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ARTS-DB: a database for antibiotic resistant targets

As a result of the continuous evolution of drug resistant bacteria, new antibiotics are urgently needed. Encoded by biosynthetic gene clusters (BGCs), antibiotic compounds are mostly produced by bacteria. With the exponential increase in the number of publicly available, sequenced genomes and the ad...

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Autores principales: Mungan, Mehmet Direnç, Blin, Kai, Ziemert, Nadine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728217/
https://www.ncbi.nlm.nih.gov/pubmed/34718689
http://dx.doi.org/10.1093/nar/gkab940
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author Mungan, Mehmet Direnç
Blin, Kai
Ziemert, Nadine
author_facet Mungan, Mehmet Direnç
Blin, Kai
Ziemert, Nadine
author_sort Mungan, Mehmet Direnç
collection PubMed
description As a result of the continuous evolution of drug resistant bacteria, new antibiotics are urgently needed. Encoded by biosynthetic gene clusters (BGCs), antibiotic compounds are mostly produced by bacteria. With the exponential increase in the number of publicly available, sequenced genomes and the advancements of BGC prediction tools, genome mining algorithms have uncovered millions of uncharacterized BGCs for further evaluation. Since compound identification and characterization remain bottlenecks, a major challenge is prioritizing promising BGCs. Recently, researchers adopted self-resistance based strategies allowing them to predict the biological activities of natural products encoded by uncharacterized BGCs. Since 2017, the Antibiotic Resistant Target Seeker (ARTS) facilitated this so-called target-directed genome mining (TDGM) approach for the prioritization of BGCs encoding potentially novel antibiotics. Here, we present the ARTS database, available at https://arts-db.ziemertlab.com/. The ARTS database provides pre-computed ARTS results for >70,000 genomes and metagenome assembled genomes in total. Advanced search queries allow users to rapidly explore the fundamental criteria of TDGM such as BGC proximity, duplication and horizontal gene transfers of essential housekeeping genes. Furthermore, the ARTS database provides results interconnected throughout the bacterial kingdom as well as links to known databases in natural product research.
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spelling pubmed-87282172022-01-05 ARTS-DB: a database for antibiotic resistant targets Mungan, Mehmet Direnç Blin, Kai Ziemert, Nadine Nucleic Acids Res Database Issue As a result of the continuous evolution of drug resistant bacteria, new antibiotics are urgently needed. Encoded by biosynthetic gene clusters (BGCs), antibiotic compounds are mostly produced by bacteria. With the exponential increase in the number of publicly available, sequenced genomes and the advancements of BGC prediction tools, genome mining algorithms have uncovered millions of uncharacterized BGCs for further evaluation. Since compound identification and characterization remain bottlenecks, a major challenge is prioritizing promising BGCs. Recently, researchers adopted self-resistance based strategies allowing them to predict the biological activities of natural products encoded by uncharacterized BGCs. Since 2017, the Antibiotic Resistant Target Seeker (ARTS) facilitated this so-called target-directed genome mining (TDGM) approach for the prioritization of BGCs encoding potentially novel antibiotics. Here, we present the ARTS database, available at https://arts-db.ziemertlab.com/. The ARTS database provides pre-computed ARTS results for >70,000 genomes and metagenome assembled genomes in total. Advanced search queries allow users to rapidly explore the fundamental criteria of TDGM such as BGC proximity, duplication and horizontal gene transfers of essential housekeeping genes. Furthermore, the ARTS database provides results interconnected throughout the bacterial kingdom as well as links to known databases in natural product research. Oxford University Press 2021-10-28 /pmc/articles/PMC8728217/ /pubmed/34718689 http://dx.doi.org/10.1093/nar/gkab940 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Database Issue
Mungan, Mehmet Direnç
Blin, Kai
Ziemert, Nadine
ARTS-DB: a database for antibiotic resistant targets
title ARTS-DB: a database for antibiotic resistant targets
title_full ARTS-DB: a database for antibiotic resistant targets
title_fullStr ARTS-DB: a database for antibiotic resistant targets
title_full_unstemmed ARTS-DB: a database for antibiotic resistant targets
title_short ARTS-DB: a database for antibiotic resistant targets
title_sort arts-db: a database for antibiotic resistant targets
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728217/
https://www.ncbi.nlm.nih.gov/pubmed/34718689
http://dx.doi.org/10.1093/nar/gkab940
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