TOUCAN: a framework for fungal biosynthetic gene cluster discovery

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 F-measure on BGC regions in the Aspergillus niger genome. Overall results show that TOUCAN outperforms previous approaches. TOUCAN focuses on fungal BGCs but can be easily adapted to expand its scope to process other species or include new features.

antiSMASH 6.0: improving cluster detection and comparison capabilities

“…Software tools to assist researchers in their natural product genome mining tasks have existed for over a decade. Recently published or updated examples include BAGEL ( 3 ), PRISM ( 4 ), RiPPER ( 5 ) and TOUCAN ( 6 ). For more in-depth reviews on the topic, see ( 7–10 ).…”

Identification of a Novel Biosynthetic Gene Cluster in Aspergillus niger Using Comparative Genomics

“…Moreover, two of the genes in the BGC for conidial pigment biosynthesis in A. fumigatus , as defined by co-expression, do not appeared to be involved in conidial pigment biosynthesis [ 23 ]. As fungal BGCs evolve rapidly [ 26 ], defining the boundary of BGCs and the role of clustered genes in the biosynthesis of secondary metabolites is very challenging and time-consuming [ 27 , 28 ]. Although, in this study, we have defined the NRRL3_00036 BGC to extend from NRRL3_00035 to NRRL3_00043 , we have only provided evidence for the functional involvement of NRRL3_00036 and NRRL3_00042 in the production of the two new compounds.…”

The year 2020 in natural product bioinformatics: an overview of the latest tools and databases

“…Aimed at fungi, CO-OCCUR 15 provides a new way of identifying BGCs based on shared syntenic relationships between biosynthetic genes. Another fungal BGC identication tool, TOUCAN, 16 was also released. A particularly challenging type of BGCs to computationally identify are those encoding the biosynthesis of Ribosomally synthesized and Posttranslationally modied Peptides (RiPPs), because of the apparent large diversity of unknown RiPP classes for which rule-based detection is not possible (as the required knowledge to design such rules is not yet available).…”