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• W2968752241 abstract "Significance Our new method uses only epigenomic patterns to classify the expression potential of annotated genes and identifies pseudogenes that are difficult to classify based solely on sequence. Genes were divided into those with protein expression, those with mRNA expression, and those that are silent. A large fraction of annotated genes are constitutively silent in one lineage but can be transcribed in others. We refer to the species-wide set of transcribed genes as the expressome and show that it is much larger than the expressible gene set in any individual. Additionally, we find that DNA methylation patterns within the gene body can differentiate between genes that express proteins and genes that express only RNAs." @default.
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• W2968752241 date "2019-08-16" @default.
• W2968752241 modified "2023-09-30" @default.
• W2968752241 title "Identification of the expressome by machine learning on omics data" @default.
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• W2968752241 doi "https://doi.org/10.1073/pnas.1813645116" @default.
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