：Zhang, Yun;Huang, Jianghua;Xie, Feixiang;Huang, Qian;Jiao, Hongguan;Cheng, Wenbo
作者单位：Guizhou Univ Tradit Chinese Med
期刊名称：FRONTIERS IN PLANT SCIENCE
期刊影响因子：0.0
出版年份：2024
出版刊次：15
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台