Computational Epigenomics and Epitranscriptomics

Herausgegeben:Oliveira, Pedro H.

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Methods in Molecular Biology 2624
• Verlag: Humana / Springer US / Springer, Berlin
• Artikelnr. des Verlages: 978-1-0716-2961-1
• 1st ed. 2023
• Seitenzahl: 276
• Erscheinungstermin: 2. Februar 2023
• Englisch
• Abmessung: 260mm x 183mm x 21mm
• Gewicht: 722g
• ISBN-13: 9781071629611
• ISBN-10: 1071629611
• Artikelnr.: 66002818

Inhaltsangabe

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 ( ) in RNA by HydraPsiSeq.- Analysis of RNA sequences and modifications using NASE.- Mapping of RNA modifications by direct Nanopore sequencing and JACUSA2.