Computational Epigenomics and Epitranscriptomics / edited by Pedro H. Oliveira.

Format:

Book

Contributor:

Oliveira, Pedro H., Editor.

Series:

Methods in Molecular Biology, 1940-6029 ; 2624

Language:

English

Subjects (All):

Epigenetics.

Biomathematics.

Mathematical and Computational Biology.

Local Subjects:

Epigenetics.

Mathematical and Computational Biology.

Physical Description:

1 online resource (XI, 262 p. 65 illus., 61 illus. in color.)

Edition:

1st ed. 2023.

Place of Publication:

New York, NY : Springer US : Imprint: Humana, 2023.

Summary:

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.

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.

ISBN:

9781071629628

107162962X