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Computational Epigenomics and Epitranscriptomics 2023 ed. [Pehme köide]

  • Formaat: Paperback / softback, 262 pages, kõrgus x laius: 254x178 mm, kaal: 529 g, 61 Illustrations, color; 4 Illustrations, black and white; XI, 262 p. 65 illus., 61 illus. in color., 1 Paperback / softback
  • Sari: Methods in Molecular Biology 2624
  • Ilmumisaeg: 02-Feb-2024
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1071629646
  • ISBN-13: 9781071629642
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  • Formaat: Paperback / softback, 262 pages, kõrgus x laius: 254x178 mm, kaal: 529 g, 61 Illustrations, color; 4 Illustrations, black and white; XI, 262 p. 65 illus., 61 illus. in color., 1 Paperback / softback
  • Sari: Methods in Molecular Biology 2624
  • Ilmumisaeg: 02-Feb-2024
  • Kirjastus: Springer-Verlag New York Inc.
  • ISBN-10: 1071629646
  • ISBN-13: 9781071629642
Teised raamatud teemal:
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.
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.