Evaluating metagenomics tools for genome binning with real metagenomic datasets and CAMI datasets.

ABSTRACT:

Background

Shotgun metagenomics based on untargeted sequencing can explore the taxonomic profile and the function of unknown microorganisms in samples, and complement the shortage of amplicon sequencing. Binning assembled sequences into individual groups, which represent microbial genomes, is the key step and a major challenge in metagenomic research. Both supervised and unsupervised machine learning methods have been employed in binning. Genome binning belonging to unsupervised method clusters contigs into individual genome bins by machine learning methods without the assistance of any reference databases. So far a lot of genome binning tools have emerged. Evaluating these genome tools is of great significance to microbiological research. In this study, we evaluate 15 genome binni

SUBMITTER: Yue Y 

PROVIDER: S-EPMC7469296 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature