E-Book, Englisch, Band 2624, 262 Seiten, eBook
Reihe: Methods in Molecular Biology
Oliveira Computational Epigenomics and Epitranscriptomics
Erscheinungsjahr 2023
ISBN: 978-1-0716-2962-8
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 2624, 262 Seiten, eBook
Reihe: Methods in Molecular Biology
ISBN: 978-1-0716-2962-8
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Weitere Infos & Material
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.
