Proteomics Data Analysis

ISBN-13: 9781071616406 / Angielski / Twarda / 2021 / 326 str.

Daniela Cecconi

Spis treści

Part I: Data Analysis for Gel-Based Proteomics

 

1. Two-Dimensional Gel Electrophoresis Image Analysis

            Elisa Robotti, Elisa Calà, and Emilio Marengo

 

2. Chemometric Tools for 2D-PAGE Data Analysis

            Elisa Robotti, Elisa Calà, 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 Schwämmle

 

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 Frédérique 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, Cătălina Cangea, Pietro Liò, 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

Blurb

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.