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MitoClipSplice: a machine learning framework for resolving mitochondrial RNA cleavage sites from strand-specific
Qing Yuan1, Yu Li2, Fanfan Xie3
1School of Life Science and Technology, Northwestern Polytechnical University, 127 West Youyi Road, Xi'an, 710072, China.
Briefings in Bioinformatics
|August 6, 2026
Summary
Mitochondrial RNA processing relies on transfer RNA (tRNA) punctuation. This study systematically characterizes soft-clipping in RNA sequencing, developing a machine-learning model to accurately identify cleavage sites for disease research.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- Mitochondrial RNA processing, guided by the transfer ribonucleic acid (tRNA) punctuation model, is crucial for cellular function and implicated in various human diseases.
- Strand-specific RNA sequencing (RNA-seq) can detect RNA cleavage intermediates through soft-clipped reads, but these signals lack systematic characterization, hindering precise cleavage site identification.
Purpose of the Study:
- To systematically characterize soft-clipped reads in mitochondrial RNA-seq data.
- To develop and validate a machine-learning model for high-confidence identification of RNA cleavage sites.
- To investigate the impact of library preparation and alignment strategies on soft-clipping detection.
Main Methods:
- Analysis of strand-specific RNA-seq data from 54 samples (35 private, 19 public) using two library types.
- Evaluation of soft-clipped read frequency, quality, GC content, and sequence characteristics.
- Development of a random forest model integrating multiple features to predict cleavage sites.
- Application of the model to identify cleavage sites in hepatocellular carcinoma samples.
Main Results:
- Soft-clipping is prevalent in mitochondrial RNA-seq, significantly higher in second-strand-specific libraries, and independent of quality metrics.
- Soft-clipped sequences are typically short (1-6 nt), guanine-rich, and located at 3' ends, with random priming substantially increasing 3' soft-clipping.
- Allowing soft-clipping during alignment enhances sequencing depth and precision.
- The random forest model demonstrates high performance (F1 > 0.85, AUC > 0.90) for cleavage site identification.
Conclusions:
- This study provides the first systematic characterization of soft-clipping in mitochondrial RNA-seq.
- A high-fidelity, machine-learning-based workflow for identifying RNA cleavage sites has been established.
- This accessible tool will advance research into mitochondrial post-transcriptional regulation and its role in human diseases.
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