Abstract
BackgroundGenome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. The >200,000 microbial genomes now publicly available hold information on abundant novel chemistry. One way to navigate this vast genomic diversity is through comparative analysis of homologous BGCs, which allows identification of cross-species patterns that can be matched to the presence of metabolites or biological activities. However, current tools are hindered by a bottleneck caused by the expensive network-based approach used to group these BGCs into gene cluster families (GCFs).ResultsHere, we introduce BiG-SLiCE, a tool designed to cluster massive numbers of BGCs. By representing them in Euclidean space, BiG-SLiCE can group BGCs into GCFs in a non-pairwise, near-linear fashion. We used BiG-SLiCE to analyze 1,225,071 BGCs collected from 209,206 publicly available microbial genomes and metagenome-assembled genomes within 10 days on a typical 36-core CPU server. We demonstrate the utility of such analyses by reconstructing a global map of secondary metabolic diversity across taxonomy to identify uncharted biosynthetic potential. BiG-SLiCE also provides a “query mode” that can efficiently place newly sequenced BGCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.ConclusionsBiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. BiG-SLiCE is available via https://github.com/medema-group/bigslice.
Highlights
Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery
In order to show how BiG-SLiCE could be applied to large datasets that capture the full diversity of BGCs from cultured and uncultured microbes, we decided to collect a merged dataset of publicly available microbial genomes and metagenomeassembled genomes (MAGs)
To draw more solid biological conclusions from this kind of analysis, the issue of uneven feature coverage needs to be addressed and a more robust approach needs to be designed for choosing a threshold for clustering
Summary
Genome mining for biosynthetic gene clusters (BGCs) has become an integral part of natural product discovery. Conclusions: BiG-SLiCE opens up new possibilities to accelerate natural product discovery and offers a first step towards constructing a global and searchable interconnected network of BGCs. As more genomes are sequenced from understudied taxa, more information can be mined to highlight their potentially novel chemistry. It was previously estimated that there might be billions of microbial species living on Earth [6, 7], and even from the heavily mined genus of Streptomyces, novel discoveries continue to be made [8,9,10,11,12,13] Tapping into this vast space of natural product diversity will increase the chances to achieve future medicinal breakthroughs. By learning about microbes and the compounds that they produce, we can gain knowledge about mechanisms of interaction within microbiomes, enabling us to study how their microbial composition is associated with human health and dis-
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