Yang Jing, Song Minggui, Bu Yifan, Zhao Haonan, Liu Chenghui, Zhang Ting, Zhang Chujun, Xu Shutu, Ma Chuang
State Key Laboratory of Crop Stress Resistance and High-Efficiency Production, College of Life Sciences, Northwest A&F University, Shaanxi, Yangling 712100, China.
Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Shaanxi, Yangling 712100, China.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf235.
Methylated RNA m6A immunoprecipitation sequencing (m6A-Seq) is a powerful technique for investigating transcriptome-wide m6A modification. However, most of the existing m6A-Seq protocols rely on reference genomes, limiting their use in species lacking sequenced genomes. Here, we introduce mlPEA, a user-friendly, multi-functional platform specifically tailored to the streamlined processing of m6A-Seq data in a reference genome-free manner. mlPEA provides a comprehensive collection of functions required for performing transcriptome-wide m6A identification and analysis, where the reference-de novo assembled transcriptome-is built solely using m6A-Seq data. By taking advantage of machine learning (ML) algorithms, mlPEA enhances m6A-Seq data analysis by constructing robust computational models for identifying high-quality transcripts and high-confidence m6A-modified regions. These functions and ML models have been integrated into a web-based Galaxy framework. This ensures that mlPEA has powerful data interaction and visualization capabilities, with flexibility, traceability, and reproducibility throughout the analytical process. mlPEA also has high compatibility and portability as it employs advanced packaging technology, dramatically simplifying its large-scale application in various species. Validated through case studies of Arabidopsis, maize, and wheat, mlPEA has demonstrated its utility and robustness regarding reference genome-free m6A-Seq data analysis for plants of various genomic complexities. mlPEA is freely available via GitHub: https://github.com/cma2015/mlPEA.
甲基化RNA m6A免疫沉淀测序（m6A-Seq）是一种用于研究全转录组范围m6A修饰的强大技术。然而，现有的大多数m6A-Seq方案都依赖参考基因组，限制了它们在缺乏测序基因组的物种中的应用。在此，我们介绍mlPEA，这是一个用户友好的多功能平台，专门为以无参考基因组的方式简化m6A-Seq数据处理而量身定制。mlPEA提供了进行全转录组范围m6A鉴定和分析所需的全面功能集合，其中参考从头组装的转录组仅使用m6A-Seq数据构建。通过利用机器学习（ML）算法，mlPEA通过构建强大的计算模型来识别高质量转录本和高可信度的m6A修饰区域，从而增强了m6A-Seq数据分析。这些功能和ML模型已集成到基于网络的Galaxy框架中。这确保了mlPEA具有强大的数据交互和可视化能力，在整个分析过程中具有灵活性、可追溯性和可重复性。mlPEA还具有高兼容性和可移植性，因为它采用了先进的打包技术，极大地简化了其在各种物种中的大规模应用。通过对拟南芥、玉米和小麦的案例研究进行验证，mlPEA已证明其在分析各种基因组复杂性植物的无参考基因组m6A-Seq数据方面的实用性和稳健性。mlPEA可通过GitHub免费获取：https://github.com/cma2015/mlPEA。
