magicc 0.2.1

MAGICC: Metagenome-Assembled Genome Inference of Completeness and Contamination

MAGICC

Metagenome-Assembled Genome Inference of Completeness and Contamination

Accurate and ultra-fast genome quality assessment using core gene k-mer profiles and deep learning.

Installation

pip install magicc

Or from source:

git clone https://github.com/renmaotian/magicc.git
cd magicc
pip install -e .

Note: Git LFS is required to clone the repository (the ONNX model is ~180 MB).

Dependencies

• Python >= 3.8
• numpy >= 1.20
• numba >= 0.53
• scipy >= 1.7
• h5py >= 3.0
• onnxruntime >= 1.10

Usage

# Predict quality for all FASTA files in a directory (uses all CPUs by default)
magicc predict --input /path/to/genomes/ --output predictions.tsv

# Single genome
magicc predict --input genome.fasta --output predictions.tsv

# Specify threads and file extension
magicc predict --input /path/to/genomes/ --output predictions.tsv --threads 8 --extension .fa

Options

magicc predict [OPTIONS]

Required:
  --input, -i       Path to genome FASTA file(s) or directory
  --output, -o      Output TSV file path

Optional:
  --threads, -t     Number of threads (default: 0 = all CPUs)
  --batch-size      Batch size for ONNX inference (default: 64)
  --extension, -x   Genome file extension filter (default: .fasta)
  --model           Path to ONNX model file (auto-downloads if not found)
  --quiet, -q       Suppress progress output
  --verbose, -v     Verbose debug output

Output

Tab-separated file with three columns:

genome_name	pred_completeness	pred_contamination
genome_001	95.2341	2.1567
genome_002	78.4521	15.3421

• pred_completeness: Predicted completeness (%), range [50, 100]
• pred_contamination: Predicted contamination (%), range [0, 100]

Citation

If you use MAGICC in your research, please cite:

Tian, R. (2026). MAGICC: Accurate and ultra-fast genome quality assessment using core gene k-mer profiles and deep learning. In preparation.

License

MIT License. See LICENSE for details.