MitoClipSplice: a machine learning framework for resolving mitochondrial RNA cleavage sites from strand-specific RNA-seq soft-clips.

Authors

Qing Yuan, Yu Li, Fanfan Xie, Xinwei Liu, Zhenni Wang, Zhiyang Xu, Yanlin Lin, Gang Wang, Yang Liu, Jinliang Xing, Kaixiang Zhou

Published in

Briefings in bioinformatics. Volume 27. Issue 4. Jul 03, 2026.

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.

PMID:
42561155
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.

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