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Journal ArticleDOI

The confluence of big data and evolutionary genome mining for the discovery of natural products.

TLDR
This review covers literature between 2003-2021 and highlights examples where Big Data and evolutionary analyses have been combined to provide bioinformatic resources and tools for the discovery of novel natural products and their biosynthetic enzymes.
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This article is published in Natural Product Reports.The article was published on 2021-11-17. It has received 21 citations till now.

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Citations
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A Systematic Computational Analysis of Biosynthetic Gene Cluster Evolution: Lessons for Engineering Biosynthesis - eScholarship

TL;DR: By performing a systematic computational analysis of BGC evolution, this work derives evidence for three findings that shed light on the ways in which, despite these constraints, nature successfully invents new molecules.
Journal ArticleDOI

Compendium of specialized metabolite biosynthetic diversity encoded in bacterial genomes

TL;DR: The authors analyzed ~170,000 bacterial genomes and ~47,000 metagenome assembled genomes using a modified BiG-SLiCE and the new clust-o-matic algorithm.
Journal ArticleDOI

Microbiome composition modulates secondary metabolism in a multispecies bacterial community

TL;DR: Results from this model community show that bacterial BGC expression and chemical output depend on the identity and biosynthetic capacity of coculture partners, suggesting community composition and microbiome interactions may shape the regulation of secondary metabolism in nature.
Journal ArticleDOI

Targeted Large-Scale Genome Mining and Candidate Prioritization for Natural Product Discovery

TL;DR: Genomics-based approaches for prioritizing candidate BGCs extracted from large-scale genomic data are discussed, by highlighting studies that have successfully produced compounds with high chemical novelty, novel biosynthesis pathway, and potent bioactivities.
Journal ArticleDOI

Integrated Metabolomic–Genomic Workflows Accelerate Microbial Natural Product Discovery

TL;DR: This work considers innovative approaches which have led to prioritization of strain targets and have mitigated rediscovery rates, and discusses integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure.
References
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Journal ArticleDOI

Expansion of RiPP biosynthetic space through integration of pan-genomics and machine learning uncovers a novel class of lanthipeptides.

TL;DR: DecRiPPter as discussed by the authors combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operonlike structures that are part of the accessory genome of a genus.
Journal ArticleDOI

GISMO--gene identification using a support vector machine for ORF classification

TL;DR: Using GISMO, the novel prokaryotic gene finder, several thousand new predictions for the published genomes that are supported by extrinsic evidence are found, which strongly suggest that these are very likely biologically active genes.
Posted ContentDOI

BiG-SLiCE: A Highly Scalable Tool Maps the Diversity of 1.2 Million Biosynthetic Gene Clusters

TL;DR: BiG-SLiCE is introduced, a tool designed to cluster massive numbers of BGCs that provides a "query mode" that can efficiently place newly sequenced B GCs into previously computed GCFs, plus a powerful output visualization engine that facilitates user-friendly data exploration.
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