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Total variance should drive data handling strategies in third generation proteomic studies
Author(s) -
Herrmann Abigail G.,
Searcy James L.,
Le Bihan Thierry,
McCulloch James,
Deighton Ruth F.
Publication year - 2013
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201300056
Subject(s) - proteomics , variance (accounting) , upstream (networking) , quantitative proteomics , computer science , computational biology , biology , biochemistry , computer network , accounting , gene , business
Quantitative proteomics is entering its “third generation,” where intricate experimental designs aim to increase the spatial and temporal resolution of protein changes. This paper re‐analyses multiple internally consistent proteomic datasets generated from whole cell homogenates and fractionated brain tissue samples providing a unique opportunity to explore the different factors influencing experimental outcomes. The results clearly indicate that improvements in data handling are required to compensate for the increased mean CV associated with complex study design and intricate upstream tissue processing. Furthermore, applying arbitrary inclusion thresholds such as fold change in protein abundance between groups can lead to unnecessary exclusion of important and biologically relevant data.