{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T11:59:04Z","timestamp":1790337544706,"version":"4.1.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":267,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009139","name":"DZIF","doi-asserted-by":"publisher","award":["09.726"],"award-info":[{"award-number":["09.726"]}],"id":[{"id":"10.13039\/100009139","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003766","name":"Ministry of National Education","doi-asserted-by":"publisher","award":["1416 to T.M.Y."],"award-info":[{"award-number":["1416 to T.M.Y."]}],"id":[{"id":"10.13039\/501100003766","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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\u2009087 high-quality, non-redundant genomes (35\u2009681 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.<\/jats:p>\n                  <jats:p>Database URL: https:\/\/arts-db.cs.uni-tuebingen.de\/<\/jats:p>","DOI":"10.1093\/database\/baag060","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T11:38:58Z","timestamp":1788867538000},"source":"Crossref","is-referenced-by-count":0,"title":["ARTS-DB 2.0: an expanded database for target-directed genome mining across bacteria and fungi"],"prefix":"10.1093","volume":"2026","author":[{"given":"Turgut Mesut","family":"Y\u0131lmaz","sequence":"first","affiliation":[{"name":"Translational Genome Mining for Natural Products, Interfaculty Institute of Microbiology and Infection Medicine T\u00fcbingen (IMIT), Interfaculty Institute for Biomedical Informatics (IBMI), University of T\u00fcbingen, Auf der 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