The evolution of genome mining in microbes - a review.

By Nadine Ziemert, Mohammad Alanjary, Tilmann Weber. Natural product reports (Print), 2016

514 citations recorded by the source corpus.

Covering: 2006 to 2016The computational mining of genomes has become an important part in the discovery of novel natural products as drug leads. Thousands of bacterial genome sequences are publically available these days containing an even larger number and diversity of secondary metabolite gene clusters that await linkage to their encoded natural products. With the development of high-throughput sequencing methods and the wealth of DNA data available, a variety of genome mining methods and tools have been developed to guide discovery and characterisation of these compounds. This article reviews the development of these computational approaches during the last decade and shows how the revolution of next generation sequencing methods has led to an evolution of various genome mining approaches, techniques and tools. After a short introduction and brief overview of important milestones, this article will focus on the different approaches of mining genomes for secondary metabolites, from detecting biosynthetic genes to resistance based methods and "evo-mining" strategies including a short evaluation of the impact of the development of genome mining methods and tools on the field of natural products and microbial ecology.

Subfields: Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/c86952981c9256e090b1ba3be8397729153abb8d

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Citations in the corpus

antiSMASH 6.0: improving cluster detection and comparison capabilities (2021, background): “This approach can now be complemented by sequencing and subsequent mining of genome and metagenome data to identify natural product biosynthetic pathways (2).”
clinker & clustermap.js: Automatic generation of gene cluster comparison figures (2020, background): “For instance, the conservation (or lack thereof) of gene clusters encoding the biosynthesis of secondary metabolites can guide the search for new drug compounds and bioactivities (Gilchrist et al. , 2018, Ziemert et al. , 2016).”
A deep learning genome-mining strategy for biosynthetic gene cluster prediction (2019, background): “Of these options, sequential protein family (Pfam) domain representations have been highly informative for BGC identification (16,30) because they represent functional elements within genes.”
antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification (2017, background): “The observation that the genomes of many microorganisms contain multiple biosynthetic gene clusters (BGCs) that code for the production of such molecules has led to a paradigm shift in natural products research: within the last 10 years, genome mining has been established as an important technology ”