4CAC: 4-class classifier of metagenome contigs using machine learning and assembly graphs

Abstract

AbstractMicrobial communities usually harbor a mix of bacteria, archaea, plasmids, viruses, and microeukaryotes. Within these communities, viruses, plasmids, and microeukaryotes coexist in relatively low abundance, yet they engage in intricate interactions with bacteria. Moreover, viruses and plasmids, as mobile genetic elements, play important roles in horizontal gene transfer and the development of antibiotic resistance within microbial populations. However, due to the difficulty of identifying viruses, plasmids, and microeukaryotes in microbial communities, our understanding of these minor classes lags behind that of bacteria and archaea. Recently, several classifiers have been developed to separate one or two minor classes from bacteria and archaea in metagenome assemblies, but none can classify all of the four classes simultaneously. Moreover, existing classifiers have low precision on minor classes. Here, we developed a classifier called 4CAC that is able to identify viruses, plasmids, microeukaryotes, and prokaryotes simultaneously from metagenome assemblies. 4CAC generates an initial four-way classification using several sequence length-adjusted XGBoost models and further improves the classification using the assembly graph. Evaluation on simulated and real metagenome datasets demonstrates that 4CAC substantially outperforms existing classifiers and combinations thereof on short reads. On long reads, it also shows an advantage unless the abundance of the minor classes is very low. 4CAC runs 1-2 orders of magnitude faster than the other classifiers. The 4CAC software is available athttps://github.com/Shamir-Lab/4CAC.