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Chapter title |
Computational and Statistical Methods for High-Throughput Mass Spectrometry-Based PTM Analysis
|
---|---|
Chapter number | 21 |
Book title |
Protein Bioinformatics
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Published in |
Methods in molecular biology, February 2017
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DOI | 10.1007/978-1-4939-6783-4_21 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6781-0, 978-1-4939-6783-4
|
Authors |
Veit Schwämmle, Marc Vaudel |
Editors |
Cathy H. Wu, Cecilia N. Arighi, Karen E. Ross |
Abstract |
Cell signaling and functions heavily rely on post-translational modifications (PTMs) of proteins. Their high-throughput characterization is thus of utmost interest for multiple biological and medical investigations. In combination with efficient enrichment methods, peptide mass spectrometry analysis allows the quantitative comparison of thousands of modified peptides over different conditions. However, the large and complex datasets produced pose multiple data interpretation challenges, ranging from spectral interpretation to statistical and multivariate analyses. Here, we present a typical workflow to interpret such data. |
Mendeley readers
The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 12 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Professor > Associate Professor | 3 | 25% |
Student > Master | 3 | 25% |
Student > Ph. D. Student | 2 | 17% |
Researcher | 1 | 8% |
Unknown | 3 | 25% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 5 | 42% |
Biochemistry, Genetics and Molecular Biology | 2 | 17% |
Environmental Science | 1 | 8% |
Chemistry | 1 | 8% |
Unknown | 3 | 25% |