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Bioinformatics. 2016 Apr 15;32(8):1138-43. doi: 10.1093/bioinformatics/btv713. Epub 2015 Dec 9.

CASSIS and SMIPS: promoter-based prediction of secondary metabolite gene clusters in eukaryotic genomes.

Author information

1
Research Group Systems Biology/Bioinformatics, Leibniz Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute (HKI), Jena 07745, Germany.

Abstract

MOTIVATION:

Secondary metabolites (SM) are structurally diverse natural products of high pharmaceutical importance. Genes involved in their biosynthesis are often organized in clusters, i.e., are co-localized and co-expressed. In silico cluster prediction in eukaryotic genomes remains problematic mainly due to the high variability of the clusters' content and lack of other distinguishing sequence features.

RESULTS:

We present Cluster Assignment by Islands of Sites (CASSIS), a method for SM cluster prediction in eukaryotic genomes, and Secondary Metabolites by InterProScan (SMIPS), a tool for genome-wide detection of SM key enzymes ('anchor' genes): polyketide synthases, non-ribosomal peptide synthetases and dimethylallyl tryptophan synthases. Unlike other tools based on protein similarity, CASSIS exploits the idea of co-regulation of the cluster genes, which assumes the existence of common regulatory patterns in the cluster promoters. The method searches for 'islands' of enriched cluster-specific motifs in the vicinity of anchor genes. It was validated in a series of cross-validation experiments and showed high sensitivity and specificity.

AVAILABILITY AND IMPLEMENTATION:

CASSIS and SMIPS are freely available at https://sbi.hki-jena.de/cassis

CONTACT:

thomas.wolf@leibniz-hki.de or ekaterina.shelest@leibniz-hki.de

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26656005
PMCID:
PMC4824125
DOI:
10.1093/bioinformatics/btv713
[Indexed for MEDLINE]
Free PMC Article

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