Machine learning-augmented m6A-Seq analysis without a reference genome.

ABSTRACT: 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

SUBMITTER: Yang J 

PROVIDER: S-EPMC12104624 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature