Computational Epigenomics and Epitranscriptomics

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213,99 

Methods in Molecular Biology 2624

ISBN: 1071629611
ISBN 13: 9781071629611
Herausgeber: Pedro H Oliveira
Verlag: Springer Verlag GmbH
Umfang: xi, 262 S., 4 s/w Illustr., 61 farbige Illustr., 262 p. 65 illus., 61 illus. in color.
Erscheinungsdatum: 02.02.2023
Auflage: 1/2023
Produktform: Gebunden/Hardback
Einband: Gebunden

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. Cuttingedge 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.

Artikelnummer: 7185057 Kategorie:

Beschreibung

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.   Cuttingedge 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.

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