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 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

IMass time: The future, in future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass

Additional file 5: Figure S5. of ProteoModlR for functional proteomic analysis

Reference peptide normalization corrects for variations in the total protein content per cell across measurements, as demonstrated on simulated data. (A) Quantitation across three replicate measurements of five peptides from a protein of interest (shades of red) and four peptides from reference proteins (shades of blue). (B) ProteoModlR corrects errors introduced by biological factors that vary the total amount of protein per cell, equalizing the intensities of one or more peptides chosen as internal reference. (TIF 4660 kb

Additional file 2: Figure S2. of ProteoModlR for functional proteomic analysis

Equimolar Isotopologue normalization corrects for technical variability across measurements, as demonstrated on simulated data. A) Quantitation across three replicate measurements of five peptides from a protein of interest (shades of red) and four peptides from reference proteins (shades of blue). (B) ProteoModlR corrects errors introduced by technical and biological variability. (C) Quantitation of heavy labeled equimolar standard peptides is affected by differential ionization efficiency and technical variability. (D) ProteoModlR equalizes the intensities of the standard isotopologues for each peptide independently. (TIF 9310 kb

Additional file 4: Figure S4. of ProteoModlR for functional proteomic analysis

Total ion current normalization corrects for technical variability across measurements in absence of isotopically encoded standards, as demonstrated on simulated data. (A) Quantitation across three replicate measurements of five peptides from a protein of interest (shades of red) and four peptides from reference proteins (shades of blue). (B) ProteoModlR corrects errors introduced by technical and biological variability. (C) Total ion current is also affected by technical variability. (D) If total ion current normalization is chosen, ProteoModlR equalizes the sum of the intensities of all peptides in each sample. (TIF 9017 kb

Additional file 1: Figure S1. of ProteoModlR for functional proteomic analysis

Conceptual overview of the operations performed by ProteoModlR. (A) A set of proteoforms is digested into peptides and (B) mixed with an equimolar set of synthetic reference peptides (in blue). (C) MS signal-response is affected by differential ionization efficiency. Furthermore, MS quantification may present missing values. (D) ProteoModlR first annotates the available set of peptides, then (E) corrects errors introduced by technical and biological variability. Finally, (F) exact or approximate calculations are deployed to obtain PTM stoichiometry and abundance. (TIF 919 kb

Additional file 19: Table S1. of ProteoModlR for functional proteomic analysis

Chemoforms available for abundance and stoichiometry calculations from experimentally derived data. The table contains the peptides available in the experimentally derived dataset (Fig. 4, [19]) for protein LCK, ZAP70, NFIL3 and STAT5A. For each chemoform, modification status and Quality Control annotation is reported. (TIF 4000 kb

Additional file 6: Software documentation. of ProteoModlR for functional proteomic analysis

The document contains detailed description of ProteoModlR implementation as well as user instructions. (PDF 196 kb

Additional file 3: Figure S3. of ProteoModlR for functional proteomic analysis

Isotopologue normalization corrects for technical variability across measurements, as demonstrated on simulated data. (A) Quantitation across three replicate measurements of five peptides from a protein of interest (shades of red) and four peptides from reference proteins (shades of blue). (B) ProteoModlR corrects errors introduced by technical and biological variability. (C) Quantitation of heavy labeled standard peptides is also affected by technical variability. (D) If isotopologue normalization is chosen, ProteoModlR equalizes the intensities of the standard isotopologues for each peptide independently. (TIF 9821 kb

Additional file 7: Figure S6. of ProteoModlR for functional proteomic analysis

Output of exact (A) and approximate (B-D) calculations from simulated datasets. The input contained quantitation across three replicate measurements of four peptides, two of which phosphorylated. (TIF 25386 kb