BinSanity: unsupervised clustering of environmental microbial assemblies using coverage and affinity propagation

Abstract

Metagenomics 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 with compositional based refinement (tetranucleotide frequency and percent GC content) to optimize bins containing multiple source organisms. This separation of composition and coverage based clustering reduces bias for closely related taxa. BinSanity was developed and tested on artificial metagenomes varying in size and complexity. Results indicate that BinSanity has a higher precision, recall, and Adjusted Rand Index compared to five commonly implemented methods. When tested on a previously published environmental metagenome, BinSanity generated high completion and low redundancy bins corresponding with the published metagenome-assembled genomes.

Mimicking microbial interactions under nitrate‐reducing conditions in an anoxic bioreactor: enrichment of novel Nitrospirae bacteria distantly related to Thermodesulfovibrio

“…The generated sequence mapping files were handled and converted as needed using SAMtools 2.1 (Li et al, 2009). Metagenome binning was performed employing five different binning algorithms: BinSanity v0.2.5.9 (Graham et al, 2017), COCACOLA (Lu et al, 2016), CONCOCT (Alneberg et al, 2014), MaxBin 2.0 2.2.3 (Wu et al, 2016) and MetaBAT 2 2.10.2 (Kang et al, 2015). The five resulting bin sets were supplied to DAS Tool 1.0 (C.M.K.…”

290 metagenome-assembled genomes from the Mediterranean Sea: a resource for marine microbiology

“…These metagenomic assemblies were binned using BinSanity (Graham, Heidelberg & Tully, 2017) into 290 draft microbial genomes with an estimated completeness ranging from 50–100%. Environmentally derived genomes are imperative for a number of downstream applications, including comparative genomics, metatranscriptomics, and metaproteomics.…”

Marine Animal Microbiomes: Toward Understanding Host–Microbiome Interactions in a Changing Ocean

“…Developing animal-specific databases (Simister et al, 2012b), which include the next-generation supplied sequences generally not available in curated taxonomic databases, could help alleviate this problem. There are also a number of new tools for metagenomicsbased analysis, including advancements in binning genomes from complex samples (Kang et al, 2015;Graham et al, 2017) as well as new visualization methods for comparing genomes (Eren et al, 2015;Wagner et al, 2017). A challenging issue that has received less attention is how to gain information from unknown genes and gene families, which can make up over half of the environmental microbial genomes.…”