Computational Epigenomics and Epitranscriptomics

by Pedro H. Oliveira (Editor)

Overview

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 prools, and tips on troubleshooting and avoiding known pitfalls.

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

Product Details

ISBN-13:	9781071629642
Publisher:	Springer US
Publication date:	02/02/2024
Series:	Methods in Molecular Biology , #2624
Edition description:	1st ed. 2023
Pages:	262
Product dimensions:	7.01(w) x 10.00(h) x (d)

Table of Contents

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.