Detecting and prioritizing biosynthetic gene clusters for bioactive compounds in bacteria and fungi

• Published in: Applied microbiology and biotechnology Vol. 103; no. 8; pp. 3277 - 3287
• Main Authors: Tran, Phuong Nguyen, Yen, Ming-Ren, Chiang, Chen-Yu, Lin, Hsiao-Ching, Chen, Pao-Yang
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2019; Springer; Springer Nature B.V
• Subjects: Algorithms; Antibiotics; Backup software; Bacteria; Bacteria - chemistry; Bacteria - genetics; Bacteria - metabolism; Bacterial genetics; Bacterial infections; Bioactive compounds; bioactive properties; Bioinformatics; Biological activity; Biological Products - metabolism; Biomedical and Life Sciences; biosynthesis; Biotechnology; Cancer; Computational Biology; Drug Resistance, Microbial - genetics; Enzymes; Fungi; Fungi - chemistry; Fungi - genetics; Fungi - metabolism; Gene clusters; Gene Duplication; Gene Transfer, Horizontal; Genes; genome mining; Genome, Microbial - genetics; Genomes; Genomics; Hypotheses; Life Sciences; Metabolites; Microbial Genetics and Genomics; Microbiology; Mini-Review; Mining industry; multigene family; Multigene Family - genetics; Natural products; Plant metabolites; prediction; prioritization; screening; Secondary Metabolism - genetics; Secondary metabolites; Secondary metabolites; Self-protection; Biosynthetic gene cluster; Duplicate gene; Bioinformatics; Horizontal gene transfer
• ISSN: 0175-7598; 1432-0614; 1432-0614
• DOI: 10.1007/s00253-019-09708-z

Secondary metabolites (SM) produced by fungi and bacteria have long been of exceptional interest owing to their unique biomedical ramifications. The traditional discovery of new natural products that was mainly driven by bioactivity screening has now experienced a fresh new approach in the form of genome mining. Several bioinformatics tools have been continuously developed to detect potential biosynthetic gene clusters (BGCs) that are responsible for the production of SM. Although the principles underlying the computation of these tools have been discussed, the biological background is left underrated and ambiguous. In this review, we emphasize the biological hypotheses in BGC formation driven from the observations across genomes in bacteria and fungi, and provide a comprehensive list of updated algorithms/tools exclusively for BGC detection. Our review points to a direction that the biological hypotheses should be systematically incorporated into the BGC prediction and assist the prioritization of candidate BGC.