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Data analysis techniques in phosphoproteomics
Author(s) -
MeyerBaese Anke,
Wildberger Joachim,
MeyerBaese Uwe,
Nilsson Carol L.
Publication year - 2014
Publication title -
electrophoresis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.666
H-Index - 158
eISSN - 1522-2683
pISSN - 0173-0835
DOI - 10.1002/elps.201400219
Subject(s) - phosphoproteomics , computer science , data science , computational biology , data mining , proteome , machine learning , bioinformatics , biology , biochemistry , protein kinase a , protein phosphorylation , enzyme
The interpretation of phosphoproteomics data sets is crucial for generating hypotheses that guide therapeutic solutions, yet not many techniques have been applied to this type of analysis. This paper intends to give an overview about the two main standard techniques that can be applied to the analysis of these large scale data sets. These are data‐driven or exploratory techniques based on a statistical model and topology‐driven methods that analyze the signaling network from a dynamical standpoint. While employing different paradigms, these algorithms will detect unique “fingerprints” by revealing the intricate interactions at the proteome level and will support the experimental environment for novel therapeutics for many diseases.