ArticleBriefings in bioinformatics2026
MitoClipSplice: a machine learning framework for resolving mitochondrial RNA cleavage sites from strand-specific RNA-seq soft-clips.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Mitochondrial RNA processing directed by the transfer ribonucleic acid (tRNA) punctuation model is essential for function and linked to human diseases. Strand-specific RNA sequencing can capture cleavage intermediates as reads with soft-clipping (unmapped sequences at read ends), but these signatures lack systematic characterization, limiting reliable cleavage site identification. We analyzed strand-specific RNA-seq data from 54 samples (35 private, 19 public) encompassing two library types. Soft-clipped reads were evaluated for frequency, quality, guanine-cytosine (GC) content, and fragment size, with sequence-level analysis of clipped portions. We compared random versus non-random priming across 10 sample pairs and assessed alignment strategies. Leveraging multiple features, we developed a random forest model to identify high-confidence cleavage sites and applied it to 20 hepatocellular carcinoma samples. Soft-clipping was prevalent in both library types but significantly higher in second-strand-specific libraries (P < 0.0001), independent of quality metrics. Soft-clipped sequences were predominantly 1-6 nt (87.9%-97.0%), guanine-rich, and preferentially at 3' ends (84.9%-93.8%). Random priming drove high-level 3' soft-clipping on both H-strand (54.47%) and L-strand (28.07%) transcripts, while non-random primers yielded minimal levels (<1.5%). Allowing soft-clipping during alignment increased sequencing depth and precision (P < 0.0001). The random forest model achieved excellent performance (F1 > 0.85, area under the curve > 0.90), with 1-2 nt soft-clips providing the highest signal-to-noise ratio. This first systematic characterization of soft-clipping in mitochondrial RNA-seq establishes a high-fidelity, machine-learning-based workflow for identifying cleavage sites, offering an accessible tool to advance studies of mitochondrial post-transcriptional regulation.
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