Chen Xiaofeng, Liu Yongqi, Lv Kaiwen, Wang Meiling, Liu Xiaoqin, Li Bosheng
Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261000, China.
National Key Laboratory of Wheat Improvement, Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang, Shandong 261325, China.
Bioinform Adv. 2023 Jul 20;3(1):vbad099. doi: 10.1093/bioadv/vbad099. eCollection 2023.
Direct RNA-seq (dRNA-seq) using Oxford Nanopore Technology (ONT) has revolutionized transcript mapping by offering enhanced precision due to its long-read length. Unlike traditional techniques, dRNA-seq eliminates the need for PCR amplification, reducing the impact of GC bias, and preserving valuable base physical information, such as RNA modification and poly(A) length estimation. However, the rapid advancement of ONT devices has set higher standards for analytical software, resulting in potential challenges of software incompatibility and reduced efficiency.
We present a novel workflow, called FASTdRNA, to manipulate dRNA-seq data efficiently. This workflow comprises two modules: a data preprocessing module and a data analysis module. The preprocessing data module, dRNAmain, encompasses basecalling, mapping, and transcript counting, which are essential for subsequent analyses. The data analysis module consists of a range of downstream analyses that facilitate the estimation of poly(A) length, prediction of RNA modifications, and assessment of alternative splicing events across different conditions with duplication. The FASTdRNA workflow is designed for the Snakemake framework and can be efficiently executed locally or in the cloud. Comparative experiments have demonstrated its superior performance compared to previous methods. This innovative workflow enhances the research capabilities of dRNA-seq data analysis pipelines by optimizing existing processes and expanding the scope of analysis.
The workflow is freely available at https://github.com/Tomcxf/FASTdRNA under an MIT license. Detailed install and usage guidance can be found in the GitHub repository.
使用牛津纳米孔技术（ONT）的直接RNA测序（dRNA-seq）因其长读长而提高了精度，从而彻底改变了转录本图谱绘制。与传统技术不同，dRNA-seq无需PCR扩增，减少了GC偏差的影响，并保留了宝贵的碱基物理信息，如RNA修饰和聚腺苷酸（poly(A)）长度估计。然而，ONT设备的快速发展对分析软件提出了更高的标准，导致软件不兼容和效率降低等潜在挑战。
我们提出了一种名为FASTdRNA的新型工作流程，以高效处理dRNA-seq数据。该工作流程包括两个模块：数据预处理模块和数据分析模块。预处理数据模块dRNAmain包括碱基识别、映射和转录本计数，这些对于后续分析至关重要。数据分析模块由一系列下游分析组成，有助于估计poly(A)长度、预测RNA修饰以及评估不同条件下重复的可变剪接事件。FASTdRNA工作流程是为Snakemake框架设计的，可以在本地或云端高效执行。对比实验表明，与以前的方法相比，它具有卓越的性能。这种创新的工作流程通过优化现有流程和扩大分析范围，增强了dRNA-seq数据分析管道的研究能力。
该工作流程可在https://github.com/Tomcxf/FASTdRNA上免费获取，遵循MIT许可。详细的安装和使用指南可在GitHub存储库中找到。
