Towards accurate identification of Biosynthetic Gene Clusters in fungi
Creators: name: Hayda Almeida
name: Tsang, Adrian
name: Abdoulaye  Baniré Diallo
Publisher: name: F1000 Research Limited
Year: 2019

description: <i>Introduction - </i>Natural products (NP) are bioactive compounds produced by plants, fungi and bacteria, and represent a vital source for many pharmaceutical drugs.<br>Genes involved in NP biosynthesis are usually clustered in a genome[1], and known as Biosynthetic Gene Clusters (BGC).<br>Previous work on identifying BGCs showed supervised learning to be effective for bacteria[2,3]. Fungi BGC genomic diversity makes their identification challenging[4].<br>We propose an alignment-free approach based on supervised learning to predict BGCs in fungi, capable of generalizing predictions for various BGC types, focusing on precision while requiring less data curation.<br><br><i>Methodology -</i> Our approach has 3 main steps: collect a curated dataset; design a classification framework; and evaluate predictions against annotated BGCs. Positive instances were curated from MIBiG (http://mibig.secondarymetabolites.org), and synthetic negative instances from OrthoDB (http://orthodb.org). We used protein k-mers and protein annotated domains as features. Classification models are based on Random Forest, C-Support Vector (SVC), LinearSVC, Nu-SVC, Logit classifiers and a LSTM network.<br>We tested our models using annotated Aspergillus niger BGCs[5].<br><br><i>Preliminary Results &amp; Conclusion - </i>Despite the task complexity, preliminary results for the positive class for all classifiers show trends with F-measure around 0.6, further improved if handling class imbalance.<br>State-of-the-art methods seem to have limited performance possibly due to the genomic diversity aspect of these clusters.<br><br>[1] Anne Osbourn. Secondary metabolic gene clusters: evolutionary toolkits for chemical innovation. Trends in Genetics, 26(10):449–457, 2010.<br>[2] Priyesh Agrawal, Shradha Khater, Money Gupta, Neetu Sain, and Debasisa Mohanty. RiPPMiner: a bioinformatics resource for deciphering chemical structures of RiPPs based on prediction of cleavage and cross-links. Nucleic Acids Research, 45(W1):W80–W88, 2017.<br>[3] Geoffrey D Hannigan, David Prihoda, Andrej Palicka, Jindrich Soukup, Ondrej Klempir, Lena Rampula, Jindrich Durcak, Michael Wurst, Jakub Kotowski, Dan Chang, et al. A deep learning genome mining strategy improves biosynthetic gene cluster prediction. bioRxiv, page 500694, 2018.<br>[4] Inge Kjærbølling, Tammi C Vesth, Jens C Frisvad, Jane L Nybo, Sebastian Theobald, Alan Kuo, Paul Bowyer, Yudai Matsuda, Stephen Mondo, Ellen K Lyhne, et al. Linking secondary metabolites to gene clusters through genome sequencing of six diverse aspergillus species. Proceedings of the National Academy of Sciences, 115(4):E753–E761, 2018.<br>[5] Diane O Inglis, Jonathan Binkley, Marek S Skrzypek, Martha B Arnaud, Gustavo C Cerqueira, Prachi Shah, Farrell Wymore, Jennifer R Wortman, and Gavin Sherlock. Comprehensive annotation of secondary metabolite biosynthetic genes and gene clusters of Aspergillus nidulans, A. fumigatus, A. niger and A. oryzae. BMC microbiology, 13(1):91, 2013.