Assembly of reads from metagenomic samples is a hard problem, often resulting in highly
fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by
re-grouping the sequences by their organism of origin, thus representing a crucial processing
step when exploring the biological diversity of metagenomic samples. Here we present
Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning
approach that integrates sequence co-abundances and tetranucleotide frequencies into a
common denoised space that enables precise clustering of sequences into microbial gen-
omes. When benchmarked, AAMB presented similar or better results compared with the
state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC)
genomes across simulated and real data. In addition, genomes reconstructed using AAMB
had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we
implemented a pipeline Integrating VAMB and AAMB that enabled improved binning,
recovering 20% and 29% more simulated and real NC genomes, respectively, compared to
VAMB, with moderate additional runtime.