TY - JOUR
T1 - Refining comparative proteomics by spectral counting to account for shared peptides and multiple search engines
AU - Chen, Yao Yi
AU - Dasari, Surendra
AU - Ma, Ze Qiang
AU - Vega-Montoto, Lorenzo J.
AU - Li, Ming
AU - Tabb, David L.
N1 - Funding Information:
Acknowledgments D. L. Tabb and Y.-Y. Chen were supported by U01 CA152647 from the National Cancer Institute. S. Dasari, Z.-Q. Ma, and L. J. Vega-Montoto were supported by R01 CA126218 from the National Cancer Institute.
PY - 2012/9
Y1 - 2012/9
N2 - 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.
AB - 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.
KW - Combining database search engines
KW - Label-free comparative proteomics
KW - Spectral counting
UR - http://www.scopus.com/inward/record.url?scp=84865611313&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84865611313&partnerID=8YFLogxK
U2 - 10.1007/s00216-012-6011-x
DO - 10.1007/s00216-012-6011-x
M3 - Article
C2 - 22552787
AN - SCOPUS:84865611313
SN - 1618-2642
VL - 404
SP - 1115
EP - 1125
JO - Analytical and bioanalytical chemistry
JF - Analytical and bioanalytical chemistry
IS - 4
ER -