Binning Metagenomic Contigs Using Unsupervised Clustering and Reference Databases.

Abstract

Metagenomics can directly extract the genetic material of all microorganisms from the environment, and obtain metagenomic samples with a large number of unknown DNA sequences. Binning of metagenomic contigs is a hot topic in metagenomics research. There are two key challenges for the current unsupervised metagenomic clustering algorithms. First, unsupervised metagenomic clustering methods rarely use reference databases, causing a certain waste of resources. Second, unsupervised metagenomic clustering methods are restricted by the characteristics of the sequences and the clustering algorithms, and the binning effect is limited. Therefore, a new binning method for metagenomic contigs using unsupervised clustering methods and reference databases is proposed to address these challenges, to make full use of the advantages of unsupervised clustering methods and reference databases constructed by scientists to improve the overall binning effect. This method uses the integrated SVM classification model to further bin the unsupervised clustering parts that do not perform well. Our proposed method was tested on simulated datasets and a real dataset and compared with other state-of-the-art metagenomic clustering methods including CONCOCT, Metabin2.0, Autometa, and MetaBAT. The results show that our method can achieve higher precision rate and improve the binning effect.