Determining biosynthetic gene cluster boundaries through comparative bioinformatics

Modern advances in sequencing, "-omics," and bioinformatics have given rise to the field of genome mining, loosely defined as the use of genomic data to guide natural product (NP) discovery. This technique applies our understanding of biosynthetic logic to predict the genes encoding for production of novel compounds. The major steps include identification of these biosynthetic gene clusters (BGCs), their classification, and prioritization for subsequent experimentation. Despite decades of effort, determination of cluster boundaries without experimental validation remains one of the greatest challenges in genome mining. Genes encoded within a BGC are the foundation for all downstream analysis. Thus, accurate determination of gene cluster content is critical for effective prioritization of BGCs and prediction of their products. Synteny, or the conservation of homologous genes and their arrangement, provides an effective solution for predicting these borders. Over evolutionary time, transfer and rearrangement of genes results in variability surrounding BGCs, such that natural breaks in conservation underlie these functional units. In this chapter, we provide a comprehensive approach for using synteny to delineate BGC boundaries.