Read the documentation on how to use Vamb here: https://vamb.readthedocs.io/en/latest/

Vamb is a family of metagenomic binners which feeds kmer composition and abundance into a variational autoencoder and clusters the embedding to form bins. Its binners perform excellently with multiple samples, and pretty good on single-sample data.

The Vamb package contains several programs, including three binners:

• TaxVamb: A semi-supervised binner that uses taxonomy information from e.g. mmseqs taxonomy. TaxVamb produces the best results, but requires you have run a taxonomic annotation workflow. Link to article.
• Vamb: The original binner based on variational autoencoders. This has been upgraded significantly since its original release. Vamb strikes a good balance between speed and accuracy. Link to article.
• Avamb: An obsolete ensemble model based on Vamb and adversarial autoencoders. Avamb has an accuracy in between Vamb and TaxVamb, but is more computationally demanding than either. We don't recommend running Avamb: If you have the compute to run it, you should instead run TaxVamb See the Avamb README page for more information. Link to article.

And a taxonomy predictor:

• Taxometer: This tool refines arbitrary taxonomy predictions (e.g. from mmseqs taxonomy) using kmer composition and co-abundance. Link to article

See also our tool BinBencher.jl for evaluating metagenomic bins when a ground truth is available, e.g. for simulated data or a mock microbiome.

For more details, and how to run on an example dataset see the documentation.

# Assemble your reads, one assembly per sample, e.g. with SPAdes
for sample in 1 2 3; do
    spades.py --meta ${sample}.{fw,rv}.fq.gz -t 24 -m 100gb -o asm_${sample};
done    

# Concatenate your assemblies, and rename the contigs to the naming scheme
# S{sample}C{original contig name}. This can be done with a script provided by Vamb
# in the vamb/src directory
python src/concatenate.py contigs.fna.gz asm_{1,2,3}/contigs.fasta

# Estimate sample-wise abundance by mapping reads to the contigs.
# Any mapper will do, but we recommend strobealign with the --aemb flag
mkdir aemb
for sample in 1 2 3; do
    strobealign -t 8 --aemb contigs.fna.gz ${sample}.{fw,rv}.fq.gz > aemb/${sample}.tsv;
done

# Create an abundance TSV file from --aemb outputs using the script in vamb/src dir
python src/merge_aemb.py aemb abundance.tsv

# Run Vamb using the contigs and the directory with abundance files
vamb bin default --outdir vambout --fasta contigs.fna.gz --abundance_tsv abundance.tsv