antiSMASH 6.0: improving cluster detection and comparison capabilities

By Kai Blin, S. Shaw, Alexander M. Kloosterman, Z. Charlop-Powers, G. P. Wezel, Marnix H. Medema, Tilmann Weber. Nucleic Acids Res., 2021

2,196 citations recorded by the source corpus.

Abstract Many microorganisms produce natural products that form the basis of antimicrobials, antivirals, and other drugs. Genome mining is routinely used to complement screening-based workflows to discover novel natural products. Since 2011, the "antibiotics and secondary metabolite analysis shell—antiSMASH" (https://antismash.secondarymetabolites.org/) has supported researchers in their microbial genome mining tasks, both as a free-to-use web server and as a standalone tool under an OSI-approved open-source license. It is currently the most widely used tool for detecting and characterising biosynthetic gene clusters (BGCs) in bacteria and fungi. Here, we present the updated version 6 of antiSMASH. antiSMASH 6 increases the number of supported cluster types from 58 to 71, displays the modular structure of multi-modular BGCs, adds a new BGC comparison algorithm, allows for the integration of results from other prediction tools, and more effectively detects tailoring enzymes in RiPP clusters.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/a4255361de8d7e7780547da29556095eae9dbbc1

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Citations in the corpus

Artificial Intelligence For Natural Product Drug Discovery (2023, methodology): “Identifying natural product BGCs still largely relies on rule-based methods such as those used in antiSMASH 11 and PRISM 12 .”
antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation (2023, neutral): “Since its 2011 release, antiSMASH ( 8–13 ) has established itself as the most widely used tool for mining microbial genomes for SM BGCs.”
MIBiG 3.0: a community-driven effort to annotate experimentally validated biosynthetic gene clusters (2022, methodology): “To address the first stage, sequence information, various tools have been developed that automatically detect BGCs from DNA sequence, including antiSMASH and its siblings fungiSMASH and plantiSMASH (1,2), GECCO (3), DeepBGC (4), RiPPMiner (5) and PRISM 4 (6).”