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Proteomics Data Analysis
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Proteomics Data Analysis

2021 ed.

Book Details

Format Hardback or Cased Book
ISBN-10 1071616404
ISBN-13 9781071616406
Edition 2021 ed.
Publisher Springer-Verlag New York Inc.
Imprint Springer-Verlag New York Inc.
Country of Manufacture GB
Country of Publication GB
Publication Date Jul 9th, 2021
Print length 326 Pages
Weight 816 grams
Dimensions 18.10 x 26.00 x 2.80 cms
Ksh 28,800.00
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This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. Authoritative and practical, Proteomics Data Analysis serves as an idealguide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics. Chapter 16 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
This thorough book collects methods and strategies to analyze proteomics data. It is intended to describe how data obtained by gel-based or gel-free proteomics approaches can be inspected, organized, and interpreted to extrapolate biological information. Organized into four sections, the volume explores strategies to analyze proteomics data obtained by gel-based approaches, different data analysis approaches for gel-free proteomics experiments, bioinformatic tools for the interpretation of proteomics data to obtain biological significant information, as well as methods to integrate proteomics data with other omics datasets including genomics, transcriptomics, metabolomics, and other types of data. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detailed implementation advice that will ensure high quality results in the lab. 

Authoritative and practical, Proteomics Data Analysis serves as an ideal guide to introduce researchers, both experienced and novice, to new tools and approaches for data analysis to encourage the further study of proteomics.

Chapter 16 is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

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