Transcriptome Data Analysis (Methods in Molecular Biology, 2812)
Language: English
Published by Springer (India) Private Limited, 2024
- Hardcover
- New
Seller: Biblios, frankfurt am main, hessen, GermanyBiblios
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- Title
- Transcriptome Data Analysis (Methods in Molecular Biology, 2812)
- Author
- Azad Rajeev K.
- Publisher
- Springer (India) Private Limited
- Publication year
- 2024
- Condition
- New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1071638858
- ISBN 13
- 9781071638859
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
Biblios
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