Proteomics Data Analysis

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

Two-Dimensional Gel Electrophoresis Image Analysis.- Chemometric Tools for 2D-PAGE Data Analysis.- Software Options for the Analysis of MS Proteomic Data.- Analysis of Label-Based Quantitative Proteomics Data Using IsoProt.- Quantification of Changes in Protein Expression Using SWATH Proteomics.-Data Processing and Analysis for DIA-Based Phosphoproteomics Using Spectronaut.- Enhanced Glycopeptide Identification Using a GlyConnect Compozitor-Derived Glycan Composition File.- Elaboration Pipeline for the Management of MALDI-MS Imaging Datasets.- Features Selection and Extraction in Statistical Analysis of Proteomics Datasets.- ORA, FCS, and PT Strategies in Functional Enrichment Analysis.- A Strategy for the Annotation and GO Enrichment Analysis of a List of Differentially Expressed Proteins Using ProteoRE.- Protein Subcellular Localization Prediction.- Protein Secretion Prediction Tools and Extracellular Vesicles Databases.- Databases for Protein-Protein Interactions.- Machine and DeepLearning for Prediction of Subcellular Localization.- Deep Learning for Protein-Protein Interaction Site Prediction.- Integrative Analysis of Incongruous Cancer Genomics and Proteomics Datasets.- Integration of Proteomics and Other Omics Data.