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Machine learning-augmented m6A-Seq analysis without a reference genome
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DOI：10.1093/bib/bbaf235.png)
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Abstract
En 中文
Methylated RNA m(6)A immunoprecipitation sequencing (m(6)A-Seq) is a powerful technique for investigating transcriptome-wide m(6)A modification. However, most of the existing m(6)A-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 m(6)A-Seq data in a reference genome-free manner. mlPEA provides a comprehensive collection of functions required for performing transcriptome-wide m(6)A identification and analysis, where the reference-de novo assembled transcriptome-is built solely using m(6)A-Seq data. By taking advantage of machine learning (ML) algorithms, mlPEA enhances m(6)A-Seq data analysis by constructing robust computational models for identifying high-quality transcripts and high-confidence m(6)A-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 m(6)A-Seq data analysis for plants of various genomic complexities. mlPEA is freely available via GitHub: https://github.com/cma2015/mlPEA.
Keywords:
epitranscriptome
m(6)A modification
m(6)A sequencing
reference genome-free analysis
machine learning
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