Peptide Detectability Predictor
DQmodel (Detectability and Quantity model) is a software for fitting artificial neural network models for peptide detectability, which is a probabilistic measure of the sequence preferences of identified peptide in Shotgun proteomics experiments. The model considers protein quantity and thus can model effective detectability and improve standard detectability prediction. From machine learning point of views, the model explicitly estimates the values for the hidden variables - protein quantities, each of which is shared by a subset of instances - peptides from each protein - in the training dataset. From proteomics point of view, effective detectability which combines standard detectability and protein quantity is a more sample specific measure compared with standard detectability or the concept of proteotypic peptide.
DQmodel currently has the following functionalities:
A previous version of peptide detectability predictor is available here, its prediction is based on a hard-wired pre-trained model trained on a sample of 12 standard proteins at equal abundances.
Yong Fuga Li, Randy J. Arnold, Haixu Tang, and Predrag Radivojac (2010). The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics. Journal of Proteome Research. 9(12): 6288–6297. PDF
Yong Fuga Li, Randy Arnold, Yixue Li, Predrag Radivojac, Quanhu Sheng and Haixu Tang. A Bayesian approach to protein inference problem in shotgun proteomics. J Comput Biol (2009) 16(8): 1-11. PDF
Yong Fuga Li, Randy Arnold, Yixue Li, Predrag Radivojac, Quanhu Sheng and Haixu Tang. A Bayesian approach to protein inference problem in shotgun proteomics. RECOMB 2008; & LNBI 4955, pp. 167 - 180, 2008. PDF
H. Tang, R. J. Arnold, P. Alves, Z. Xun, D. E. Clemmer, M. V. Novotny, J. P. Reilly and P. Radivojac, A computational approach toward label-free protein quantification using predicted peptide detectability. ISMB (Supplement of Bioinformatics) 2006: 481-488
Last modified: 09/07/2010, Questions?