ProteoModlR for functional proteomic analysis Journal Article


Authors: Cifani, P.; Shakiba, M.; Chhangawala, S.; Kentsis, A.
Article Title: ProteoModlR for functional proteomic analysis
Abstract: 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/ProteoModlRas a stand-alone R package. © 2017 The Author(s).
Keywords: chemical analysis; mass spectrometry; proteins; peptides; molecular biology; stoichiometry; chemical modification; spectrum analysis; quantitative proteomics; spectrometry; functional analysis; absolute quantification; post-translational modifications; r; post-translational modification stoichiometry; computational proteomics; implemented architectures; mass-spectral analysis; quantitative mass spectrometries; relative quantitations
Journal Title: BMC Bioinformatics
Volume: 18
Issue: 1
ISSN: 1471-2105
Publisher: Biomed Central Ltd  
Date Published: 2017-01-01
Start Page: 153
Language: English
DOI: 10.1186/s12859-017-1563-6
PROVIDER: scopus
PMCID: PMC5336658
PUBMED: 28259147
DOI/URL:
Notes: Article -- Export Date: 3 April 2017 -- Source: Scopus
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MSK Authors
  1. Alex   Kentsis
    50 Kentsis
  2. Paolo   Cifani
    17 Cifani