Journal
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
Volume 404, Issue 4, Pages 1115-1125Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s00216-012-6011-x
Keywords
Label-free comparative proteomics; Spectral counting; Combining database search engines
Funding
- National Cancer Institute [U01 CA152647, R01 CA126218]
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Spectral counting has become a widely used approach for measuring and comparing protein abundance in label-free shotgun proteomics. However, when analyzing complex samples, the ambiguity of matching between peptides and proteins greatly affects the assessment of peptide and protein inventories, differentiation, and quantification. Meanwhile, the configuration of database searching algorithms that assign peptides to MS/MS spectra may produce different results in comparative proteomic analysis. Here, we present three strategies to improve comparative proteomics through spectral counting. We show that comparing spectral counts for peptide groups rather than for protein groups forestalls problems introduced by shared peptides. We demonstrate the advantage and flexibility of this new method in two datasets. We present four models to combine four popular search engines that lead to significant gains in spectral counting differentiation. Among these models, we demonstrate a powerful vote counting model that scales well for multiple search engines. We also show that semi-tryptic searching outperforms tryptic searching for comparative proteomics. Overall, these techniques considerably improve protein differentiation on the basis of spectral count tables.
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