Identification of plant microRNAs using convolutional neural network

Yun Zhang; Jianghua Huang; Feixiang Xie; Qian Huang; Hongguan Jiao; Wenbo Cheng

doi:10.3389/fpls.2024.1330854

Identification of plant microRNAs using convolutional neural network

Front Plant Sci. 2024 Mar 19:15:1330854. doi: 10.3389/fpls.2024.1330854. eCollection 2024.

Authors

Yun Zhang^#¹, Jianghua Huang^#¹, Feixiang Xie¹, Qian Huang¹, Hongguan Jiao¹, Wenbo Cheng¹

Affiliation

¹ College of Information Engineering, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.

^# Contributed equally.

Abstract

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.

Keywords: Java; SRICATs; deep learning; microRNA; plant.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the National Natural Science Foundation of China (32360152) and the doctoral start-up grant (2019-76) from Guizhou University of Traditional Chinese Medicine.