A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments

Katherine Richardson, R. Denny, C. Hughes, J. Skilling, J. Sikora, M. Dadlez, Angel Manteca Fernandez, H. R. Jung, O. N. Jensen, V. Redeker, R. Melki, J. I. Langridge, J. P. C. Vissers

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

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.
OriginalsprogEngelsk
TidsskriftO M I C S: A Journal of Integrative Biology
Vol/bind16
Udgave nummer9
Sider (fra-til)468-482
Antal sider15
ISSN1557-8100
DOI
StatusUdgivet - 2012

Citer dette

Richardson, Katherine ; Denny, R. ; Hughes, C. ; Skilling, J. ; Sikora, J. ; Dadlez, M. ; Manteca Fernandez, Angel ; Jung, H. R. ; Jensen, O. N. ; Redeker, V. ; Melki, R. ; Langridge, J. I. ; Vissers, J. P. C. / A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments. I: O M I C S: A Journal of Integrative Biology. 2012 ; Bind 16, Nr. 9. s. 468-482.
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title = "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 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.",
author = "Katherine Richardson and R. Denny and C. Hughes and J. Skilling and J. Sikora and M. Dadlez and {Manteca Fernandez}, Angel and Jung, {H. R.} and Jensen, {O. N.} and V. Redeker and R. Melki and Langridge, {J. I.} and Vissers, {J. P. C.}",
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journal = "O M I C S: A Journal of Integrative Biology",
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Richardson, K, Denny, R, Hughes, C, Skilling, J, Sikora, J, Dadlez, M, Manteca Fernandez, A, Jung, HR, Jensen, ON, Redeker, V, Melki, R, Langridge, JI & Vissers, JPC 2012, 'A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments', O M I C S: A Journal of Integrative Biology, bind 16, nr. 9, s. 468-482. https://doi.org/10.1089/omi.2012.0019

A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments. / Richardson, Katherine; Denny, R.; Hughes, C.; Skilling, J.; Sikora, J.; Dadlez, M.; Manteca Fernandez, Angel ; Jung, H. R.; Jensen, O. N.; Redeker, V.; Melki, R.; Langridge, J. I.; Vissers, J. P. C.

I: O M I C S: A Journal of Integrative Biology, Bind 16, Nr. 9, 2012, s. 468-482.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments

AU - Richardson, Katherine

AU - Denny, R.

AU - Hughes, C.

AU - Skilling, J.

AU - Sikora, J.

AU - Dadlez, M.

AU - Manteca Fernandez, Angel

AU - Jung, H. R.

AU - Jensen, O. N.

AU - Redeker, V.

AU - Melki, R.

AU - Langridge, J. I.

AU - Vissers, J. P. C.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

U2 - 10.1089/omi.2012.0019

DO - 10.1089/omi.2012.0019

M3 - Journal article

VL - 16

SP - 468

EP - 482

JO - O M I C S: A Journal of Integrative Biology

JF - O M I C S: A Journal of Integrative Biology

SN - 1557-8100

IS - 9

ER -