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ProteoModlR for functional proteomic analysis

BACKGROUND: High-accuracy mass spectrometry enables near comprehensive quantification of the components of the cellular proteomes, increasingly including their chemically modified variants. Likewise, large-scale libraries of quantified synthetic peptides are becoming available, enabling absolute qua...

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Autores principales: Cifani, Paolo, Shakiba, Mojdeh, Chhangawala, Sagar, Kentsis, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336658/
https://www.ncbi.nlm.nih.gov/pubmed/28259147
http://dx.doi.org/10.1186/s12859-017-1563-6
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author Cifani, Paolo
Shakiba, Mojdeh
Chhangawala, Sagar
Kentsis, Alex
author_facet Cifani, Paolo
Shakiba, Mojdeh
Chhangawala, Sagar
Kentsis, Alex
author_sort Cifani, Paolo
collection PubMed
description BACKGROUND: High-accuracy mass spectrometry enables near comprehensive quantification of the components of the cellular proteomes, increasingly including their chemically modified variants. Likewise, large-scale libraries of quantified synthetic peptides are becoming available, enabling absolute quantification of chemically modified proteoforms, and therefore systems-level analyses of changes of their absolute abundance and stoichiometry. Existing computational methods provide advanced tools for mass spectral analysis and statistical inference, but lack integrated functions for quantitative analysis of post-translationally modified proteins and their modification stoichiometry. RESULTS: Here, we develop ProteoModlR, a program for quantitative analysis of abundance and stoichiometry of post-translational chemical modifications across temporal and steady-state biological states. While ProteoModlR is intended for the analysis of experiments using isotopically labeled reference peptides for absolute quantitation, it also supports the analysis of labeled and label-free data, acquired in both data-dependent and data-independent modes for relative quantitation. Moreover, ProteoModlR enables functional analysis of sparsely sampled quantitative mass spectrometry experiments by inferring the missing values from the available measurements, without imputation. The implemented architecture includes parsing and normalization functions to control for common sources of technical variation. Finally, ProteoModlR’s modular design and interchangeable format are optimally suited for integration with existing computational proteomics tools, thereby facilitating comprehensive quantitative analysis of cellular signaling. CONCLUSIONS: ProteoModlR and its documentation are available for download at http://github.com/kentsisresearchgroup/ProteoModlR as a stand-alone R package. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1563-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-53366582017-03-07 ProteoModlR for functional proteomic analysis Cifani, Paolo Shakiba, Mojdeh Chhangawala, Sagar Kentsis, Alex BMC Bioinformatics Software BACKGROUND: High-accuracy mass spectrometry enables near comprehensive quantification of the components of the cellular proteomes, increasingly including their chemically modified variants. Likewise, large-scale libraries of quantified synthetic peptides are becoming available, enabling absolute quantification of chemically modified proteoforms, and therefore systems-level analyses of changes of their absolute abundance and stoichiometry. Existing computational methods provide advanced tools for mass spectral analysis and statistical inference, but lack integrated functions for quantitative analysis of post-translationally modified proteins and their modification stoichiometry. RESULTS: Here, we develop ProteoModlR, a program for quantitative analysis of abundance and stoichiometry of post-translational chemical modifications across temporal and steady-state biological states. While ProteoModlR is intended for the analysis of experiments using isotopically labeled reference peptides for absolute quantitation, it also supports the analysis of labeled and label-free data, acquired in both data-dependent and data-independent modes for relative quantitation. Moreover, ProteoModlR enables functional analysis of sparsely sampled quantitative mass spectrometry experiments by inferring the missing values from the available measurements, without imputation. The implemented architecture includes parsing and normalization functions to control for common sources of technical variation. Finally, ProteoModlR’s modular design and interchangeable format are optimally suited for integration with existing computational proteomics tools, thereby facilitating comprehensive quantitative analysis of cellular signaling. CONCLUSIONS: ProteoModlR and its documentation are available for download at http://github.com/kentsisresearchgroup/ProteoModlR as a stand-alone R package. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1563-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-04 /pmc/articles/PMC5336658/ /pubmed/28259147 http://dx.doi.org/10.1186/s12859-017-1563-6 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Cifani, Paolo
Shakiba, Mojdeh
Chhangawala, Sagar
Kentsis, Alex
ProteoModlR for functional proteomic analysis
title ProteoModlR for functional proteomic analysis
title_full ProteoModlR for functional proteomic analysis
title_fullStr ProteoModlR for functional proteomic analysis
title_full_unstemmed ProteoModlR for functional proteomic analysis
title_short ProteoModlR for functional proteomic analysis
title_sort proteomodlr for functional proteomic analysis
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336658/
https://www.ncbi.nlm.nih.gov/pubmed/28259147
http://dx.doi.org/10.1186/s12859-017-1563-6
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