Dataset Information

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data.

ABSTRACT: The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the Saccharomyces cerevisiae genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encode species-specific proteins ("orphan-ORFs"); hundreds have mean expression comparable to annotated genes. These data reveal unannotated ORFs most likely to be protein-coding genes. We partitioned a co-expression matrix by Markov Chain Clustering; the resultant clusters contain 2,468 orphan-ORFs. We provide the aggregated RNA-Seq yeast data with extensive metadata as a project in MetaOmGraph (MOG), a tool designed for interactive analysis and visualization. This approach enables reuse of public RNA-Seq data for exploratory discovery, providing a rich context for experimentalists to make novel, experimentally testable hypotheses about candidate genes.

SUBMITTER: Li J

PROVIDER: S-EPMC8415361 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data.

Li Jing J Singh Urminder U Arendsee Zebulun Z Wurtele Eve Syrkin ES

Frontiers in genetics 20210816

The "dark transcriptome" can be considered the multitude of sequences that are transcribed but not annotated as genes. We evaluated expression of 6,692 annotated genes and 29,354 unannotated open reading frames (ORFs) in the <i>Saccharomyces cerevisiae</i> genome across diverse environmental, genetic and developmental conditions (3,457 RNA-Seq samples). Over 30% of the highly transcribed ORFs have translation evidence. Phylostratigraphic analysis infers most of these transcribed ORFs would encod ...[more]

PMID: 34484307

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Project description:Penicillium digitatum is the most severe pathogen that infects citrus fruits during storage. It can cause fruit rot and bring significant economic losses. The continuous use of fungicides has resulted in the emergence of drug-resistant strains. Consequently, there is a need to develop naturally and efficiently antifungal fungicides. Natural antimicrobial agents such as clove oil, cinnamon oil, and thyme oil can be extracted from different plant parts. They exhibited broad-spectrum antimicrobial properties and have great potential in the food industry. Here, we exploit a novel cinnamaldehyde (CA), eugenol (EUG), or carvacrol (CAR) combination antifungal therapy and formulate it into nanoemulsion form to overcome lower solubility and instability of essential oil. In this study, the antifungal activity evaluation and transcriptional profile of Penicillium digitatum exposed to compound nanoemulsion were evaluated. Results showed that compound nanoemulsion had a striking inhibitory effect on P. digitatum in a dose-dependent manner. According to RNA-seq analysis, there were 2,169 differentially expressed genes (DEGs) between control and nanoemulsion-treated samples, including 1,028 downregulated and 1,141 upregulated genes. Gene Ontology (GO) analysis indicated that the DEGs were mainly involved in intracellular organelle parts of cell component: cellular respiration, proton transmembrane transport of biological process, and guanyl nucleotide-binding molecular function. KEGG analysis revealed that metabolic pathway, biosynthesis of secondary metabolites, and glyoxylate and dicarboxylate metabolism were the most highly enriched pathways for these DEGs. Taken together, we can conclude the promising antifungal activity of nanoemulsion with multiple action sites against P. digitatum. These outcomes would deepen our knowledge of the inhibitory mechanism from molecular aspects and exploit naturally, efficiently, and harmlessly antifungal agents in the citrus postharvest industry.

Dataset Information

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data.

Publications

Landscape of the Dark Transcriptome Revealed Through Re-mining Massive RNA-Seq Data.

Similar Datasets

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