Transcriptome Data Analysis

Rajeev K. Azad

ISBN 10: 1071638858 / ISBN 13: 9781071638859

Edité par Springer US Jul 2024, 2024

A propos de cet article

Neuware Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 408 pp. Englisch.

N° de réf. du vendeur 9781071638859

Détails bibliographiques

• Titre: Transcriptome Data Analysis
• Éditeur: Springer US Jul 2024
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 1071638858
• ISBN à 13 chiffres: 9781071638859
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 260 L x 183 H x 28 P (en millimètres) 260 millimètres de largeur par 183 millimètres de hauteur par 28 millimètres de profondeur
• Poids: 961 grammes

A propos de ce titre

Synopsis

This detailed volume presents a comprehensive exploration of the advances in transcriptomics, with a focus on methods and pipelines for transcriptome data analysis. In addition to well-established RNA sequencing (RNA-Seq) data analysis protocols, the chapters also examine specialized pipelines, such as multi-omics data integration and analysis, gene interaction network construction, single-cell trajectory inference, detection of structural variants, application of machine learning, and more. As part of the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that leads to best results in the lab. 

 

Authoritative and practical, Transcriptome Data Analysis serves as an ideal resource for educators and researchers looking to understand new developments in the field, learn usage of the protocols for transcriptome data analysis, and implement the tools or pipelines to address relevant problemsof their interest.

 

Chapter 4 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.