Towards complete representation of bacterial contents in metagenomic samples

Background: In the metagenome assembly of a microbiome community, we may think abundant species would be easier to assemble due to their deeper coverage. However, this conjucture is rarely tested. We often do not know how many abundant species we are missing and do not have an approach to recover these species. Results: Here we proposed k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We showed that even with PacBio High-Fidelity (HiFi) reads, abundant species are often not assembled as high strain diversity may lead to fragmented contigs. We developed a novel algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Our algorithm is reference-free and complement to standard metagenome binning. Evaluated on 14 real datasets, it rescued many abundant species that would be missing with existing methods. Conclusions: Our work stresses the importance of metagenome completeness which is often overlooked before. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbiome communities.