Modtector: ultra-fast modification signal mining on mapped sequencing reads
Year: 2026
Type: article
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).
Source: Bioinformatics
Authors: Tong Zhou, Yifan Hong, Panfeng Li, Xitong Liu, Jilin Zhang, Xianwei Wang, Ang Li, Lei Sun
Institutions: Shandong University, City University of Hong Kong
Language: English
Topic: RNA modifications and cancer
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,989
OpenAlex ID: W4415053013
