Modtector: Ultra-Fast Modification Signal Mining on Mapped Sequencing Reads

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ID: 325853
2026
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Abstract
SUMMARY: Existing tools for RNA epitranscriptomic modification and structural signal analysis are often fragmented, inefficiency, and limited to single signal types. We developed Modtector, an unified tool for extracting mutation and reverse-transcription stop signals from aligned sequencing reads. By using a "count-then-correct" strategy, Modtector reduces computational complexity and enables efficient dual-signal analysis. It achieves multi-fold speedups on large-genome and high-coverage datasets, including completing HEK293 22G data analysis in 5 minutes, and show strong scalability on single-cell datasets with speedups exceeding 50-fold. AVAILABILITY: The source code is available at GitHub (https://github.com/TongZhou2017/modtector) and Crates.io (https://crates.io/crates/modtector). The archived source-code snapshot used in this study is available at Zenodo (DOI: 10.5281/zenodo.20967747), corresponding to GitHub commit 7c60e9d. Workflow examples, datasets, and analysis scripts are available at Zenodo (DOI: 10.5281/zenodo.17316476 and 10.5281/zenodo.18523297). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W4415053013 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tong Zhou, Yifan Hong, Pei Li, X B Liu, Ang Li, Lei Sun, Ang Li, Lei Sun
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag627
URL
https://doi.org/10.1093/bioinformatics/btag627
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