Identification of plant microRNAs using convolutional neural network

被引：0

作者：

Zhang, Yun ^{[1
]}

Huang, Jianghua ^{[1
]}

Xie, Feixiang ^{[1
]}

Huang, Qian ^{[1
]}

Jiao, Hongguan ^{[1
]}

Cheng, Wenbo ^{[1
]}

机构：

[1] Guizhou Univ Tradit Chinese Med, Coll Informat Engn, Guiyang, Guizhou, Peoples R China

来源：

FRONTIERS IN PLANT SCIENCE | 2024年 / 15卷

基金：

中国国家自然科学基金;

关键词：

deep learning; plant; microRNA; !text type='Java']Java[!/text; SRICATs; ANNOTATION; TOOL; CRITERIA;

D O I：

10.3389/fpls.2024.1330854

中图分类号：

Q94 [植物学];

学科分类号：

071001 ;

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.

引用

页数：9