Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing

Area of Science:

• Epigenetics
• Computational Biology
• RNA Biology

Background:

• N6-methyladenosine (m6A) is a prevalent internal mRNA modification regulating diverse biological processes.
• Direct Nanopore sequencing of native RNA (dRNA-seq) is a key technology for m6A identification.
• Existing m6A detection software require independent benchmarking across species and reference datasets.

Purpose of the Study:

• To develop and validate NanOlympicsMod, a Nextflow pipeline for comparing m6A detection tools on dRNA-seq data.
• To provide a streamlined computational workflow for m6A tool execution and analysis.
• To assess the performance of 14 m6A detection tools using synthetic and biological dRNA-seq datasets.

Main Methods:

• Developed NanOlympicsMod, a containerized Nextflow pipeline for m6A tool benchmarking.
• Tested NanOlympicsMod on in vitro synthetic oligos with known m6A positions.
• Evaluated tool performance on dRNA-seq data from yeast, mouse, and human wild-type and m6A-depleted samples, comparing against orthogonal m6A datasets.

Main Results:

• Significant variation in tool performance was observed across different datasets and species.
• m6A detection tools exhibited distinct precision-recall trade-offs based on their underlying methodologies.
• Tool precision and recall are influenced by sequencing depth, suggesting benefits from increased sequencing.
• Adjusting stringency cut-offs allows users to tune the precision-recall balance.

Conclusions:

• NanOlympicsMod provides a robust platform for standardized benchmarking of m6A detection tools.
• The pipeline facilitates comparison of tools on diverse dRNA-seq data, aiding in the selection of appropriate methods.
• Future inclusion of novel tools within NanOlympicsMod will enhance RNA modification characterization and discovery.