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. 2017 Mar 4;18:153. doi: 10.1186/s12859-017-1563-6

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© 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.

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Fig. 3 — ProteoModlR computes exact and approximate abundance and PTM stoichiometry based on the available set of peptides for a given protein. Exact and approximate calculations for abundance and stoichiometry were tested on simulated datasets modeling a hypothetical protein producing four peptides, two of which bear a PTM. If the input file contains all ‘Q’, ‘M’ and ‘NM’ peptides, then ProteoModlR computes ‘exact’ relative peptide abundance, expressed as fold change compared to Sample #1 (a) and stoichiometry (c) If ‘Quantification’ (‘Q’) or non-modified (‘NM’) peptides are not available, ProteoModlR can calculate ‘approximate’ relative abundances (b) and 'approximate stoichiometry (d) resembling the exact values (Pearson product–moment correlation coefficient = 0.98)