Modtector: ultra-fast modification signal mining on mapped sequencing reads

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Authors: Tong Zhou, Yifan Hong, Pei Li, X B Liu, Jilin Zhang, Xianwei Wang, Ang Li, Lei Sun
Cited by: 0
References: 21

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).

Fields of Study & Subjects

Algorithms and Data Compression Natural Language Processing Techniques Network Packet Processing and Optimization

Publication details

Journal / source
Bioinformatics
Volume
42
Issue
9
Publication date
19 August 2026
Language
EN