chimeralm 1.0.3

ChimeraLM: A genomic lanuage model to identify chimera artifact introduced by whole genome amplification (WGA).

ChimeraLM

A Genomic Language Model for Detecting WGA Chimeric Artifacts

Installation • Quick Start • Documentation • Citation

---

A deep learning-powered tool to identify chimeric artifacts introduced by whole genome amplification (WGA).

Installation

pip install chimeralm

Requirements: Python 3.10, 3.11 and 3.12

For GPU support, installation instructions, and troubleshooting, see the Installation Guide.

Quick Start

# Predict chimeric reads (CPU)
chimeralm predict your_data.bam

# Predict with GPU acceleration
chimeralm predict your_data.bam --gpus 1 --batch-size 24

# Filter BAM to remove chimeric reads
chimeralm filter your_data.bam your_data.predictions

Output:

• Predictions: Tab-separated file with read names and labels (0=biological, 1=chimeric)
• Filtered BAM: {input}.filtered.sorted.bam with chimeric reads removed

Need more help? See the Quick Start Tutorial for a complete walkthrough.

Documentation

Full documentation is available at ylab-hi.github.io/ChimeraLM

Key Resources:

• Installation Guide - Setup with pip, conda, uv, or from source
• Quick Start Tutorial - Your first prediction in 15 minutes
• CLI Reference - Complete command documentation
• BAM Filtering Tutorial - Comprehensive filtering guide
• Performance Optimization - Speed up your analysis
• Troubleshooting - Common issues and solutions

Features

• High Accuracy: Deep learning model trained on real WGA data
• GPU Accelerated: Optimized for CUDA, MPS (Apple Silicon), and CPU
• Easy to Use: Simple CLI with sensible defaults
• Fast Processing: Batch inference with configurable parallelism
• Web Interface: Interactive web UI for visualization and analysis
• Production Ready: Includes filtering, sorting, and indexing of BAM files

Contributing

Contributions are welcome! See our Contributing Guide for development setup and guidelines.

Citation

If you use ChimeraLM in your research, please cite:

@software{chimeralm2025,
  title={ChimeraLM: A genomic language model to identify chimera artifacts},
  author={Li, Yangyang and Guo, Qingxiang and Yang, Rendong},
  year={2025},
  url={https://github.com/ylab-hi/ChimeraLM}
}

License

Apache License 2.0 - see LICENSE for details.