Description
Book SynopsisThis 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.
Table of Contents1. DNA methylation data analysis using Msuite
Xiaojian Liu, Pengxiang Yuan, and Kun Sun
2. Interactive DNA methylation arrays analysis with ShinyÉPICo
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 Ballaré, 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 (ψ) 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
