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Systematic evaluation of computational tools for multitype RNA modification detection using nanopore direct RNA
Tingting Luo1, Moping Xu1, Miao Wang1
1Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of the Ministry of Education, Tongji Hospital affiliated to Tongji University, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, China.
Evaluating 86 tools for RNA modification detection via nanopore sequencing, this study finds retraining with diverse data improves accuracy. Non-m6A modification tools require further development for reliable biological validation.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Nanopore direct RNA sequencing enables single-base resolution detection of RNA modifications.
- Accurate identification of diverse RNA modifications is crucial for understanding gene regulation and cellular function.
Purpose of the Study:
- To systematically evaluate the performance of 86 computational tools for detecting six key RNA modifications (m6A, Ψ, m5C, A-to-I editing, m7G, and m1A) using nanopore direct RNA sequencing data.
- To assess the impact of retraining tools with combined in vitro transcription and real biological samples on their accuracy and generalizability.
- To identify the strengths and weaknesses of current computational tools for various RNA modification detection tasks.
Main Methods:
- Systematic evaluation of 86 computational tools across six RNA modifications.
- Utilized direct RNA sequencing data from RNA002 and RNA004 chemistries.
- Assessed tool performance before and after retraining with combined in vitro and real biological samples.
Main Results:
- Retraining tools significantly enhanced accuracy and generalizability, particularly for Ψ, m5C, and A-to-I modifications.
- m6A detection tools demonstrated high accuracy on real biological samples.
- Non-m6A detection tools exhibited challenges in precision-recall balance, quantification accuracy, and biological validity.
Conclusions:
- Diverse training data, including both in vitro and real biological samples, is essential for improving RNA modification detection tools.
- Current non-m6A detection tools require further optimization to ensure reliable performance and biological relevance.
- Development of tools with enhanced capability for distinguishing between modification types at single-base resolution is needed.
