Proteomics data analysis
著者
書誌事項
Proteomics data analysis
（Methods in molecular biology / John M. Walker, series editor, 2361）（Springer protocols）
Humana Press, 2021
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
Part I: Data Analysis for Gel-Based Proteomics
1. Two-Dimensional Gel Electrophoresis Image Analysis
Elisa Robotti, Elisa Cala, and Emilio Marengo
2. Chemometric Tools for 2D-PAGE Data Analysis
Elisa Robotti, Elisa Cala, and Emilio Marengo
Part II: Data Analysis for Gel-Free Proteomics
3. Software Options for the Analysis of MS Proteomic Data
Avinash Yadav, Federica Marini, Alessandro Cuomo, and Tiziana Bonaldi
4. Analysis of Label-Based Quantitative Proteomics Data Using IsoProt
Johannes Griss and Veit Schwammle
5. Quantification of Changes in Protein Expression Using SWATH Proteomics
Clarissa Braccia, Nara Liessi, and Andrea Armirotti
6. Data Processing and Analysis for DIA-Based Phosphoproteomics Using Spectronaut
Ana Martinez-Val, Dorte Breinholdt Bekker-Jensen, Alexander Hogrebe, and Jesper Velgaard Olsen
7. Enhanced Glycopeptide Identification Using a GlyConnect Compozitor-Derived Glycan Composition File
Julien Mariethoz, Catherine Hayes, and Frederique Lisacek
8. Elaboration Pipeline for the Management of MALDI-MS Imaging Datasets
Andrew Smith, Isabella Piga, Vanna Denti, Clizia Chinello, and Fulvio Magni
9. Features Selection and Extraction in Statistical Analysis of Proteomics Datasets
Marta Lualdi and Mauro Fasano
Part III: Proteomics Data Interpretation
10. ORA, FCS, and PT Strategies in Functional Enrichment Analysis
Marco Fernandes and Holger Husi
11. A Strategy for the Annotation and GO Enrichment Analysis of a List of Differentially Expressed Proteins Using ProteoRE
Florence Combes, Valentin Loux, and Yves Vandenbrouck
12. Protein Subcellular Localization Prediction
Elettra Barberis, Emilio Marengo, and Marcello Manfredi
13. Protein Secretion Prediction Tools and Extracellular Vesicles Databases
Daniela Cecconi, Claudia Di Carlo, and Jessica Brandi
14. Databases for Protein-Protein Interactions
Natsu Nakajima, Tatsuya Akutsu, and Ryuichiro Nakato
15. Machine and Deep Learning for Prediction of Subcellular Localization
Gaofeng Pan, Chao Sun, Zijun Liao, and Jijun Tang
16. Deep Learning for Protein-Protein Interaction Site Prediction
Arian R. Jamasb, Ben Day, Catalina Cangea, Pietro Lio, and Tom L. Blundell
Part IV: Proteomics Data Integration with Other -Omics
17. Integrative Analysis of Incongruous Cancer Genomics and Proteomics Datasets
Karla Cervantes-Gracia, Richard Chahwan, and Holger Husi
18. Integration of Proteomics and Other Omics Data
Mengyun Wu, Yu Jiang, and Shuangge Ma
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