  Fungal Biosynthetic Gene Clusters (BGCs) of secondary metabolites are
clusters of genes capable of producing natural products, compounds that play an
important role in the production of a wide variety of bioactive compounds,
including antibiotics and pharmaceuticals. Identifying BGCs can lead to the
discovery of novel natural products to benefit human health. Previous work has
been focused on developing automatic tools to support BGC discovery in plants,
fungi, and bacteria. Data-driven methods, as well as probabilistic and
supervised learning methods have been explored in identifying BGCs. Most
methods applied to identify fungal BGCs were data-driven and presented limited
scope. Supervised learning methods have been shown to perform well at
identifying BGCs in bacteria, and could be well suited to perform the same task
in fungi. But labeled data instances are needed to perform supervised learning.
Openly accessible BGC databases contain only a very small portion of previously
curated fungal BGCs. Making new fungal BGC datasets available could motivate
the development of supervised learning methods for fungal BGCs and potentially
improve prediction performance compared to data-driven methods. In this work we
propose new publicly available fungal BGC datasets to support the BGC discovery
task using supervised learning. These datasets are prepared to perform binary
classification and predict candidate BGC regions in fungal genomes. In addition
we analyse the performance of a well supported supervised learning tool
developed to predict BGCs.
