modtector Documentation

Welcome to modtector, a high-performance RNA modification detection tool built with Rust.

Welcome

Welcome to the modtector documentation! This is your starting point for learning about our high-performance RNA modification detection tool.

Getting Started

• New to modtector? Start with the Overview to understand the tool’s capabilities and browse the documentation structure

• Need installation help? Check the Installation Guide

• Ready to get started? Follow the Quick Start Guide

Quick Links

• Installation - Detailed installation instructions

• Quick Start Guide - Get started with modtector

• User Guide - Comprehensive usage guide

• Command Reference - Complete command documentation

• Examples - Practical examples and tutorials

• Troubleshooting - Common issues and solutions

• Contributing - How to contribute to modtector

Workflow Overview

modtector provides a complete workflow from raw data to evaluation results:

1. Data Input: BAM files (modified/unmodified samples), FASTA reference sequences, secondary structure files

2. Statistical Analysis: Pileup traversal, counting stop and mutation signals

3. Data Normalization: Signal filtering, outlier handling, background correction

4. Comparative Analysis: Modified vs unmodified sample comparison, identifying modification sites

5. Reactivity Calculation: Calculate signal differences, generate reactivity data

6. Duet Analysis: Sliding-window inference of dynamic ensembles from normalized reactivity and read-level co-variation

7. Visualization: Generate signal distribution plots and reactivity plots

8. Accuracy Assessment: Performance evaluation based on secondary structure

Version Information

• Current Version: v0.15.4

• Release Date: 2026-02-08

• License: MIT License

Project Links

• GitHub Repository: https://github.com/TongZhou2017/modtector

• Crates.io: https://crates.io/crates/modtector

• Documentation: https://modtector.readthedocs.io/

Recent Highlights

• PCR Bias Correction: Chi-Square distribution-based depth correction (correct command)

• Base Quality Filtering: Per-base quality filtering for mutation detection (--min-base-qual)

• Extended Format Support: Support for multiple input formats in convert command (rf-rctools, shapemapper2, icSHAPE-rt, bedgraph, etc.)

• Zarringhalam Remap Improvements: Uses actual maximum values instead of fixed 1.0 for better high-value region mapping

• Distribution-Based K-Factor Prediction: Advanced k-factor prediction method using statistical distribution analysis

• Batch Processing: Process multiple BAM files sequentially with glob patterns

• Single-cell Unified Processing: Unified processing with cell label extraction for 2-3x performance improvement

Example Dataset

To help you quickly evaluate modtector, we provide a minimal example dataset available at Zenodo (10.5281/zenodo.17316476). This dataset contains a small subset of data that can be processed quickly to demonstrate modtector’s functionality.

Quick Start with Example Data

1. Download and extract the example dataset:

   wget https://zenodo.org/record/17316476/files/modtector_example_dataset.zip
   unzip modtector_example_dataset.zip
   cd modtector_example_dataset
   

2. Run the example script:

   bash test_modtector_v0.10.0.sh
   

This will execute a complete workflow on the example data, including:

• Pileup processing of BAM files

• Reactivity calculation with multiple methods

• Data normalization

• Duet sliding-window ensemble decomposition using normalized reactivity and BAM co-variation

• Visualization generation

• Accuracy evaluation

The results will be organized in the signal_v0.5.6/ directory with subdirectories for each processing step.

Support

For questions, issues, or contributions, please refer to the Contributing Guide or open an issue on the GitHub repository.

Citation

If you use modtector in your research, please cite:

[Add citation information when available]