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pmid:29900295

Peptidomics dataset: Blood plasma and serum samples of healthy donors fractionated on a set of chromatography sorbents.

Georgij Arapidi · Maria Osetrova · Olga Ivanova · Ivan Butenko · Tatjana Saveleva · Polina Pavlovich · Nikolay Anikanov · Vadim Ivanov · Vadim Govorun

Data in brief · 2018 doi:10.1016/j.dib.2018.04.018

Paper

complete

Experiment

partial

Data

partial

Analysis

partial

Execution

absent

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title
Peptidomics dataset: Blood plasma and serum samples of healthy donors fractionated on a set of chromatography sorbents.
doi
10.1016/j.dib.2018.04.018
pmid
29900295
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Georgij Arapidi
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Maria Osetrova
[2]
Olga Ivanova
[3]
Ivan Butenko
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Tatjana Saveleva
[5]
Polina Pavlovich
[6]
Nikolay Anikanov
[7]
Vadim Ivanov
[8]
Vadim Govorun
journal
Data in brief
year
2018
abstract
Blood as connective tissue potentially contains evidence of all processes occurring within the organism, at least in trace amounts (Petricoin et al., 2006) [1]. Because of their small size, peptides penetrate cell membranes and epithelial barriers more freely than proteins. Among the peptides found in blood, there are both fragments of proteins secreted by various tissues and performing their function in plasma and receptor ligands: hormones, cytokines and mediators of cellular response (Anderson et al., 2002) [2]. In addition, in minor amounts, there are peptide disease markers (for example, oncomarkers) and even foreign peptides related to pathogenic organisms and infection agents. To propose an approach for detailed peptidome characterization, we carried out an LC-MS/MS analysis of blood serum and plasma samples taken from 20 healthy donors on a TripleTOF 5600+ mass-spectrometer. We prepared samples based on our previously developed method of peptide desorption from the surface of abundant blood plasma proteins followed by standard chromatographic steps (Ziganshin et al., 2011) [3]. The mass-spectrometry peptidomics data presented in this article have been deposited to the ProteomeXchange Consortium (Deutsch et al., 2017) [4] via the PRIDE partner repository with the dataset identifier PXD008141 and 10.6019/PXD008141.
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We used the local false discovery rate scoring algorithm with standard experiment-wide protein grouping.
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Complete file
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