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Publication for MIDDAS-M
Heterologous Production of a Novel Cyclic Peptide Compound, KK 1, in Aspergillus oryzae
[…] potential secondary metabolites in filamentous fungi. Moreover, another novel program, which is independent of the known sequence motifs of core genes for potential secondary metabolites and is named MIDDAS-M (motif-independent de novo detection algorithm for secondary metabolite biosynthetic gene clusters), has been constructed to predict the gene clusters required for secondary metabolite produc […]
Temperature during conidiation affects stress tolerance, pigmentation, and trypacidin accumulation in the conidia of the airborne pathogen Aspergillus fumigatus
[…] The gene clusters whose transcript levels are coordinately changed in the conidia cultured at 25°C or 45°C compared to those at 37°C were investigated using the MIDDAS-M algorithm . Briefly, the induction ratio of genes at 25°C or 45°C over 37°C were evaluated in the logarithmic form with the base of 2 using the transcriptome data. After the Z-score normali […]
Are Some Fungal Volatile Organic Compounds (VOCs) Mycotoxins?
[…] other secondary metabolite gene clusters can be identified through gene expression patterns detected by EST, microarray or RNA-Seq data. Motif Independent Detection Algorithm for Secondary Products (MIDDAS-M) was developed based on this kind of transcriptome analysis. The MIDDAS-M program has the advantage that it detects only those secondary metabolite gene clusters that are expressed .In summ […]
Motif independent de novo detection of secondary metabolite gene clusters—toward identification from filamentous fungi
[…] les of producing and non-producing conditions and successively comparing these profiles should be an effective way to identify SMB gene clusters. Based on this idea, another motif-independent method, MIDDAS-M, was developed to detect gene clusters, whose component genes are co-regulated (Umemura et al., ). This method uses induction ratios of whole genes under compound producing over non-producing […]
Association of fungal secondary metabolism and sclerotial biology
[…] bioinformatics, has provided researchers with an in silico approach for identifying potential secondary metabolic gene clusters (; ; ). Many of the prediction algorithms (e.g., SMURF, antiSMASH, and MIDDAS-M) in use are based on identification of core or “backbone” genes that encode enzymes such as a PKSs, NRPSs, or dimethylallyltryptophans (DMATs) as well as closely allied genes encoding “decora […]
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