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Welcome to BASALT

Binning Across a Series of Assemblies Toolkit

  • Multi-Assembly Binning

    Process single assemblies and co-assemblies in one run with automatic dereplication. No need for separate tools like dRep.

  • Deep Learning Refinement

    Neural network ensemble (5 MLP models) identifies and removes contamination from individual bins using TNF, coverage, and k-mer features.

  • Hybrid Read Support

    Maximise long-read utilisation for contig retrieval and polishing. ~90% faster than assembly-level polishing.

  • GPU Acceleration

    GPU support for Semibin2 binning and deep learning inference in BASALT v1.2.0+.

What is BASALT?

BASALT is a comprehensive toolkit for binning and post-binning refinement of metagenomic assemblies. It produces high-quality metagenome-assembled genomes (MAGs) from short-read, long-read (ONT/PacBio), and HiFi metagenomic data.

The pipeline integrates multiple binning algorithms, a deep-learning-based contamination removal module, and a reassembly engine to maximise MAG recovery while minimising contamination.

BASALT Workflow
Overview of the BASALT pipeline, from raw reads to final MAGs.

BASALT vs BASALT-Air

BASALT comes in two editions:

BASALT BASALT-Air
Package manager Conda / Singularity Pixi
Path support Working directory only Absolute paths anywhere
Dataset separator / (slash) ; (semicolon)
Output control Writes to CWD --workdir + --outdir
CUDA config Manual Built-in via pixi.toml
Model download BASALT_models_download.py Hugging Face / pixi tasks
Status Mature (v1.2.0) New (v1.0.0)

Recommendation: New users should start with BASALT-Air for easier installation and flexible path handling. Existing BASALT users can continue using the Conda edition — both versions share the same core pipeline.

Key Features

Feature Description
Multiple binners MetaBAT2, Maxbin2, CONCOCT, Semibin2 + optional MetaBinner, VAMB, LorBin
Multi-assembly Process any number of single assemblies and co-assemblies in one run
DL contamination removal 5-model MLP ensemble classifies real vs. contaminated contigs
Contig retrieval PE-tracking and long-read connectivity recover missed contigs
Reassembly SPAdes / Unicycler hybrid reassembly for improved genome quality
Data feeding Import externally generated bins for refinement and reassembly
Checkpoint resumption Interrupted runs resume cleanly from the last completed step
Dual QC backends CheckM and CheckM2 quality assessment

Citation

If you use BASALT in your research, please cite:

Qiu, Z., Yuan, L., Lian, CA. et al. BASALT refines binning from metagenomic data and increases resolution of genome-resolved metagenomic analysis. Nature Communications 15, 2179 (2024). DOI: 10.1038/s41467-024-46539-7

@article{qiu2024basalt,
  title   = {BASALT refines binning from metagenomic data and increases
             resolution of genome-resolved metagenomic analysis},
  author  = {Qiu, Zhiguang and Yuan, Li and Lian, Chun-Ang and Lin, Bin
             and Chen, Jie and Mu, Rong and Qiao, Xuejiao and Zhang, Liyu
             and Xu, Zheng and Fan, Lu and others},
  journal = {Nature Communications},
  volume  = {15},
  number  = {1},
  pages   = {2179},
  year    = {2024},
  doi     = {10.1038/s41467-024-46539-7}
}

Contact

References

  1. Qiu, Z. et al. BASALT refines binning from metagenomic data... Nat. Commun. 15, 2179 (2024).
  2. Uritskiy, G.V. et al. MetaWRAP—a flexible pipeline... Microbiome 6, 1-13 (2018).
  3. Sieber, C.M. et al. Recovery of genomes from metagenomes via DAS Tool... Nat. Microbiol. 3, 836-843 (2018).
  4. Olm, M.R. et al. dRep: a tool for fast and accurate genomic comparisons... ISME J. 11, 2864-2868 (2017).
  5. Xue, W. et al. LorBin: efficient binning of long-read metagenomes... Nat. Commun. 16, 9353 (2025).