Computational and Statistical Methods for High-Throughput Mass Spectrometry-Based PTM Analysis.
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.
Protein Research Group, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230, Odense, Denmark. veits@bmb.sdu.dk.
Veit
V
Schwämmle
Proteomics Unit, Department of Biomedicine, University of Bergen, Jonas Lies vei 91, 5020, Bergen, Norway.
Marc
M
Vaudel
D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D030562:Databases, Protein; D013058:Mass Spectrometry; D011499:Protein Processing, Post-Translational; D011506:Proteins / Q000737:chemistry / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D057386:Search Engine; D012984:Software; D014584:User-Computer Interface; D064878:Web Browser
D011506:Proteins
Bioinformatics; Post-translational modifications (PTMs); Proteomics