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Updated: Sep 26, 2026

DNA-affinity-purified Chip (DAP-chip) Method to Determine Gene Targets for Bacterial Two component Regulatory Systems
Published on: July 21, 2014
ARTS-DB 2.0: an expanded database for target-directed genome mining across bacteria and fungi
Turgut Mesut Yılmaz1,2, Semih Erdogmus1,2, Caner Bağcı1,2
1Translational Genome Mining for Natural Products, Interfaculty Institute of Microbiology and Infection Medicine Tübingen (IMIT), Interfaculty Institute for Biomedical Informatics (IBMI), University of Tübingen, Auf der Morgenstelle 24, Tübingen, Baden-Württemberg 72076, Germany.
Abstract:
The increasing prevalence of antimicrobial resistance requires efficient strategies to prioritize biosynthetic gene clusters (BGCs) that may encode bioactive compounds with new modes of action. Target-directed genome mining (TDGM) addresses this challenge by leveraging self-resistance determinants encoded within or adjacent to BGCs to generate target hypotheses and prioritize candidate clusters. ARTS and FunARTS automate TDGM for bacterial and fungal genomes by detecting resistance-associated evolutionary signals, including duplicated essential targets, atypical phylogenetic patterns, and BGC co-localization. The Antibiotic Resistant Target Seeker database (ARTS-DB) was originally developed to enable exploration of precomputed TDGM outputs for bacteria. Here, we present ARTS-DB 2.0, a major update that integrates precomputed ARTS and FunARTS results into a single cross-domain resource. ARTS-DB 2.0 contains TDGM outputs for 42 087 high-quality, non-redundant genomes (35 681 bacteria and 6406 fungi) and incorporates updated BGC annotations generated with antiSMASH 8.0 together with an expanded resistance model repertoire, including the NCBI AMR HMM catalog. The web interface supports flexible query construction and gene-centric exploration, and provides extensive cross-links to external protein, biosynthetic, and pharmacology resources (e.g. UniProt, MIBiG, ChEMBL, and DrugBank). ARTS-DB 2.0 enables rapid, large-scale investigation of resistance-associated biosynthetic patterns across bacteria and fungi, supporting resistance-aware target and BGC prioritization for natural product discovery. Database URL: https://arts-db.cs.uni-tuebingen.de/.
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