Computational Epigenomics and Epitranscriptomics

Pedro H. Oliveira

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English

Springer-Verlag New York Inc.
02 February 2024

Genetics (non-medical); Molecular biology

Series: Methods in Molecular Biology

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, supervised analysis, and integrative approaches for single-cell data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and thorough, Computational Epigenomics and Epitranscriptomics aims to provide an overview of epiomic protocols, making it easier for researchers to extract impactful biological insight from their data.

Edited by: Pedro H. Oliveira
Imprint: Springer-Verlag New York Inc.
Country of Publication: United States
Edition: 2023 ed.
Volume: 2624
Dimensions: Height: 254mm, Width: 178mm,
Weight: 529g
ISBN: 9781071629642
ISBN 10: 1071629646
Series: Methods in Molecular Biology
Pages: 262
Publication Date: 02 February 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

DNA methylation data analysis using Msuite.- Interactive DNA methylation arrays analysis with ShinyÉPICo.- Predicting Chromatin Interactions from DNA Sequence using DeepC.- Integrating single-cell methylome and transcriptome data with MAPLE.- Quantitative comparison of multiple chromatin immunoprecipitation-sequencing (ChIP-seq) experiments with spikChIP.- A Guide To MethylationToActivity: A Deep-Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes In Individual Tumors.- DNA modification patterns filtering and analysis using DNAModAnnot.- Methylome imputation by methylation patterns.- Sequoia: a framework for visual analysis of RNA modifications from direct RNA sequencing data.- Predicting pseudouridine sites with Porpoise.- Pseudouridine Identification and Functional Annotation with PIANO.- Analyzing mRNA epigenetic sequencing data with TRESS.- Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores.- Data Analysis Pipeline for Detection and Quantification of Pseudouridine (ψ) in RNA by HydraPsiSeq.- Analysis of RNA sequences and modifications using NASE.- Mapping of RNA modifications by direct Nanopore sequencing and JACUSA2.