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

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 ma...

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Bibliographic Details
Published in:Bioinformatics Vol. 32; no. 8; pp. 1138 - 1143
Main Authors: Wolf, Thomas, Shelest, Vladimir, Nath, Neetika, Shelest, Ekaterina
Format: Journal Article
Language:English
Published: England Oxford University Press 15.04.2016
Subjects:
Anchors
Bioinformatics
Clusters
Eukaryota
Genes
Genomes
Islands
Metabolites
Multigene Family
Original Papers
Peptide Synthases - genetics
Polyketide Synthases - genetics
Searching
Software
ISSN:1367-4803, 1367-4811, 1460-2059
Online Access:Get full text
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Summary: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.
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Associate Editor: Alfonso Valencia
ISSN:1367-4803
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btv713