# REMAG
[](https://doi.org/10.5281/zenodo.16443991)
**RE**covery of eukaryotic genomes using contrastive learning. A specialized metagenomic binning tool designed for recovering high-quality eukaryotic genomes from mixed prokaryotic-eukaryotic samples.
## Quick Start
### Option 1: Using Conda (Recommended - handles all dependencies)
```bash
# Create environment and install everything
conda create -n remag -c bioconda -c conda-forge remag miniprot
conda activate remag
# Run REMAG
remag -f contigs.fasta -c alignments.bam -o output_directory
```
### Option 2: Using Docker (No local installation needed)
```bash
docker run --rm -v $(pwd):/data danielzmbp/remag:latest \
-f /data/contigs.fasta -c /data/alignments.bam -o /data/output
```
### Option 3: Using pip
```bash
# Create environment first
conda create -n remag python=3.9
conda activate remag
# Install dependencies and REMAG
conda install -c bioconda miniprot
pip install remag
# Run REMAG
remag -f contigs.fasta -c alignments.bam -o output_directory
```
## Installation
### Recommended: Conda Installation
This is the easiest method as conda handles all dependencies automatically:
```bash
# Create a new environment with all dependencies
conda create -n remag -c bioconda -c conda-forge remag miniprot
conda activate remag
# Verify installation
remag --help
```
### Alternative: PyPI Installation
If you prefer pip, you'll need to install the external dependency separately:
```bash
# Step 1: Create and activate environment
conda create -n remag python=3.9
conda activate remag
# Step 2: Install external dependency
conda install -c bioconda miniprot
# Step 3: Install REMAG from PyPI
pip install remag
```
### Advanced Conda Setup
For additional features:
```bash
# Basic installation
conda create -n remag -c bioconda remag miniprot
conda activate remag
# Add optional plotting capabilities
conda install -c conda-forge matplotlib umap-learn
```
**Note**: Bioconda installs only the core dependencies. Optional features (plotting, GPU acceleration) must be installed separately using conda or pip extras.
### Using Docker
```bash
# Pull and run the latest version
docker run --rm -v $(pwd):/data danielzmbp/remag:latest \
-f /data/contigs.fasta -c /data/alignments.bam -o /data/output
# Or use a specific version
docker run --rm -v $(pwd):/data danielzmbp/remag:0.2.2 \
-f /data/contigs.fasta -c /data/alignments.bam -o /data/output
# For interactive use
docker run -it --rm -v $(pwd):/data danielzmbp/remag:latest /bin/bash
```
### Using Singularity
```bash
# Pull and run the latest version directly
singularity run docker://danielzmbp/remag:latest \
-f contigs.fasta -c alignments.bam -o output_directory
# Build Singularity image from Docker Hub
singularity build remag_v0.2.2.sif docker://danielzmbp/remag:v0.2.2
# Or build latest version
singularity build remag_latest.sif docker://danielzmbp/remag:latest
# Run with Singularity
singularity run --bind $(pwd):/data remag_v0.2.2.sif \
-f /data/contigs.fasta -c /data/alignments.bam -o /data/output
# Or use exec for direct command execution
singularity exec --bind $(pwd):/data remag_v0.2.2.sif \
remag -f /data/contigs.fasta -c /data/alignments.bam -o /data/output
# For interactive shell
singularity shell --bind $(pwd):/data remag_v0.2.2.sif
# Build a local Singularity image file (optional)
singularity build remag.sif docker://danielzmbp/remag:latest
singularity run remag.sif -f contigs.fasta -c alignments.bam -o output_directory
```
### From source
```bash
# Create and activate conda environment
conda create -n remag python=3.9
conda activate remag
# Clone and install
git clone https://github.com/danielzmbp/remag.git
cd remag
pip install .
```
### Development installation
For contributors and developers:
```bash
# Install with development dependencies
pip install -e ".[dev]"
```
### Optional Features Installation
For visualization capabilities:
```bash
# Install with plotting dependencies
pip install "remag[plotting]"
```
## Usage
### Command line interface
After installation, you can use REMAG via the command line:
```bash
remag -f contigs.fasta -c alignments.bam -o output_directory
```
### Python module mode
```bash
python -m remag -f contigs.fasta -c alignments.bam -o output_directory
```
## How REMAG Works
REMAG uses a sophisticated multi-stage pipeline specifically designed for eukaryotic genome recovery:
1. **Bacterial Pre-filtering**: By default, REMAG automatically filters out bacterial contigs using the integrated 4CAC classifier (can be disabled with `--skip-bacterial-filter`)
2. **Feature Extraction**: Combines k-mer composition (4-mers) with coverage profiles across multiple samples. Large contigs are split into overlapping fragments for augmentation during training
3. **Contrastive Learning**: Trains a Siamese neural network using the Barlow Twins self-supervised loss function. This creates embeddings where fragments from the same contig are close together
4. **Clustering**: Graph-based Leiden clustering on the learned contig embeddings to form bins
5. **Quality Assessment**: Uses miniprot to align bins against a database of eukaryotic core genes to detect contamination
6. **Iterative Refinement**: Automatically splits contaminated bins based on core gene duplications to improve bin quality
## Key Features
- **Automatic Bacterial Filtering**: The 4CAC classifier automatically identifies and removes bacterial sequences before binning
- **Multi-Sample Support**: Can process coverage information from multiple samples (BAM/CRAM files) simultaneously
- **Barlow Twins Loss**: Uses a self-supervised contrastive learning approach that doesn't require negative pairs
- **Fragment Augmentation**: Large contigs are split into multiple overlapping fragments during training to improve representation learning
## Options
```
-f, --fasta PATH Input FASTA file with contigs to bin. Can be gzipped. [required]
-c, --coverage PATH Coverage files for calculation. Supports BAM, CRAM (indexed), and TSV formats. Auto-detects format by extension. Each file represents one sample. Supports space-separated paths and glob patterns (e.g., "*.bam", "*.cram", "*.tsv"). Use quotes around glob patterns.
-o, --output PATH Output directory for results. [required]
--epochs INTEGER RANGE Training epochs for neural network. [default: 400; 20<=x<=2000]
--batch-size INTEGER RANGE Batch size for training. [default: 2048; 16<=x<=8192]
--embedding-dim INTEGER RANGE Embedding dimension for contrastive learning. [default: 256; 64<=x<=512]
--base-learning-rate FLOAT RANGE
Base learning rate for contrastive learning training (scaled by batch size). [default: 0.008; 0.00001<=x<=0.1]
--min-cluster-size INTEGER RANGE
Minimum number of contigs required to form a cluster/bin. [default: 2; 2<=x<=100]
--leiden-resolution FLOAT Resolution parameter for Leiden clustering (higher = more clusters). [default: 1.0; 0.1<=x<=5.0]
--leiden-k-neighbors INTEGER Number of nearest neighbors for k-NN graph construction in Leiden clustering. [default: 15; 5<=x<=100]
--leiden-similarity-threshold FLOAT
Minimum cosine similarity threshold for k-NN graph edges in Leiden clustering. [default: 0.1; 0.0<=x<=1.0]
--min-contig-length INTEGER RANGE
Minimum contig length in base pairs for binning consideration. [default: 1000; 500<=x<=10000]
--max-positive-pairs INTEGER RANGE
Maximum number of positive pairs for contrastive learning training. [default: 5000000; 100000<=x<=10000000]
-t, --threads INTEGER RANGE Number of CPU cores to use for parallel processing. [default: 8; 1<=x<=64]
--min-bin-size INTEGER RANGE Minimum total bin size in base pairs for output. [default: 100000; 50000<=x<=10000000]
-v, --verbose Enable verbose logging.
--skip-bacterial-filter Skip bacterial contig filtering (4CAC classifier + contrastive learning).
--skip-refinement Skip bin refinement.
--max-refinement-rounds INTEGER RANGE
Maximum refinement rounds. [default: 2; 1<=x<=10]
--num-augmentations INTEGER RANGE
Number of random fragments per contig. [default: 8; 1<=x<=32]
--keep-intermediate Keep intermediate files (training fragments, etc.).
-h, --help Show this message and exit.
```
## Output
REMAG produces several output files:
### Core output files (always created):
- `bins/`: Directory containing FASTA files for each bin
- `bins.csv`: Final contig-to-bin assignments
- `remag.log`: Detailed log file
- `*_non_bacterial_filtered.fasta`: Filtered FASTA file with bacterial contigs removed (when bacterial filtering is enabled)
### Additional files (with `--keep-intermediate` option):
- `embeddings.csv`: Contig embeddings from the neural network
- `siamese_model.pt`: Trained Siamese neural network model
- `params.json`: Complete run parameters for reproducibility
- `features.csv`: Extracted k-mer and coverage features
- `fragments.pkl`: Fragment information used during training
- `classification_results.csv`: 4CAC bacterial classification results
- `refinement_summary.json`: Summary of the bin refinement process
- `gene_mappings_cache.json`: Cached gene-to-contig mappings for faster refinement
- `core_gene_duplication_results.json`: Core gene duplication analysis from refinement
- `temp_miniprot/`: Temporary directory for miniprot alignments (removed unless --keep-intermediate)
### Visualization (optional, requires plotting dependencies):
To generate UMAP visualization plots:
```bash
# Install plotting dependencies if not already installed
pip install remag[plotting]
# Generate UMAP visualization from embeddings
python scripts/plot_features.py --features output_directory/embeddings.csv --clusters output_directory/bins.csv --output output_directory
```
This creates:
- `umap_coordinates.csv`: UMAP projections for visualization
- `umap_plot.pdf`: UMAP visualization plot with cluster assignments
## Requirements
### Core dependencies (always installed):
- Python 3.8+
- PyTorch (≥1.11.0)
- scikit-learn (≥1.0.0)
- XGBoost (≥1.6.0) - for 4CAC classifier
- leidenalg (≥0.9.0) - for graph-based clustering
- igraph (≥0.10.0) - for graph construction in Leiden clustering
- pandas (≥1.3.0)
- numpy (≥1.21.0)
- pysam (≥0.18.0)
- loguru (≥0.6.0)
- tqdm (≥4.62.0)
- rich-click (≥1.5.0)
- joblib (≥1.1.0)
- psutil (≥5.8.0)
### Optional dependencies:
- **For visualization**: matplotlib (≥3.5.0), umap-learn (≥0.5.0)
- Install with: `pip install remag[plotting]`
The package includes a pre-trained 4CAC classifier model for bacterial contig filtering. The 4CAC classifier code and models are adapted from the [Shamir-Lab/4CAC repository](https://github.com/Shamir-Lab/4CAC).
## Acknowledgments
The integrated 4CAC classifier (`xgbclass` module) is adapted from the work by Shamir Lab:
- **Repository**: [Shamir-Lab/4CAC](https://github.com/Shamir-Lab/4CAC)
- **Paper**: Pu L, Shamir R. 4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs. Nucleic Acids Res. 2024;52(19):e94–e94.
## License
MIT License - see LICENSE file for details.
## Citation
If you use REMAG in your research, please cite:
[](https://doi.org/10.5281/zenodo.16443991)
```bibtex
@software{gomez_perez_2025_remag,
author = {Gómez-Pérez, Daniel},
title = {REMAG: Recovering high-quality Eukaryotic genomes from complex metagenomes},
year = 2025,
publisher = {Zenodo},
doi = {10.5281/zenodo.16443991},
url = {https://doi.org/10.5281/zenodo.16443991}
}
```
Note: The DOI 10.5281/zenodo.16443991 represents all versions and will always resolve to the latest release. A manuscript describing REMAG is in preparation.
Raw data
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"description": "# REMAG\n\n[](https://doi.org/10.5281/zenodo.16443991)\n\n**RE**covery of eukaryotic genomes using contrastive learning. A specialized metagenomic binning tool designed for recovering high-quality eukaryotic genomes from mixed prokaryotic-eukaryotic samples.\n\n## Quick Start\n\n### Option 1: Using Conda (Recommended - handles all dependencies)\n```bash\n# Create environment and install everything\nconda create -n remag -c bioconda -c conda-forge remag miniprot\nconda activate remag\n\n# Run REMAG\nremag -f contigs.fasta -c alignments.bam -o output_directory\n```\n\n### Option 2: Using Docker (No local installation needed)\n```bash\ndocker run --rm -v $(pwd):/data danielzmbp/remag:latest \\\n -f /data/contigs.fasta -c /data/alignments.bam -o /data/output\n```\n\n### Option 3: Using pip\n```bash\n# Create environment first\nconda create -n remag python=3.9\nconda activate remag\n\n# Install dependencies and REMAG\nconda install -c bioconda miniprot\npip install remag\n\n# Run REMAG\nremag -f contigs.fasta -c alignments.bam -o output_directory\n```\n\n## Installation\n\n### Recommended: Conda Installation\n\nThis is the easiest method as conda handles all dependencies automatically:\n\n```bash\n# Create a new environment with all dependencies\nconda create -n remag -c bioconda -c conda-forge remag miniprot\nconda activate remag\n\n# Verify installation\nremag --help\n```\n\n### Alternative: PyPI Installation\n\nIf you prefer pip, you'll need to install the external dependency separately:\n\n```bash\n# Step 1: Create and activate environment\nconda create -n remag python=3.9\nconda activate remag\n\n# Step 2: Install external dependency\nconda install -c bioconda miniprot\n\n# Step 3: Install REMAG from PyPI\npip install remag\n```\n\n### Advanced Conda Setup\n\nFor additional features:\n\n```bash\n# Basic installation\nconda create -n remag -c bioconda remag miniprot\nconda activate remag\n\n# Add optional plotting capabilities\nconda install -c conda-forge matplotlib umap-learn\n```\n\n**Note**: Bioconda installs only the core dependencies. Optional features (plotting, GPU acceleration) must be installed separately using conda or pip extras.\n\n### Using Docker\n\n```bash\n# Pull and run the latest version\ndocker run --rm -v $(pwd):/data danielzmbp/remag:latest \\\n -f /data/contigs.fasta -c /data/alignments.bam -o /data/output\n\n# Or use a specific version\ndocker run --rm -v $(pwd):/data danielzmbp/remag:0.2.2 \\\n -f /data/contigs.fasta -c /data/alignments.bam -o /data/output\n\n# For interactive use\ndocker run -it --rm -v $(pwd):/data danielzmbp/remag:latest /bin/bash\n```\n\n### Using Singularity\n\n```bash\n# Pull and run the latest version directly\nsingularity run docker://danielzmbp/remag:latest \\\n -f contigs.fasta -c alignments.bam -o output_directory\n\n# Build Singularity image from Docker Hub\nsingularity build remag_v0.2.2.sif docker://danielzmbp/remag:v0.2.2\n\n# Or build latest version\nsingularity build remag_latest.sif docker://danielzmbp/remag:latest\n\n# Run with Singularity\nsingularity run --bind $(pwd):/data remag_v0.2.2.sif \\\n -f /data/contigs.fasta -c /data/alignments.bam -o /data/output\n\n# Or use exec for direct command execution\nsingularity exec --bind $(pwd):/data remag_v0.2.2.sif \\\n remag -f /data/contigs.fasta -c /data/alignments.bam -o /data/output\n\n# For interactive shell\nsingularity shell --bind $(pwd):/data remag_v0.2.2.sif\n\n# Build a local Singularity image file (optional)\nsingularity build remag.sif docker://danielzmbp/remag:latest\nsingularity run remag.sif -f contigs.fasta -c alignments.bam -o output_directory\n```\n\n### From source\n\n```bash\n# Create and activate conda environment\nconda create -n remag python=3.9\nconda activate remag\n\n# Clone and install\ngit clone https://github.com/danielzmbp/remag.git\ncd remag\npip install .\n```\n\n### Development installation\n\nFor contributors and developers:\n\n```bash\n# Install with development dependencies\npip install -e \".[dev]\"\n```\n\n### Optional Features Installation\n\nFor visualization capabilities:\n\n```bash\n# Install with plotting dependencies\npip install \"remag[plotting]\"\n```\n\n\n## Usage\n\n### Command line interface\n\nAfter installation, you can use REMAG via the command line:\n\n```bash\nremag -f contigs.fasta -c alignments.bam -o output_directory\n```\n\n### Python module mode\n\n```bash\npython -m remag -f contigs.fasta -c alignments.bam -o output_directory\n```\n\n## How REMAG Works\n\nREMAG uses a sophisticated multi-stage pipeline specifically designed for eukaryotic genome recovery:\n\n1. **Bacterial Pre-filtering**: By default, REMAG automatically filters out bacterial contigs using the integrated 4CAC classifier (can be disabled with `--skip-bacterial-filter`)\n2. **Feature Extraction**: Combines k-mer composition (4-mers) with coverage profiles across multiple samples. Large contigs are split into overlapping fragments for augmentation during training\n3. **Contrastive Learning**: Trains a Siamese neural network using the Barlow Twins self-supervised loss function. This creates embeddings where fragments from the same contig are close together\n4. **Clustering**: Graph-based Leiden clustering on the learned contig embeddings to form bins\n5. **Quality Assessment**: Uses miniprot to align bins against a database of eukaryotic core genes to detect contamination\n6. **Iterative Refinement**: Automatically splits contaminated bins based on core gene duplications to improve bin quality\n\n## Key Features\n\n- **Automatic Bacterial Filtering**: The 4CAC classifier automatically identifies and removes bacterial sequences before binning\n- **Multi-Sample Support**: Can process coverage information from multiple samples (BAM/CRAM files) simultaneously\n- **Barlow Twins Loss**: Uses a self-supervised contrastive learning approach that doesn't require negative pairs\n- **Fragment Augmentation**: Large contigs are split into multiple overlapping fragments during training to improve representation learning\n\n## Options\n\n```\n -f, --fasta PATH Input FASTA file with contigs to bin. Can be gzipped. [required]\n -c, --coverage PATH Coverage files for calculation. Supports BAM, CRAM (indexed), and TSV formats. Auto-detects format by extension. Each file represents one sample. Supports space-separated paths and glob patterns (e.g., \"*.bam\", \"*.cram\", \"*.tsv\"). Use quotes around glob patterns.\n -o, --output PATH Output directory for results. [required]\n --epochs INTEGER RANGE Training epochs for neural network. [default: 400; 20<=x<=2000]\n --batch-size INTEGER RANGE Batch size for training. [default: 2048; 16<=x<=8192]\n --embedding-dim INTEGER RANGE Embedding dimension for contrastive learning. [default: 256; 64<=x<=512]\n --base-learning-rate FLOAT RANGE\n Base learning rate for contrastive learning training (scaled by batch size). [default: 0.008; 0.00001<=x<=0.1]\n --min-cluster-size INTEGER RANGE\n Minimum number of contigs required to form a cluster/bin. [default: 2; 2<=x<=100]\n --leiden-resolution FLOAT Resolution parameter for Leiden clustering (higher = more clusters). [default: 1.0; 0.1<=x<=5.0]\n --leiden-k-neighbors INTEGER Number of nearest neighbors for k-NN graph construction in Leiden clustering. [default: 15; 5<=x<=100]\n --leiden-similarity-threshold FLOAT\n Minimum cosine similarity threshold for k-NN graph edges in Leiden clustering. [default: 0.1; 0.0<=x<=1.0]\n --min-contig-length INTEGER RANGE\n Minimum contig length in base pairs for binning consideration. [default: 1000; 500<=x<=10000]\n --max-positive-pairs INTEGER RANGE\n Maximum number of positive pairs for contrastive learning training. [default: 5000000; 100000<=x<=10000000]\n -t, --threads INTEGER RANGE Number of CPU cores to use for parallel processing. [default: 8; 1<=x<=64]\n --min-bin-size INTEGER RANGE Minimum total bin size in base pairs for output. [default: 100000; 50000<=x<=10000000]\n -v, --verbose Enable verbose logging.\n --skip-bacterial-filter Skip bacterial contig filtering (4CAC classifier + contrastive learning).\n --skip-refinement Skip bin refinement.\n --max-refinement-rounds INTEGER RANGE\n Maximum refinement rounds. [default: 2; 1<=x<=10]\n --num-augmentations INTEGER RANGE\n Number of random fragments per contig. [default: 8; 1<=x<=32]\n --keep-intermediate Keep intermediate files (training fragments, etc.).\n -h, --help Show this message and exit.\n```\n\n## Output\n\nREMAG produces several output files:\n\n### Core output files (always created):\n- `bins/`: Directory containing FASTA files for each bin\n- `bins.csv`: Final contig-to-bin assignments\n- `remag.log`: Detailed log file\n- `*_non_bacterial_filtered.fasta`: Filtered FASTA file with bacterial contigs removed (when bacterial filtering is enabled)\n\n### Additional files (with `--keep-intermediate` option):\n- `embeddings.csv`: Contig embeddings from the neural network\n- `siamese_model.pt`: Trained Siamese neural network model\n- `params.json`: Complete run parameters for reproducibility\n- `features.csv`: Extracted k-mer and coverage features\n- `fragments.pkl`: Fragment information used during training\n- `classification_results.csv`: 4CAC bacterial classification results\n- `refinement_summary.json`: Summary of the bin refinement process\n- `gene_mappings_cache.json`: Cached gene-to-contig mappings for faster refinement\n- `core_gene_duplication_results.json`: Core gene duplication analysis from refinement\n- `temp_miniprot/`: Temporary directory for miniprot alignments (removed unless --keep-intermediate)\n\n### Visualization (optional, requires plotting dependencies):\nTo generate UMAP visualization plots:\n\n```bash\n# Install plotting dependencies if not already installed\npip install remag[plotting]\n\n# Generate UMAP visualization from embeddings\npython scripts/plot_features.py --features output_directory/embeddings.csv --clusters output_directory/bins.csv --output output_directory\n```\n\nThis creates:\n- `umap_coordinates.csv`: UMAP projections for visualization\n- `umap_plot.pdf`: UMAP visualization plot with cluster assignments\n\n\n## Requirements\n\n### Core dependencies (always installed):\n- Python 3.8+\n- PyTorch (\u22651.11.0)\n- scikit-learn (\u22651.0.0)\n- XGBoost (\u22651.6.0) - for 4CAC classifier\n- leidenalg (\u22650.9.0) - for graph-based clustering\n- igraph (\u22650.10.0) - for graph construction in Leiden clustering\n- pandas (\u22651.3.0)\n- numpy (\u22651.21.0)\n- pysam (\u22650.18.0)\n- loguru (\u22650.6.0)\n- tqdm (\u22654.62.0)\n- rich-click (\u22651.5.0)\n- joblib (\u22651.1.0)\n- psutil (\u22655.8.0)\n\n### Optional dependencies:\n- **For visualization**: matplotlib (\u22653.5.0), umap-learn (\u22650.5.0)\n - Install with: `pip install remag[plotting]`\n\nThe package includes a pre-trained 4CAC classifier model for bacterial contig filtering. The 4CAC classifier code and models are adapted from the [Shamir-Lab/4CAC repository](https://github.com/Shamir-Lab/4CAC).\n\n## Acknowledgments\n\nThe integrated 4CAC classifier (`xgbclass` module) is adapted from the work by Shamir Lab:\n\n- **Repository**: [Shamir-Lab/4CAC](https://github.com/Shamir-Lab/4CAC)\n- **Paper**: Pu L, Shamir R. 4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs. Nucleic Acids Res. 2024;52(19):e94\u2013e94.\n \n\n## License\n\nMIT License - see LICENSE file for details.\n\n## Citation\n\nIf you use REMAG in your research, please cite:\n\n[](https://doi.org/10.5281/zenodo.16443991)\n\n```bibtex\n@software{gomez_perez_2025_remag,\n author = {G\u00f3mez-P\u00e9rez, Daniel},\n title = {REMAG: Recovering high-quality Eukaryotic genomes from complex metagenomes},\n year = 2025,\n publisher = {Zenodo},\n doi = {10.5281/zenodo.16443991},\n url = {https://doi.org/10.5281/zenodo.16443991}\n}\n```\n\nNote: The DOI 10.5281/zenodo.16443991 represents all versions and will always resolve to the latest release. A manuscript describing REMAG is in preparation.\n",
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