+ Site Statistics
+ Search Articles
+ PDF Full Text Service
How our service works
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ Translate
+ Recently Requested

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



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



Omics 16(9): 468-482



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 contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.

Please choose payment method:






(PDF emailed within 0-6 h: $19.90)

Accession: 051218946

Download citation: RISBibTeXText

PMID: 22871168

DOI: 10.1089/omi.2012.0019


Related references

Protein quantification by peptide quality control (PQPQ) of shotgun proteomics data. Methods in Molecular Biology 1023: 149-158, 2014

Generation of accurate peptide retention data for targeted and data independent quantitative LC-MS analysis: Chromatographic lessons in proteomics. Proteomics 16(23): 2931-2936, 2016

Enhanced information output from shotgun proteomics data by protein quantification and peptide quality control (PQPQ). Molecular and Cellular Proteomics 10(10): M111.010264, 2012

A probabilistic framework to improve microrna target prediction by incorporating proteomics data. Journal of Bioinformatics and Computational Biology 7(6): 955-972, 2010

A Probabilistic Framework To Improve Microrna Target Prediction By Incorporating Proteomics Data. Journal of Bioinformatics and Computational Biology 7(6): 955-972, 2009

Using Data Independent Acquisition (DIA) to Model High-responding Peptides for Targeted Proteomics Experiments. Molecular and Cellular Proteomics 14(9): 2331-2340, 2016

A simulation framework for correlated count data of features subsets in high-throughput sequencing or proteomics experiments. Statistical Applications in Genetics and Molecular Biology 15(5): 401-414, 2016

A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments. Proteomics 13(3-4): 493-503, 2013

Micro-Data-Independent Acquisition for High-Throughput Proteomics and Sensitive Peptide Mass Spectrum Identification. Analytical Chemistry 90(15): 8905-8911, 2018

Mining the Secretome of C2C12 Muscle Cells: Data Dependent Experimental Approach To Analyze Protein Secretion Using Label-Free Quantification and Peptide Based Analysis. Journal of Proteome Research 17(2): 879-890, 2018

A probabilistic framework to predict protein function from interaction data integrated with semantic knowledge. Bmc Bioinformatics 9: 382, 2008

A Data Analysis Protocol for Quantitative Data-Independent Acquisition Proteomics. Methods in Molecular Biology 1871: 455-465, 2018

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. Journal of Proteomics 129: 108-120, 2016

Improving the identification rate of data independent label-free quantitative proteomics experiments on non-model crops: a case study on apple fruit. Journal of Proteomics 105: 31-45, 2015

Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers. Proteomics 16(15-16): 2257-2271, 2017