Main description:

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:

1. DNA methylation data analysis using Msuite

Xiaojian Liu, Pengxiang Yuan, and Kun Sun

2. Interactive DNA methylation arrays analysis with ShinyEPICo

Octavio Morante-Palacios

3. Predicting Chromatin Interactions from DNA Sequence using DeepC

Ron Schwessinger

4. Integrating single-cell methylome and transcriptome data with MAPLE

Yasin Uzun, Hao Wu, and Kai Tan

5. Quantitative comparison of multiple chromatin immunoprecipitation-sequencing (ChIP-seq) experiments with spikChIP

Enrique Blanco, Cecilia Ballare, Luciano Di Croce, and Sergi Aranda

6. A Guide To MethylationToActivity: A Deep-Learning Framework That Reveals Promoter Activity Landscapes from DNA Methylomes In Individual Tumors

Karissa Dieseldorff Jones, Daniel Putnam, Justin Williams, and Xiang Chen

7. DNA modification patterns filtering and analysis using DNAModAnnot

Alexis Hardy, Sandra Duharcourt, and Matthieu Defrance

8. Methylome imputation by methylation patterns

Ya-Ting Chang, Ming-Ren Yen, and Pao-Yang Chen

9. Sequoia: a framework for visual analysis of RNA modifications from direct RNA sequencing data

Ratanond Koonchanok, Swapna Vidhur Daulatabad, Khairi Reda, and Sarath Chandra Janga

10. Predicting pseudouridine sites with Porpoise

Xudong Guo, Fuyi Li, and Jiangning Song

11. Pseudouridine Identification and Functional Annotation with PIANO

Jiahui Yao, Cuiyueyue Hao, Kunqi Chen, Jia Meng, and Bowen Song

12. Analyzing mRNA epigenetic sequencing data with TRESS

Zhenxing Guo, Andrew M. Shafik, Peng Jin, Zhijin Wu, and Hao Wu

13. Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores

Luca Cozzuto, Anna Delgado-Tejedor, Toni Hermoso Pulido, Eva Maria Novoa,

and Julia Ponomarenko

14. Data Analysis Pipeline for Detection and Quantification of Pseudouridine ( ) in RNA by HydraPsiSeq Florian Pichot, Virginie Marchand, Mark Helm, and Yuri Motorin

15. Analysis of RNA sequences and modifications using NASE

Samuel Wein

16. Mapping of RNA modifications by direct Nanopore sequencing and JACUSA2

Amina Lemsara, Christoph Dieterich, and Isabel Naarmann-de Vries