Titlebook: Computational Epigenomics and Epitranscriptomics; Pedro H. Oliveira Book 2023 The Editor(s) (if applicable) and The Author(s), under exclu

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https://doi.org/10.1007/978-3-642-93418-6utional layers to achieve simultaneously a large sequence context while interpreting the DNA sequence at single base pair resolution. Using transfer learning of convolutional weights trained to predict a compendium of chromatin features across cell types allows deepC to predict cell type-specific ch

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https://doi.org/10.1007/978-3-658-28778-8cts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data.

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Walter Bien,Angela Hartl,Markus Teubnerse of methylation information from neighboring sites to recover partially observed methylation patterns. Our method and software are proven to be faster and more accurate among all evaluated. Ultimately, our method allows for a more streamlined monitoring of epigenetic changes within cellular populations and their putative role in disease.

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Integrating Single-Cell Methylome and Transcriptome Data with MAPLE,cts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data.

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1064-3745 ation advice from the experts.This volume details state-of-the-art computational methods designed to manage, analyze, and generally leverage epigenomic and epitranscriptomic data. Chapters guide readers through fine-mapping and quantification of modifications, visual analytics, imputation methods, s

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