ArticleNAR genomics and bioinformatics2020
TOUCAN: a framework for fungal biosynthetic gene cluster discovery.
Article in NAR genomics and bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 35 citations in OpenAlex.
- A novel transformer-based platform for the prediction and design of biosynthetic gene clusters for (un)natural products.PLoS computational biology · 2026Article
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Review
- The Role of Nucleotide Sequencing in Natural Product Drug Discovery.Journal of natural products · 2025Review
- Computational Approaches for Discovering Virulence Factors inJournal of fungi (Basel, Switzerland) · 2025Review
- Global survey of secondary metabolism inPNAS nexus · 2025Article
- Microbial secondary metabolites: advancements to accelerate discovery towards application.Nature reviews. Microbiology · 2025Review
- Metabolic engineering approaches for the biosynthesis of antibiotics.Microbial cell factories · 2025Review
- Discovery of fungal onoceroid triterpenoids through domainless enzyme-targeted global genome mining.Nature communications · 2024Article
- Fungal BGCs for Production of Secondary Metabolites: Main Types, Central Roles in Strain Improvement, and Regulation According to the Piano Principle.International journal of molecular sciences · 2023Review
- FunARTS, the Fungal bioActive compound Resistant Target Seeker, an exploration engine for target-directed genome mining in fungi.Nucleic acids research · 2023Article
- Review
- Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability.Materials today. Bio · 2023Article
- Segregation of the genusMycoKeys · 2023Article
- Application of Gene Knockout and Heterologous Expression Strategy in Fungal Secondary Metabolites Biosynthesis.Marine drugs · 2022Review
- How to Completely Squeeze a Fungus-Advanced Genome Mining Tools for Novel Bioactive Substances.Pharmaceutics · 2022Review
- Improving candidate Biosynthetic Gene Clusters in fungi through reinforcement learning.Bioinformatics (Oxford, England) · 2022Article
- FunOrder: A robust and semi-automated method for the identification of essential biosynthetic genes through computational molecular co-evolution.PLoS computational biology · 2021Article
- antiSMASH 6.0: improving cluster detection and comparison capabilities.Nucleic acids research · 2021Article
- Identification of a Novel Biosynthetic Gene Cluster inJournal of fungi (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fungal secondary metabolites (SMs) are an important source of numerous bioactive compounds largely applied in the pharmaceutical industry, as in the production of antibiotics and anticancer medications. The discovery of novel fungal SMs can potentially benefit human health. Identifying biosynthetic gene clusters (BGCs) involved in the biosynthesis of SMs can be a costly and complex task, especially due to the genomic diversity of fungal BGCs. Previous studies on fungal BGC discovery present limited scope and can restrict the discovery of new BGCs. In this work, we introduce TOUCAN, a supervised learning framework for fungal BGC discovery. Unlike previous methods, TOUCAN is capable of predicting BGCs on amino acid sequences, facilitating its use on newly sequenced and not yet curated data. It relies on three main pillars: rigorous selection of datasets by BGC experts; combination of functional, evolutionary and compositional features coupled with outperforming classifiers; and robust post-processing methods. TOUCAN best-performing model yields 0.982
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.