AUTHOR=Umemura Myco , Koike Hideaki , Machida Masayuki TITLE=Motif-independent de novo detection of secondary metabolite gene clusters—toward identification from filamentous fungi JOURNAL=Frontiers in Microbiology VOLUME=6 YEAR=2015 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2015.00371 DOI=10.3389/fmicb.2015.00371 ISSN=1664-302X ABSTRACT=

Secondary metabolites are produced mostly by clustered genes that are essential to their biosynthesis. The transcriptional expression of these genes is often cooperatively regulated by a transcription factor located inside or close to a cluster. Most of the secondary metabolism biosynthesis (SMB) gene clusters identified to date contain so-called core genes with distinctive sequence features, such as polyketide synthase (PKS) and non-ribosomal peptide synthetase (NRPS). Recent efforts in sequencing fungal genomes have revealed far more SMB gene clusters than expected based on the number of core genes in the genomes. Several bioinformatics tools have been developed to survey SMB gene clusters using the sequence motif information of the core genes, including SMURF and antiSMASH. More recently, accompanied by the development of sequencing techniques allowing to obtain large-scale genomic and transcriptomic data, motif-independent prediction methods of SMB gene clusters, including MIDDAS-M, have been developed. Most these methods detect the clusters in which the genes are cooperatively regulated at transcriptional levels, thus allowing the identification of novel SMB gene clusters regardless of the presence of the core genes. Another type of the method, MIPS-CG, uses the characteristics of SMB genes, which are highly enriched in non-syntenic blocks (NSBs), enabling the prediction even without transcriptome data although the results have not been evaluated in detail. Considering that large portion of SMB gene clusters might be sufficiently expressed only in limited uncommon conditions, it seems that prediction of SMB gene clusters by bioinformatics and successive experimental validation is an only way to efficiently uncover hidden SMB gene clusters. Here, we describe and discuss possible novel approaches for the determination of SMB gene clusters that have not been identified using conventional methods.