BinSanity: Unsupervised Clustering of Environmental Microbial Assemblies Using Coverage and Affinity Propagation

Abstract

AbstractMetagenomics has become an integral part of defining microbial diversity in various environments. Many ecosystems have characteristically low biomass and few cultured representatives. Linking potential metabolisms to phylogeny in environmental microorganisms is important for interpreting microbial community functions and the impacts these communities have on geochemical cycles. However, with metagenomic studies there is the computational hurdle of ‘binning’ contigs into phylogenetically related units or putative genomes. Binning methods have been implemented with varying approaches such as k-means clustering, Gaussian mixture models, hierarchical clustering, neural networks, and two-way clustering; however, many of these suffer from biases against low coverage/abundance organisms and closely related taxa/strains. We are introducing a new binning method, BinSanity, that utilizes the clustering algorithm affinity propagation (AP), to cluster assemblies using coverage alone, removing potential composition based biases in clustering contigs, but requires a minimum of two samples. To increase fidelity, a refinement script was developed that uses composition data (tetranucleotide frequency and %G+C content) to refine bins containing multiple source organisms. This separation of composition and coverage based signatures reduces clustering bias for closely related taxa. BinSanity was developed and tested on artificial metagenomes varying in size and complexity. Results indicate that this implementation of AP lead to a higher precision, recall, and Adjusted Rand Index over five commonly implemented methods. When tested on a previously published infant gut metagenome, BinSanity generated high completion and low redundancy bins corresponding with the published metagenome-assembled genomes.