A bayesian approach to protein inference problem in shotgun proteomics.
The protein inference problem represents a major challenge in shotgun proteomics. In this article, we describe a novel Bayesian approach to address this challenge by incorporating the predicted peptide detectabilities as the prior probabilities of peptide identification. We propose a rigorious probabilistic model for protein inference and provide practical algoritmic solutions to this problem. We used a complex synthetic protein mixture to test our method and obtained promising results.
Li
Yong Fuga
YF
School of Informatics, Indiana University , Bloomington, IN 47408, USA.
Arnold
Randy J
RJ
Li
Yixue
Y
Radivojac
Predrag
P
Sheng
Quanhu
Q
Tang
Haixu
H
D000465:Algorithms; D030562:Databases, Protein; D015233:Models, Statistical; D040901:Proteomics / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*