Identification of plant microRNAs using convolutional neural network

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...

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Published inFrontiers in plant science Vol. 15; p. 1330854
Main Authors Zhang, Yun, Huang, Jianghua, Xie, Feixiang, Huang, Qian, Jiao, Hongguan, Cheng, Wenbo
Format Journal Article
LanguageEnglish
Published Switzerland Frontiers Media S.A 19.03.2024
Subjects
deep learning
Java
microRNA
plant
Plant Science
SRICATs
Java
SRICATs
deep learning
microRNA
plant
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Summary: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.
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These authors have contributed equally to this work
Erpei Lin, Zhejiang Agriculture and Forestry University, China
Reviewed by: Massimo La Rosa, National Research Council (CNR), Italy
Edited by: George V. Popescu, Mississippi State University, United States
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2024.1330854