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BiG-SLiCE: A highly scalable tool maps the diversity of 1.2 million biosynthetic gene clusters.
Kautsar, Satria A; van der Hooft, Justin J J; de Ridder, Dick; Medema, Marnix H.
Afiliación
  • Kautsar SA; Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands.
  • van der Hooft JJJ; Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, sThe Netherlands.
  • de Ridder D; Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands.
  • Medema MH; Bioinformatics Group, Wageningen University, Droevendaalsesteeg 1, 6708PB, Wageningen, The Netherlands.
Gigascience ; 10(1)2021 01 13.
Article en En | MEDLINE | ID: mdl-33438731
ABSTRACT

BACKGROUND:

Genome 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).

RESULTS:

Here, 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.

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. BiG-SLiCE is available via https//github.com/medema-group/bigslice.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Familia de Multigenes / Vías Biosintéticas Límite: Humans Idioma: En Revista: Gigascience Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Familia de Multigenes / Vías Biosintéticas Límite: Humans Idioma: En Revista: Gigascience Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos