A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments.

Abstract

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise… (More)
DOI: 10.1089/omi.2012.0019

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@article{Richardson2012APF, title={A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments.}, author={Keith G. Richardson and Richard Denny and Chris J Hughes and John Skilling and Jacek Sikora and Michał Dadlez and {\'A}ngel Manteca and Hye Ryung Jung and Ole N\orregaard Jensen and Virginie Redeker and Ronald Melki and James I. Langridge and Johannes P. C. Vissers}, journal={Omics : a journal of integrative biology}, year={2012}, volume={16 9}, pages={468-82} }