Song Junwei, Lin Li-An, Tang Chao, Chen Chuan, Yang Qingxin, Zhang Dan, Zhao Yuancun, Wei Han-Cheng, Linghu Kepan, Xu Zijie, Chen Tingfeng, He Zhifeng, Liu Defu, Zhong Yu, Zhu Weizhen, Zeng Wanqin, Chen Li, Song Guiqin, Chen Mutian, Jiang Juan, Zhou Juan, Wang Jing, Chen Bojiang, Ying Binwu, Wang Yuan, Geng Jia, Lin Jing-Wen, Chen Lu
Department of Laboratory Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
Biosafety Laboratory, lnternational Center for Biological and Translational Research, West China Hospital, Sichuan University, Chengdu, 610041, China.
Genome Biol. 2025 Mar 28;26(1):76. doi: 10.1186/s13059-025-03536-3.
Nanopore direct RNA sequencing (DRS) is a powerful tool for RNA biology but suffers from low basecalling accuracy, low throughput, and high input requirements. We present DEMINERS, a novel DRS toolkit combining an RNA multiplexing workflow, a Random Forest-based barcode classifier, and an optimized convolutional neural network basecaller with species-specific training. DEMINERS enables accurate demultiplexing of up to 24 samples, reducing RNA input and runtime. Applications include clinical metagenomics, cancer transcriptomics, and parallel transcriptomic comparisons, uncovering microbial diversity in COVID-19 and mA's role in malaria and glioma. DEMINERS offers a robust, high-throughput solution for precise transcript and RNA modification analysis.
纳米孔直接RNA测序(DRS)是RNA生物学研究的强大工具,但存在碱基识别准确率低、通量低和输入要求高的问题。我们提出了DEMINERS,这是一种新颖的DRS工具包,它结合了RNA多重工作流程、基于随机森林的条形码分类器以及经过物种特异性训练优化的卷积神经网络碱基识别器。DEMINERS能够对多达24个样本进行准确的多重分析,减少RNA输入量和运行时间。其应用包括临床宏基因组学、癌症转录组学以及平行转录组比较,可揭示新冠病毒中的微生物多样性以及甲基化在疟疾和神经胶质瘤中的作用。DEMINERS为精确的转录本和RNA修饰分析提供了强大的高通量解决方案。