2013
DOI: 10.1371/journal.pone.0084028
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MIDDAS-M: Motif-Independent De Novo Detection of Secondary Metabolite Gene Clusters through the Integration of Genome Sequencing and Transcriptome Data

Abstract: Many bioactive natural products are produced as “secondary metabolites” by plants, bacteria, and fungi. During the middle of the 20th century, several secondary metabolites from fungi revolutionized the pharmaceutical industry, for example, penicillin, lovastatin, and cyclosporine. They are generally biosynthesized by enzymes encoded by clusters of coordinately regulated genes, and several motif-based methods have been developed to detect secondary metabolite biosynthetic (SMB) gene clusters using the sequence… Show more

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Cited by 103 publications
(92 citation statements)
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“…The influence of temperature or water activity combined with RNA-seq has been used to examine small non-coding RNAs 62) , development 63) and SM production 64,65) . Yu et al 65) showed 10 of the 55 gene clusters had higher expression at 30°C as compared to 37°C (cluster 1,10,11,19,20,21,24,45,54, and 55) and cluster 3 decreasing at lower temperatures. Medina et al 64) found genome-wide, a decreased transcriptome response to changing environmental conditions correlating with low water activity or high temperature, with water being the primary predictor.…”
Section: -2 Rna Sequencingmentioning
confidence: 99%
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“…The influence of temperature or water activity combined with RNA-seq has been used to examine small non-coding RNAs 62) , development 63) and SM production 64,65) . Yu et al 65) showed 10 of the 55 gene clusters had higher expression at 30°C as compared to 37°C (cluster 1,10,11,19,20,21,24,45,54, and 55) and cluster 3 decreasing at lower temperatures. Medina et al 64) found genome-wide, a decreased transcriptome response to changing environmental conditions correlating with low water activity or high temperature, with water being the primary predictor.…”
Section: -2 Rna Sequencingmentioning
confidence: 99%
“…Finally, MIDDAS-M does not utilize characterized motifs as a basis for prediction 10) . Rather, it utilizes both genomic and transcriptomic data to calculate expression ratios of neighboring genes, and large induction ratios to indicate cluster activity.…”
Section: -3 Bioinformatic Tools Used For Cluster Prediction In a Fmentioning
confidence: 99%
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“…It has been a very clear result obtained after the bioinformatic studies of genomes sequenced from filamentous fungi that they have many apparently silent gene clusters potentially coding the production of "new" secondary metabolites [59, 123,125,[132][133][134][135][136][137][138][139][140]. This is, of course, of great interest for the area of chemotaxonomy, as an expression of a more diverse sampling of secondary metabolites will help classifying the different species even better.…”
Section: Silent Gene Clusters For Secondary Metabolitesmentioning
confidence: 99%