Efficient De Novo Assembly and Recovery of Microbial Genomes from Complex Metagenomes Using a Reduced Set of k-mers

De novo assembly and genome binning are fundamental steps for genome-resolved metagenomics analyses. However, the availability of limited computational resources and extensive processing time limit the broader application of these analyses. To address these challenges, the optimization of the parameters employed in these processes can improve the effective utilization of available metagenomics tools. Therefore, this study tested three sets of k-mers (default, reduced, and extended) for their efficiency in metagenome assembly and suitability in recovering metagenome-assembled genomes. The results demonstrate that the reduced set of k-mers outperforms the other two sets in computational efficiency and the quality of results. The assemblies from the default set are comparable with those from the reduced set; however, less complete and highly contaminated metagenome-assembled genomes are obtained at the expense of higher processing time. The extended set of k-mers yields less contiguous but computationally expensive assemblies. This set takes approximately 3-times more processing time than the reduced k-mers and recovers the lowest proportions of high and medium-quality metagenome-assembled genomes. Contrarily, the reduced set produces better assemblies, substantially improving the number and quality of the recovered metagenome-assembled genomes in significantly reduced processing time. Validation of the reduced k-mer set on previously published metagenome datasets further demonstrates its effectiveness not only for human metagenomes but also for the metagenomes of environmental origin. These findings underscore that the reduced k-mer set is optimal for efficient metagenome analyses of varying complexities and origins. This optimization of the k-mer set used in metagenome assemblers significantly reduces computational time while improving the quality of the assemblies and recovered metagenome-assembled genomes. This efficient solution will facilitate the widespread application of genome-resolved analyses, even in resource-limited settings, and help the recovery of better-quality metagenome-assembled genomes for downstream analyses.