MetaDecoder: a novel method for clustering metagenomic contigs

Background: Clustering the metagenomic contigs into potential genomes is a key step to investigate the functional roles of microbial populations. Existing algorithms have achieved considerable success with simulated or real sequencing datasets. However, accurately classifying contigs from complex metagenomes is still a challenge. Results: We introduced a novel clustering algorithm, MetaDecoder, which can classify metagenomic contigs based on the frequencies of k-mers and coverages. MetaDecoder was built as a two-layer model with the first layer being a GPU-based modified Dirichlet process Gaussian mixture model (DPGMM), which controls the weight of each DPGMM cluster to avoid over-segmentation by dynamically dissolving contigs in small clusters and reassigning them to the remaining clusters. The second layer comprises a semi-supervised k-mer frequency probabilistic model and a modified Gaussian mixture model for modeling the coverage based on single copy marker genes. Benchmarks on simulated and real-world datasets demonstrated that MetaDecoder can be served as a promising approach for effectively clustering metagenomic contigs. Conclusions: In conclusion, we developed the GPU-based MetaDecoder for effectively clustering metagenomic contigs and reconstructing microbial communities from microbial data. Applying MetaDecoder on both simulated and real-world datasets demonstrated that it could generate more complete clusters with lower contamination. Using MetaDecoder, we identified novel high-quality genomes and expanded the existing catalog of bacterial genomes.

Automated summary

In this study, a novel clustering algorithm called MetaDecoder was introduced, which can classify metagenomic contigs based on the frequencies of k-mers and coverages. Benchmark tests on simulated and real-world datasets demonstrated that MetaDecoder can effectively cluster metagenomic contigs and has the potential to be a promising approach.