remag 0.4.3

Recovery of high-quality eukaryotic genomes from complex metagenomes

REMAG

Recovery of Eukaryotic Metagenome-Assembled Genomes using contrastive learning. A specialized metagenomic binning tool designed for recovering high-quality eukaryotic genomes from mixed prokaryotic-eukaryotic samples.

Index

• Installation
• Quick Start
• Usage
• Common Options
• How It Works
• Output
• Requirements
• Acknowledgments
• License
• Citation

Installation

Conda (recommended)

conda create -n remag -c bioconda -c conda-forge remag
conda activate remag

PyPI

Install miniprot separately first:

conda create -n remag python=3.9
conda activate remag
conda install -c bioconda miniprot
pip install remag

From source

conda create -n remag python=3.9
conda activate remag

git clone https://github.com/danielzmbp/remag.git
cd remag
conda install -c bioconda miniprot
pip install .

Development installation

pip install -e ".[dev]"

Docker

docker pull danielzmbp/remag:latest

Optional plotting dependencies

pip install remag[plotting]

GPU acceleration

REMAG uses PyTorch and will use GPU acceleration automatically when a supported backend is available. No extra REMAG flag is required.

Conda with NVIDIA CUDA

If you want a CUDA-enabled PyTorch build, install REMAG first and then replace the CPU PyTorch package with the CUDA-enabled one that matches your system:

conda create -n remag -c bioconda -c conda-forge remag
conda activate remag
conda install -c pytorch -c nvidia pytorch pytorch-cuda=12.1

Adjust the CUDA version to match your driver and platform.

Apple Silicon

On Apple Silicon, PyTorch can use Metal (mps) automatically when available. In most cases no extra REMAG-specific setup is needed beyond installing a current PyTorch build.

PyPI installs

If you install REMAG with pip, install the PyTorch build you want first, then install REMAG:

conda create -n remag python=3.9
conda activate remag
conda install -c bioconda miniprot

# Install the desired PyTorch build first
pip install torch

# Then install REMAG
pip install remag

For NVIDIA systems, use the PyTorch install command from the official PyTorch selector so the wheel matches your CUDA runtime.

Quick Start

Conda

remag contigs.fasta -c alignments.bam

Docker

docker run --rm -v $(pwd):/data danielzmbp/remag:latest \
  /data/contigs.fasta -c /data/alignments.bam -o /data/output

Singularity

singularity build remag.sif docker://danielzmbp/remag:latest
singularity run --bind $(pwd):/data remag.sif \
  /data/contigs.fasta -c /data/alignments.bam -o /data/output

Usage

Command line interface

After installation, you can use REMAG via the command line:

# Basic usage
remag contigs.fasta -c alignments.bam

# With explicit output directory
remag contigs.fasta -c alignments.bam -o output_directory

# Multiple samples
remag contigs.fasta -c sample1.bam -c sample2.bam

# Multiple samples using shell-expanded globs
remag contigs.fasta -c samples/*.bam

# Using precomputed coverage tables (one TSV per sample)
remag contigs.fasta -c sample1.tsv -c sample2.tsv

# Only run eukaryotic filtering (skip binning)
remag contigs.fasta --filter-only

# Use single-cell mode (adjusts k-NN defaults and skips eukaryotic filtering)
remag contigs.fasta -c alignments.bam -m single-cell

# Keep intermediate files
remag contigs.fasta -c alignments.bam -k

Python module mode

python -m remag contigs.fasta -c alignments.bam

Coverage TSV format

Precomputed coverage TSVs are supported as an alternative to BAM/CRAM. Use one TSV per sample.

• Column 1: contig ID
• Last column: coverage value for that contig
• No header row

Example:

contig_1	12.4
contig_2	3.8
contig_3	0.0

TSV input provides contig-level coverage only. REMAG cannot infer fragment-specific coverage for augmented fragments from a TSV, so every fragment from the same contig gets the same coverage value. Use BAM/CRAM if you want fragment-level augmented coverage features. Do not mix TSV inputs with BAM/CRAM inputs in the same run.

Common Options

• -c, --coverage: one or more BAM, CRAM, or TSV coverage inputs
• -o, --output: output directory; defaults to remag_output next to the input FASTA
• -k, --keep-intermediate: retain embeddings, features, model weights, and other intermediate files
• --filter-only: stop after eukaryotic filtering and write filtered FASTA output
• -m, --mode: select presets such as metagenomics, single-cell, or short-reads
• --save-filtered-contigs: also write the contigs removed by the eukaryotic filter

Use remag -h for a quick reference and remag --help for the full CLI, including training, clustering, filtering, and rescue options.

How It Works

REMAG recovers eukaryotic bins with a multi-stage pipeline:

1. Eukaryotic filtering: By default, REMAG filters contigs with the integrated HyenaDNA classifier. This step can be disabled with --skip-bacterial-filter.
2. Feature extraction: REMAG combines 4-mer composition with optional multi-sample coverage data. Large contigs are augmented into multiple fragments for training.
3. Contrastive learning: A Siamese network trained with Barlow Twins learns embeddings that place fragments from the same contig close together.
4. Core gene annotation: miniprot maps eukaryotic single-copy core genes to support clustering and quality checks.
5. Greedy clustering and rescue: REMAG applies greedy Leiden clustering across multiple resolutions, then merges or rescues bins when single-copy gene checks support it.

Output

Core outputs

• bins/: Directory containing FASTA files for each bin
• bins.csv: Final contig-to-bin assignments
• embeddings.csv: Contig embeddings from the neural network
• remag.log: Detailed log file
• *_eukaryotic_filtered.fasta: Filtered FASTA written when eukaryotic filtering is enabled

Additional outputs with -k / --keep-intermediate

• siamese_model.pt: Trained Siamese neural network model
• kmer_embeddings.csv: K-mer encoder embeddings (before fusion)
• coverage_embeddings.csv: Coverage encoder embeddings (before fusion)
• params.json: Run parameters for reproducibility
• features.csv: Extracted k-mer and coverage features
• fragments.pkl: Fragment information used during training
• *_hyenadna_classification.tsv: HyenaDNA eukaryotic classification results (tab-separated)
• gene_contig_mappings.json: Cached gene-to-contig mappings
• core_gene_duplication_results.json: Core gene duplication analysis
• knn_graph_edges.csv: k-NN graph edge list used for Leiden clustering
• knn_graph_stats.json: k-NN graph construction statistics
• temp_miniprot/: Temporary directory for miniprot alignments

Filtering output

• *_non_eukaryotic.fasta: Contigs removed by the HyenaDNA filter when --save-filtered-contigs is used

Visualization

With plotting dependencies installed, you can generate UMAP plots from embeddings.csv and bins.csv:

pip install "remag[plotting]"
python scripts/plot_features.py --features output_directory/embeddings.csv --clusters output_directory/bins.csv --output output_directory

• umap_coordinates.csv: UMAP projections for visualization
• umap_plot.pdf: UMAP visualization plot with cluster assignments

Requirements

• Python 3.9+
• miniprot is required for core gene analysis when installing outside conda packages or the project Docker image
• Plotting extras are optional: pip install remag[plotting]

The package includes a pre-trained HyenaDNA classifier model for eukaryotic contig filtering.

Acknowledgments

The integrated HyenaDNA classifier uses a pre-trained genomic foundation model:

• Repository: HazyResearch/hyena-dna
• Paper: Nguyen E, Poli M, Faizi M, et al. HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution. NeurIPS 2023.

License

MIT License - see LICENSE file for details.

Citation

If you use REMAG in your research, please cite:

@article {G{\'o}mez-P{\'e}rez2026.03.05.709928,
	author = {G{\'o}mez-P{\'e}rez, Daniel and Raguideau, S{\'e}bastien and Warring, Sally and James, Robert and Hildebrand, Falk and Quince, Christopher},
	title = {REMAG: recovery of eukaryotic genomes from metagenomic data using contrastive learning},
	elocation-id = {2026.03.05.709928},
	year = {2026},
	doi = {10.64898/2026.03.05.709928},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2026/03/08/2026.03.05.709928},
	eprint = {https://www.biorxiv.org/content/early/2026/03/08/2026.03.05.709928.full.pdf},
	journal = {bioRxiv}
}