Skip to main content
SpringerLink
Log in

Proteomics Data Analysis

  • Book
  • © 2021

Overview

Editors:
  1. Daniela Cecconi

    1. Department of Biotechnology, University of Verona, VERONA, Italy

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Includes cutting-edge techniques
  • Provides step-by-step detail essential for reproducible results
  • Contains key implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 2361)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (19 protocols)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Data Analysis for Gel-Based Proteomics

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Two-Dimensional Gel Electrophoresis Image Analysis

      • Elisa Robotti, Elisa Calà, Emilio Marengo
      Pages 3-13

    3. Chemometric Tools for 2D-PAGE Data Analysis

      • Elisa Robotti, Elisa Calà, Emilio Marengo
      Pages 15-31

  3. Data Analysis for Gel-Free Proteomics

    1. Front Matter

      Pages 33-33
      Download chapter PDF

    2. Software Options for the Analysis of MS-Proteomic Data

      • Avinash Yadav, Federica Marini, Alessandro Cuomo, Tiziana Bonaldi
      Pages 35-59

    3. Analysis of Label-Based Quantitative Proteomics Data Using IsoProt

      • Johannes Griss, Veit Schwämmle
      Pages 61-73

    4. Quantification of Changes in Protein Expression Using SWATH Proteomics

      • Clarissa Braccia, Nara Liessi, Andrea Armirotti
      Pages 75-94

    5. Data Processing and Analysis for DIA-Based Phosphoproteomics Using Spectronaut

      • Ana Martinez-Val, Dorte Breinholdt Bekker-Jensen, Alexander Hogrebe, Jesper Velgaard Olsen
      Pages 95-107

    6. Glycan Compositions with GlyConnect Compozitor to Enhance Glycopeptide Identification

      • Julien Mariethoz, Catherine Hayes, Frédérique Lisacek
      Pages 109-127

    7. Elaboration Pipeline for the Management of MALDI-MS Imaging Datasets

      • Andrew Smith, Isabella Piga, Vanna Denti, Clizia Chinello, Fulvio Magni
      Pages 129-142

    8. Features Selection and Extraction in Statistical Analysis of Proteomics Datasets

      • Marta Lualdi, Mauro Fasano
      Pages 143-159

  4. Proteomics Data Interpretation

    1. Front Matter

      Pages 161-161
      Download chapter PDF

    2. ORA , FCS , and PT Strategies in Functional Enrichment Analysis

      • Marco Fernandes, Holger Husi
      Pages 163-178

    3. GO Enrichment Analysis  for Differential Proteomics Using ProteoRE

      • Florence Combes, Valentin Loux, Yves Vandenbrouck
      Pages 179-196

    4. Protein Subcellular Localization Prediction

      • Elettra Barberis, Emilio Marengo, Marcello Manfredi
      Pages 197-212

    5. Protein Secretion Prediction Tools and Extracellular Vesicles Databases

      • Daniela Cecconi, Claudia Di Carlo, Jessica Brandi
      Pages 213-227

    6. Databases for Protein–Protein Interactions

      • Natsu Nakajima, Tatsuya Akutsu, Ryuichiro Nakato
      Pages 229-248

    7. Machine and Deep Learning for Prediction of Subcellular Localization

      • Gaofeng Pan, Chao Sun, Zijun Liao, Jijun Tang
      Pages 249-261

    8. Deep Learning for Protein–Protein Interaction Site Prediction

      • Arian R. Jamasb, Ben Day, Cătălina Cangea, Pietro Liò, Tom L. Blundell
      Pages 263-288Open Access
Back to top

Keywords

About this book

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.

Editors and Affiliations

  • Department of Biotechnology, University of Verona, VERONA, Italy

    Daniela Cecconi

Bibliographic Information

  • Book Title: Proteomics Data Analysis

  • Editors: Daniela Cecconi

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-1641-3

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2021

  • Hardcover ISBN: 978-1-0716-1640-6Published: 09 July 2021

  • Softcover ISBN: 978-1-0716-1643-7Published: 09 July 2022

  • eBook ISBN: 978-1-0716-1641-3Published: 08 July 2021

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XIII, 326

  • Number of Illustrations: 8 b/w illustrations, 46 illustrations in colour

  • Topics: Chemistry/Food Science, general, Bioinformatics, Statistics, general

Publish with us

Policies and ethics

Back to top

Search

Navigation