MetaDecoder: a novel method for clustering metagenomic contigs.

ABSTRACT:

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 rema

SUBMITTER: Liu CC 

PROVIDER: S-EPMC8908641 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature