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Fungal Resistance gene-directed genome mining (FRIGG) pipeline

Authors: Inge Kjærbølling; Tammi C. Vesth; Mikael Rørdam Andersen;

Fungal Resistance gene-directed genome mining (FRIGG) pipeline

Abstract

Fungal secondary metabolites are a rich source of valuable natural products, and genome sequencing has revealed a proliferation of predicted biosynthetic gene clusters in the genomes. It is however currently an unfeasible task to characterize all biosynthetic gene clusters and to identify possible uses of the compounds. Therefore, a rational approach is needed to identify a shortlist of gene clusters responsible for producing valuable compounds. To this end, several bioactive clusters include a resistance gene, which is a paralog of the target gene inhibited by the compound. This mechanism can be used to identify those clusters. We have developed the FRIGG (Fungal ResIstance Gene-directed Genome mining) pipeline for identifying this type of biosynthetic gene clusters based on homology patterns of the cluster genes. As an example dataset, the FRIGG pipeline has been run using 51 Aspergillus and Penicillium genomes, identifying 72 unique families of putative resistance genes. The dataset is included with the software.

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Keywords

secondary metabolism, Aspergillus, bioactive compounds, fungal, genome mining, genomics, Penicillium, comparative genomics, antibiotics

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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