title: Mining Bacterial Genomes for Secondary Metabolite Gene Clusters.
abstract: With the emergence of bacterial resistance against frequently used antibiotics, novel antibacterial compounds are urgently needed. Traditional bioactivity-guided drug discovery strategies involve laborious screening efforts and display high rediscovery rates. With the progress in next generation sequencing methods and the knowledge that the majority of antibiotics in clinical use are produced as secondary metabolites by bacteria, mining bacterial genomes for secondary metabolites with antimicrobial activity is a promising approach, which can guide a more time and cost-effective identification of novel compounds. However, what sounds easy to accomplish, comes with several challenges. To date, several tools for the prediction of secondary metabolite gene clusters are available, some of which are based on the detection of signature genes, while others are searching for specific patterns in gene content or regulation.Apart from the mere identification of gene clusters, several other factors such as determining cluster boundaries and assessing the novelty of the detected cluster are important. For this purpose, comparison of the predicted secondary metabolite genes with different cluster and compound databases is necessary. Furthermore, it is advisable to classify detected clusters into gene cluster families. So far, there is no standardized procedure for genome mining; however, different approaches to overcome all of these challenges exist and are addressed in this chapter. We give practical guidance on the workflow for secondary metabolite gene cluster identification, which includes the determination of gene cluster boundaries, addresses problems occurring with the use of draft genomes, and gives an outlook on the different methods for gene cluster classification. Based on comprehensible examples a protocol is set, which should enable the readers to mine their own genome data for interesting secondary metabolites.
authors.affiliation: Interfaculty Institute of Microbiology and Infection Medicine Tübingen, Microbiology/Biotechnology, University of Tübingen, 72076, Tübingen, Germany.|German Centre for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany.
authors.forename: Martina
authors.initials: M
authors.lastname: Adamek
authors.affiliation: Interfaculty Institute of Microbiology and Infection Medicine Tübingen, Microbiology/Biotechnology, University of Tübingen, 72076, Tübingen, Germany.
authors.forename: Marius
authors.initials: M
authors.lastname: Spohn
authors.affiliation: Interfaculty Institute of Microbiology and Infection Medicine Tübingen, Microbiology/Biotechnology, University of Tübingen, 72076, Tübingen, Germany.|German Centre for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany.
authors.forename: Evi
authors.initials: E
authors.lastname: Stegmann
authors.affiliation: Interfaculty Institute of Microbiology and Infection Medicine Tübingen, Microbiology/Biotechnology, University of Tübingen, 72076, Tübingen, Germany. nadine.ziemert@uni-tuebingen.de.|German Centre for Infection Research (DZIF), Partner Site Tübingen, Tübingen, Germany. nadine.ziemert@uni-tuebingen.de.
authors.forename: Nadine
authors.initials: N
authors.lastname: Ziemert
mesh_terms: D001483:Base Sequence; D057225:Data Mining / Q000379:methods*; D016680:Genome, Bacterial; D005810:Multigene Family; D064210:Secondary Metabolism / Q000235:genetics*
keywords: Antibiotics; Biosynthesis; Cluster boundaries; Gene cluster families; Genome mining; INBEKT; Prioritization; Secondary metabolite gene cluster; antiSMASH