ModTector

ModTector is a high-performance RNA modification detection tool developed in Rust, supporting multi-signal analysis, data normalization, reactivity calculation, and accuracy assessment. Based on pileup analysis, it can detect RNA modification sites from BAM-format sequencing data and generate rich visualization results.

🚀 Key Features

Multi-signal Analysis: Simultaneous analysis of stop signals (pipeline truncation) and mutation signals (base mutations)
High-performance Pileup: Robust pileup traversal based on htslib, supporting large file processing
Batch Processing: Process multiple BAM files sequentially with glob pattern matching
Single-cell Unified Processing: Unified processing strategy for single-cell data with cell label extraction and splitting
Data Distribution Optimization: Smart data distribution scanning for efficient processing
Data Normalization: Signal validity filtering and outlier handling
Reactivity Calculation: Calculate signal differences between modified and unmodified samples with multi-threading support
Accuracy Assessment: AUC, F1-score and other metrics evaluation based on secondary structure with auto-shift correction
Rich Visualization: Signal distribution plots, reactivity plots, ROC/PR curves, and RNA structure SVG plots
Multi-threading: Parallel processing and plotting for improved efficiency
Complete Workflow: One-stop solution from raw data to final evaluation
Base Matching: Intelligent base matching algorithm handling T/U equivalence
Auto-alignment: Auto-shift correction for sequence length differences

🔄 Complete Workflow

The following diagram shows the complete ModTector workflow from raw data to evaluation results:

ModTector provides a complete workflow from raw BAM data to final evaluation results:

Data Input: BAM files (modified/unmodified samples), FASTA reference sequences, secondary structure files
Statistical Analysis: Pileup traversal, counting stop and mutation signals
Reactivity Calculation: Calculate signal differences between modified and unmodified samples, generate reactivity data
Data Normalization: Normalize reactivity signals, signal filtering, outlier handling, background correction
Comparative Analysis: Compare modified vs unmodified samples, identify differential modification sites
Visualization: Generate signal distribution plots, reactivity plots, and RNA structure SVG plots
Accuracy Assessment: Performance evaluation based on secondary structure

🔧 Installation

System Requirements

Operating System: Linux, macOS, or Windows
Memory: 4 GB RAM (8 GB recommended for large datasets)
Storage: 2 GB free space
CPU: Multi-core processor recommended for parallel processing

Required Dependencies

Rust: Version 1.70 or higher
Cargo: Included with Rust installation

Installing Rust

Linux/macOS

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source ~/.cargo/env

Windows

Download and run the rustup installer from https://rustup.rs/

Installing ModTector

From crates.io (Recommended)

cargo install modtector

From Source

git clone https://github.com/TongZhou2017/ModTector.git
cd ModTector
cargo build --release

System Dependencies

ModTector requires the following system libraries:

Linux (Ubuntu/Debian)

sudo apt-get update
sudo apt-get install build-essential pkg-config libssl-dev libhts-dev

Linux (CentOS/RHEL)

sudo yum groupinstall "Development Tools"
sudo yum install pkgconfig openssl-devel htslib-devel

macOS

xcode-select --install
brew install htslib

Rust Dependencies

ModTector uses the following Rust crates:

[dependencies]
rust-htslib = "0.47.0"    # BAM file processing
bio = "2.0.1"             # Bioinformatics utilities
plotters = { version = "0.3", features = ["svg_backend", "bitmap_backend"] }  # Plotting and visualization
csv = "1.3"               # CSV file handling
rayon = "1.8"             # Parallel processing
clap = { version = "4.5", features = ["derive"] }  # Command-line argument parsing
chrono = "0.4"            # Date and time handling
rand = "0.8"              # Random number generation
quick-xml = "0.36"        # XML/SVG parsing
regex = "1.11"            # Regular expressions

📋 Command Line Usage

Quick Start

# Generate pileup data from BAM files
modtector count -b sample.bam -f reference.fa -o output.csv

# Calculate reactivity scores
modtector reactivity -M mod_sample.csv -U unmod_sample.csv -O reactivity.csv
# (Optional) For workflows without an unmodified control, omit -U to run in mod-only mode
# modtector reactivity -M smartshape_mod.csv -O reactivity.csv

# Normalize reactivity signals
modtector norm -i reactivity.csv -o normalized_reactivity.csv -m winsor90

# Compare samples and identify differences
modtector compare -m mod_sample.csv -u unmod_sample.csv -o comparison.csv

# Generate visualizations
modtector plot -M mod_sample.csv -U unmod_sample.csv -o plots/ -r normalized_reactivity.csv

# Generate RNA structure SVG plots
modtector plot -o svg_output/ --svg-template rna_structure.svg --reactivity normalized_reactivity.csv

# Evaluate accuracy
modtector evaluate -r normalized_reactivity.csv -s structure.dp -o results/ -g gene_id

1. Count - Data Processing

Generate pileup data from BAM files by counting stop and mutation signals.

modtector count [OPTIONS] --bam <BAM> --fasta <FASTA> --output <OUTPUT>

Parameters:

-b, --bam: Input BAM file path (must be sorted and indexed), or glob pattern for batch/single-cell mode
-f, --fasta: Reference FASTA file path
-o, --output: Output CSV file path (or directory for batch/single-cell mode)
-s, --strand: Optional strand filter (+, -, or +/-; default +/-)
-t, --threads: Number of parallel threads (default: 1)
-w, --window: Window size for genome segmentation in bases (optional)
-l, --log: Log file path (optional)
--batch: Enable batch mode for sequential file processing (use glob pattern)
--single-cell: Enable single-cell unified processing mode (use glob pattern, unified processing with cell labels)

Input File Requirements:

BAM files: Must be sorted and indexed. Use samtools for sorting and indexing:
```
samtools sort input.bam -o sorted.bam
samtools index sorted.bam
```
FASTA files: Reference genome sequence file, sequence IDs must match chromosome names in BAM file

Example:

# Standard mode: process single BAM file
modtector count \
  -b data/mod_sample.bam \
  -f data/reference.fa \
  -o result/mod.csv \
  -t 8

# Batch mode: process multiple BAM files sequentially
modtector count --batch \
  -b "/path/to/bam/*sort.bam" \
  -f data/reference.fa \
  -o result/batch_output/ \
  -t 8 \
  -w 10000

# Single-cell unified mode: unified processing with cell labels
modtector count --single-cell \
  -b "/path/to/single_cell/*sort.bam" \
  -f data/reference.fa \
  -o result/single_cell_output/ \
  -t 8 \
  -w 10000

Batch and Single-cell Modes:

Batch mode (--batch): Processes multiple BAM files sequentially, each file independently. Suitable for scenarios where files need separate processing.
Single-cell unified mode (--single-cell): True unified processing strategy that:
- Reads all BAM files at once for each window
- Collects all reads from all files, unified pileup processing
- Tracks cell labels during processing, splits results by cell
- Provides 2-3x performance improvement by:
  - Skipping data distribution scanning (direct processing of all reference sequences)
  - Unified read collection and pileup (reduces I/O overhead)
  - Better parallelization efficiency (cross-file parallel processing)
  - Window-based memory management (prevents memory accumulation)

Cell Label Extraction:

Automatically extracts cell labels from BAM filenames
Supports RHX pattern (e.g., *RHX672.sort.bam → RHX672)
Falls back to last underscore-separated part if RHX not found
Example: in_vivo_single_cell_Mut_transfected_RNAs_in_HEK293T_DMSO_RHX672.sort.bam → RHX672

Progress Reporting:

Real-time progress display: chunks processed, percentage, processing speed
Estimated time remaining (ETA) based on current processing speed
CPU usage recommendations based on BAM file count
Automatic suggestions for optimal thread count

Output Format (CSV):

ChrID,Strand,Position,RefBase,StopCount,MutationCount,Depth,InsertionCount,DeletionCount,BaseA,BaseC,BaseG,BaseT

2. Reactivity - Reactivity Calculation

Calculate reactivity scores by comparing modified and unmodified samples.

modtector reactivity [OPTIONS] --mod-csv <MOD_CSV> --unmod-csv <UNMOD_CSV> --output <OUTPUT>

Parameters:

-M, --mod-csv: Modified sample CSV
-U, --unmod-csv: Unmodified sample CSV (optional, omit for mod-only mode)
-O, --output: Output reactivity file
-s, --stop-method: Stop signal method (kfactor, ding, rouskin; default: kfactor)
-m, --mutation-method: Mutation signal method (kfactor, siegfried, zubradt; default: kfactor)
-t, --threads: Number of parallel threads (default: 8)
--pseudocount: Pseudocount parameter for Ding method (default: 1.0)
--maxscore: Maximum score limit for Ding method (default: 10.0)
--snp-cutoff: SNP threshold for filtering positions (default: 0.25)
--k-prediction-method: K-factor prediction method (background, distribution, recursive; default: background)
--structure-file: Reference secondary structure file (required for recursive method)
--k-background-gene-id: Gene ID for k-factor background region selection

Stop Signal Methods:

kfactor: k-factor correction method (default)
ding: Ding et al., 2014 logarithmic processing method
rouskin: Uses only modified sample stop signal

Mutation Signal Methods:

kfactor: k-factor correction method (default)
siegfried: Siegfried method for mutation signal processing
zubradt: Uses only modified sample mutation signal

Example:

# Using default method
modtector reactivity \
  -M result/mod.csv \
  -U result/unmod.csv \
  -O result/reactivity.csv \
  -t 24

# Using Ding method for stop signal
modtector reactivity \
  -M result/mod.csv \
  -U result/unmod.csv \
  -O result/reactivity.csv \
  -t 24 \
  -s ding \
  --pseudocount 1.0 \
  --maxscore 10.0

Output Format (CSV):

ChrID,Strand,Position,RefBase,Reactivity

3. Norm - Data Normalization

Normalize and filter signals to remove noise and outliers.

modtector norm [OPTIONS] --input <INPUT> --output <OUTPUT>

Parameters:

-i, --input: Input reactivity CSV file (output from modtector reactivity)
-o, --output: Output normalized CSV file
-m, --method: Normalization method (winsor90, percentile28, boxplot)
--bases: Target bases for analysis (e.g., AC for A and C bases)
--coverage-threshold: Coverage threshold (default: 0.2)
--depth-threshold: Depth threshold (default: 50)
--linear: Apply Zarringhalam piecewise linear mapping

Example:

modtector norm \
  -i result/reactivity.csv \
  -o result/reactivity_norm.csv \
  -m winsor90 \
  --bases AC \
  --coverage-threshold 0.2 \
  --depth-threshold 50

4. Compare - Sample Comparison

Compare modified and unmodified samples to identify differential modification sites.

modtector compare [OPTIONS] --mod-csv <MOD_CSV> --unmod-csv <UNMOD_CSV> --output <OUTPUT>

Parameters:

-m, --mod-csv: Modified sample CSV
-u, --unmod-csv: Unmodified sample CSV
-o, --output: Output comparison CSV file
--min-depth: Minimum depth threshold (default: 10)
--min-fold: Minimum fold change threshold (default: 2.0)

Example:

modtector compare \
  -m result/mod.csv \
  -u result/unmod.csv \
  -o result/comparison.csv \
  --min-depth 10 \
  --min-fold 2.0

Output Format (CSV):

ChrID,Strand,Position,RefBase,ModStop,ModMutation,ModDepth,UnmodStop,UnmodMutation,UnmodDepth,StopFoldChange,MutationFoldChange,ModificationScore

5. Plot - Visualization

Generate visualization plots including signal distributions and RNA structure SVG plots.

modtector plot [OPTIONS] --mod-csv <MOD_CSV> --unmod-csv <UNMOD_CSV> --output <OUTPUT>

Parameters:

-M, --mod-csv: Modified sample CSV (optional for SVG-only mode)
-U, --unmod-csv: Unmodified sample CSV (optional for SVG-only mode)
-o, --output: Output directory
-r, --reactivity: Reactivity file (optional)
-t, --threads: Number of parallel threads (default: 8)
--coverage-threshold: Coverage threshold (default: 0.2)
--depth-threshold: Depth threshold (default: 50)

SVG Plotting Options:

--svg-template: SVG template file path
--svg-bases: Bases to plot (default: ATGC)
--svg-signal: Signal type to plot (stop, mutation, all)
--svg-strand: Strand to include (+, -, both)
--svg-ref: Reference sequence file for alignment
--svg-max-shift: Maximum shift for alignment (default: 50)

Example:

# Regular plotting
modtector plot \
  -M result/mod.csv \
  -U result/unmod.csv \
  -o result/plots \
  -r result/reactivity.csv \
  -t 8

# SVG-only mode
modtector plot \
  -o svg_output/ \
  --svg-template rna_structure.svg \
  --reactivity result/reactivity.csv \
  --svg-signal all \
  --svg-strand +

# Combined mode (regular + SVG)
modtector plot \
  -M result/mod.csv \
  -U result/unmod.csv \
  -o result/plots \
  --svg-template rna_structure.svg \
  --reactivity result/reactivity.csv

6. Evaluate - Accuracy Assessment

Evaluate the accuracy of results using known secondary structure.

modtector evaluate [OPTIONS] --reactivity-file <REACTIVITY_FILE> --structure-file <STRUCTURE_FILE> --output-dir <OUTPUT_DIR>

Parameters:

-r, --reactivity-file: Reactivity CSV file (stop or mutation type)
-s, --structure-file: Secondary structure file (.dp format)
-o, --output-dir: Output directory
-g, --gene-id: Gene ID (optional, for base matching)
-S, --strand: Strand information (optional, default: +)
--signal-type: Signal type (stop or mutation)

Example:

# Evaluate stop signal accuracy
modtector evaluate \
  --reactivity-file result/reactivity_stop.csv \
  --structure-file data/structure.dp \
  --output-dir result/eval \
  --signal-type stop \
  --gene-id gene_id

# Evaluate mutation signal accuracy
modtector evaluate \
  --reactivity-file result/reactivity_mutation.csv \
  --structure-file data/structure.dp \
  --output-dir result/eval \
  --signal-type mutation \
  --gene-id gene_id

Evaluation Metrics:

AUC: ROC curve area under curve (0.5-1.0, higher is better)
F1-score: Harmonic mean of precision and recall (0-1, higher is better)
Sensitivity: True positive rate (0-1, higher is better)
Specificity: True negative rate (0-1, higher is better)
Precision: Positive predictive value (0-1, higher is better)
Recall: True positive rate (0-1, higher is better)
Accuracy: Proportion of correct classifications (0-1, higher is better)

📊 Representative Visualization Results

The following shows typical visualization results generated by ModTector:

Overall Signal Distribution Scatter Plot: Shows signal comparison between modified and unmodified groups at all positions

ROC Curve Evaluation Results: Accuracy assessment based on 16S rRNA secondary structure

Stop Signal ROC Curve	Mutation Signal ROC Curve

Signal Distribution Plots: Display stop and mutation signal distribution across gene positions

Stop Signal Distribution	Mutation Signal Distribution

Reactivity Plots: Bar charts showing reactivity intensity of modification sites

Stop Signal Reactivity	Mutation Signal Reactivity

🧪 Example Dataset

To help you get started quickly, we provide a minimal example dataset that can be used to test ModTector functionality. This dataset is available at Zenodo (10.5281/zenodo.17316476).

Download and Setup

Download the example dataset from Zenodo:

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

Run the example script:
```
bash test_modtector_v0.10.0.sh
```

This script will run a complete ModTector workflow on a small example dataset, demonstrating all major functionality including pileup processing, reactivity calculation, normalization, visualization, and evaluation.

Expected Results

After running the example script, you will see output files in the signal_v0.5.6/ directory, organized by processing step:

01_count/ - Pileup count results
02_reactivity/ - Reactivity calculation results
03_norm/ - Normalized reactivity data
04_plot/ - Visualization plots
05_evaluate/ - Accuracy evaluation results
logs/ - Processing logs

📈 Performance Optimization

Multi-threading Support: Reactivity and plot commands support multi-threaded parallel processing
Memory Optimization: Pileup analysis uses stream processing to reduce memory consumption
Boundary Checking: Enhanced data validity validation for improved stability
Progress Reporting: Detailed processing progress, timing reports, and statistical summaries (dual-signal reads, high-confidence positions)

⚠️ Important Notes

Ensure BAM files are properly sorted and indexed
Sequence IDs in FASTA files must match chromosome names in BAM files
Use multi-threading parameters when processing large files
Evaluation function only analyzes genes with secondary structure information
Recommend using sufficient memory for processing large datasets

🔬 Technical Features

Based on stone project: Added complete workflow on top of the stone project
Rust High Performance: Leverages Rust's memory safety and concurrency features
htslib Integration: Uses mature htslib library for BAM file processing
Modular Design: Independent functional modules for easy maintenance and extension

📄 License

This project is licensed under the MIT License.

🤝 Contributing

Issues and Pull Requests are welcome to improve ModTector.

📚 Documentation

Installation Guide - Detailed installation instructions
Quick Start Guide - Get started in minutes
User Guide - Comprehensive usage guide
Command Reference - Complete command documentation
Examples - Practical examples and tutorials
Troubleshooting - Common issues and solutions

📞 Contact

For questions or suggestions, please contact us through GitHub Issues.

Version Information

Current Version: v0.15.4
Release Date: 2026-02-08
License: MIT

v0.15.4 Bug Fixes (2026-02-08)

Base Mismatch in SVG Metadata Extraction: Fixed critical bug where SVG circle attributes were being matched from adjacent circles
- Replaced context window method with individual circle tag parsing
- Eliminates "Base mismatch" errors in HTML metadata verification
- Ensures RNA_METADATA matches circle data-base attributes correctly
Base Type Extraction from SVG Template: Fixed base type extraction to use reliable template position mapping
- Uses position.label in template: POS.BASE format for accurate base mapping
- Ensures data-base attribute uses SVG template's actual base type

v0.14.10 Changes (2026-02-03)

Zarringhalam Remap Negative Value Handling: Integrated "align min to 0" logic into zarringhalam_remap internal implementation
- Preserves mod/unmod relative differences regardless of normalization workflow
- Handles negative values correctly in Zarringhalam piecewise linear mapping

v0.14.9 Bug Fixes (2026-02-01)

Interactive SVG Right-Click Menu: Fixed style changes not taking effect
Text Element Location: Enhanced text element finding with multiple fallback methods
State Saving: Fixed null reference errors in state saving function
Legend Position: Changed to bottom-right to avoid obscuring RNA structure
Page Loading: Improved loading experience with better loading message
Minimap: Enabled viewport dragging and improved auto-hide behavior
Version Display: Automatically uses version from Cargo.toml

v0.14.8 New Features (2026-01-30)

Bedgraph Format Support: Native Rust implementation for bedgraph file format conversion
- New --format bedgraph option for modtector convert command
- Correctly calculates RT stop count from adjacent position differences
- Replaces Python script-based conversion for better performance

v0.14.7 New Features (2026-01-30)

Native icSHAPE RT Format Support: Implemented icSHAPE-pipe RT file format conversion directly in Rust
- New --format icSHAPE-rt option for modtector convert command
- New --filter-strand parameter for strand filtering
- Automatic format detection and robust error handling

v0.14.6 New Features (2026-01-30)

icSHAPE RT Format Support: Extended convert command to support icSHAPE-pipe RT file format conversion
- Support for converting icSHAPE-pipe RT files to modtector pileup CSV format
- Configurable strand filtering via filter_strand parameter

v0.14.5 New Features (2026-01-30)

Dual Input Mode for modtector convert: Support simultaneous input of mutation and stop signal files
- New --input-mutation and --input-stop parameters
- Merges both files into a single pileup output with both mutation and stop counts

v0.14.4 New Features (2025-12-20)

Quality-Based Effective Depth Calculation: Enhanced depth filtering based on base quality scores
- New effective_depth field in output CSV files when --min-base-qual is enabled
- Quality filtering improves mutation rate calculation accuracy
PCR Bias Correction Method: Chi-Square distribution-based PCR amplification bias correction
- Available in modtector correct command (optional feature)

v0.14.0 New Features (2025-12-15)

Base Quality Filtering: Added per-base quality filtering for mutation detection in modtector count command
- New --min-base-qual parameter (default: 0, recommended: 20)
- Filters mutations by base quality score to improve signal-to-noise ratio
- Filters ~15% of low-quality mutations with min-base-qual=20
- Minimal performance overhead (<1%)
- Integrated into Snakefile workflow

v0.13.0 New Features (2025-11-20)

Distribution-Based K-Factor Prediction: New advanced k-factor prediction method using statistical distribution analysis
- New --k-prediction-method parameter with options: background (default) and distribution
- Significantly improves k prediction accuracy, especially for 2A3 samples
- Average k difference reduced from 0.1758 to 0.1175

v0.12.5 Bug Fixes (2025-11-15)

Critical Normalization Bug Fix: Fixed zarringhalam_remap function to properly handle negative input values
- Now correctly maps all negative values to 0 before applying piecewise linear mapping
- Ensures all output values are in [0, 1] range

v0.12.4 Bug Fixes (2025-11-10)

Critical Evaluation Bug Fix: Fixed incorrect sensitivity and specificity calculation
- Now uses optimal threshold (threshold with highest F1-score) to calculate all metrics
- Fixes evaluation metrics for non-_sf methods

v0.12.3 New Features (2025-11-05)

Input Format Extension: Significantly expanded modtector convert command's input format support
- RNA Framework formats: rf-rctools, rf-norm, rf-norm-xml
- ShapeMapper2 format: shapemapper-profile
- Samtools format: samtools-mpileup
- Multi-level format auto-detection and streaming processing

v0.12.2 Changes (2025-10-30)

Critical Accuracy Improvement: Modified Siegfried method to allow negative reactivity values
- Removed .max(0.0) operation that was forcing all negative values to zero
- Matches Shapemapper's implementation: R_i = T_i - U_i (allowing negatives)
- Expected AUC improvement of ~17.8%

v0.12.1 Bug Fixes (2025-10-25)

Critical Fix: Fixed NaN value handling in evaluate command
- Fixed panic when processing reactivity files containing NaN values
- Properly handles NaN values in sorting and statistical calculations

v0.12.0 New Features (2025-10-20)

Bamreadcount Convert Command: Native Rust implementation for converting bamreadcount format to modtector pileup CSV format
- New convert command: modtector convert -i input.txt -o output.csv -s +
- Replaces Python script with high-performance Rust module
- Streaming processing supports large files (tested up to 35GB)
Bamreadcount Reactivity Support: Added workflow support for reactivity calculation using bamreadcount-derived pileup files

v0.11.1 Bug Fixes (2025-08-15)

Critical Fix: Corrected stop signal position correction for reverse-strand reads
- Forward strand: start_position - 1 (upstream correction)
- Reverse strand: start_position + 1 (downstream correction)
- Ensures accurate genomic position assignment according to theoretical principles

v0.11.0 New Features (2025-08-10)

Batch Processing Mode: Process multiple BAM files sequentially with glob pattern matching
Single-cell Unified Processing: Unified processing strategy for single-cell data with automatic cell label extraction
Performance Optimization: 2-3x speedup for single-cell data processing

Citation

If you use ModTector in your research, please cite:

[Add citation information when available]

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ModTector

🚀 Key Features

🔄 Complete Workflow

🔧 Installation

System Requirements

Required Dependencies

Installing Rust

Linux/macOS

Windows

Installing ModTector

From crates.io (Recommended)

From Source

System Dependencies

Linux (Ubuntu/Debian)

Linux (CentOS/RHEL)

macOS

Rust Dependencies

📋 Command Line Usage

Quick Start

1. Count - Data Processing

2. Reactivity - Reactivity Calculation

3. Norm - Data Normalization

4. Compare - Sample Comparison

5. Plot - Visualization

6. Evaluate - Accuracy Assessment

📊 Representative Visualization Results

🧪 Example Dataset

Download and Setup

Expected Results

📈 Performance Optimization

⚠️ Important Notes

🔬 Technical Features

📄 License

🤝 Contributing

📚 Documentation

📞 Contact

Version Information

v0.15.4 Bug Fixes (2026-02-08)

v0.14.10 Changes (2026-02-03)

v0.14.9 Bug Fixes (2026-02-01)

v0.14.8 New Features (2026-01-30)

v0.14.7 New Features (2026-01-30)

v0.14.6 New Features (2026-01-30)

v0.14.5 New Features (2026-01-30)

v0.14.4 New Features (2025-12-20)

v0.14.0 New Features (2025-12-15)

v0.13.0 New Features (2025-11-20)

v0.12.5 Bug Fixes (2025-11-15)

v0.12.4 Bug Fixes (2025-11-10)

v0.12.3 New Features (2025-11-05)

v0.12.2 Changes (2025-10-30)

v0.12.1 Bug Fixes (2025-10-25)

v0.12.0 New Features (2025-10-20)

v0.11.1 Bug Fixes (2025-08-15)

v0.11.0 New Features (2025-08-10)

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