Improving metagenome binning by integrating intrinsic features and taxonomy

A common procedure for studying the microbiome is binning the sequenced contigs into metagenome-assembled genomes. State-of-the-art binning methods use coabundance and sequence-based motifs such as tetranucleotide frequencies, whereas taxonomic labels derived from alignment based classification have not been widely used. Here we propose TaxVAMB, a metagenome binning tool based on semisupervised bimodal variational autoencoders, combining tetranucleotide frequencies and contig coabundances with taxonomic information. TaxVAMB outperformed all other binners on CAMI2 human microbiome datasets, returning on average 29% more high-quality assemblies than the next best binner, and performed on par with the best binners on short-read datasets. On a human gut long-read dataset, TaxVAMB recovered 29% more high-quality bins. In a typical single-sample setup, TaxVAMB on average returns 83% more high-quality bins compared to VAMB. Lastly, TaxVAMB binned incomplete genomes better than any other tool, returning on average 300% more high-quality bins of incomplete genomes than the next best binner.