Mass Spectrometry Data Analysis in Proteomics

Herausgegeben:Matthiesen, Rune

• Broschiertes Buch

Produktbeschreibung

The aim of this new edition is to provide detailed information on each topic and present novel ideas and views that can influence future developments in mass spectrometry-based proteomics. In contrast to the previous editions, this third edition aims to provide the most relevant computational methods, focusing on computational concepts. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.
Authoritative and cutting-edge, Mass Spectrometry Data Analysis in Proteomics, Third Edition to ensure successful results in the further study of this vital field.

Produktdetails

• Methods in Molecular Biology 2051
• Verlag: Humana / Springer New York / Springer, Berlin
• Artikelnr. des Verlages: 978-1-4939-9746-6
• 3. Aufl.
• Seitenzahl: 456
• Erscheinungstermin: 26. September 2020
• Englisch
• Abmessung: 254mm x 178mm x 25mm
• Gewicht: 851g
• ISBN-13: 9781493997466
• ISBN-10: 1493997467
• Artikelnr.: 60202132

Inhaltsangabe

Introduction to Mass Spectrometry-based Proteomics.- LC-MS Spectra Processing.- Isotopic Distributions.- Retention Time Prediction and Protein Identification.- Comparing Peptide Spectra Matches Across Search Engines.- Calculation of False Discovery Rate for Peptide and Protein Identification.- Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry.- Interpretation of Tandem Mass Spectra of Posttranslationally Modified Peptides.- Solution to Dark Matter Identified by Mass-tolerant Database Search.- Phosphoproteomics Profiling to Identify Altered Signaling Pathways and Kinase-targeted Cancer Therapies.- Mass Spectrometry Based Characterization of Ub- and UbL-modified Proteins.- Targeted Proteomics as a Tool for Quantifying Urine-based Biomarkers.- Data Imputation in Merged Isobaric Labelling-based Relative Quantification Datasets.- Clustering Clinical Data in R.- Review of Issues and Solutions to Data Analysis Reproducibility and Data Qualityin Clinical Proteomics.- Review of Batch Effects Prevention, Diagnostics, and Correction Approaches.- Using the Object-oriented PowerShell for Simple Proteomics Data Analysis.- Considerations in the Analysis of Hydrogen Exchange Mass Spectrometry Data.