Proteomics Data Analysis

Buch | Softcover

326 Seiten

2022 | 1st ed. 2021
Springer-Verlag New York Inc.
978-1-0716-1643-7 (ISBN)

Artikel merken

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.

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.

Erscheinungsdatum	13.07.2022
Reihe/Serie	Methods in Molecular Biology ; 2361
Zusatzinfo	46 Illustrations, color; 8 Illustrations, black and white; XIII, 326 p. 54 illus., 46 illus. in color.
Verlagsort	New York, NY
Sprache	englisch
Maße	178 x 254 mm
Themenwelt	Informatik ► Weitere Themen ► Bioinformatik
	Mathematik / Informatik ► Mathematik
	Naturwissenschaften ► Biologie ► Biochemie
	Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie
	Naturwissenschaften ► Chemie
ISBN-10	1-0716-1643-9 / 1071616439
ISBN-13	978-1-0716-1643-7 / 9781071616437
Zustand	Neuware