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Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data

Finding new sustainable means of diagnosing and treating diseases is one of the most pressing issues of our time. In recent years, several endogenous peptides have been found to be both excellent biomarkers for many diseases and to possess important physiological roles which may be utilized in treat...

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Autores principales: Hartman, Erik, Mahdavi, Simon, Kjellström, Sven, Schmidtchen, Artur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581017/
https://www.ncbi.nlm.nih.gov/pubmed/36303760
http://dx.doi.org/10.3389/fbinf.2021.722466
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author Hartman, Erik
Mahdavi, Simon
Kjellström, Sven
Schmidtchen, Artur
author_facet Hartman, Erik
Mahdavi, Simon
Kjellström, Sven
Schmidtchen, Artur
author_sort Hartman, Erik
collection PubMed
description Finding new sustainable means of diagnosing and treating diseases is one of the most pressing issues of our time. In recent years, several endogenous peptides have been found to be both excellent biomarkers for many diseases and to possess important physiological roles which may be utilized in treatments. The detection of peptides has been facilitated by the rapid development of biological mass spectrometry and now the combination of fast and sensitive high resolution MS instruments and stable nano HP-LC equipment sequences thousands of peptides in one single experiment. In most research conducted with these advanced systems, proteolytically cleaved proteins are analyzed and the specific peptides are identified by software dedicated for protein quantification using different proteomics workflows. Analysis of endogenous peptides with peptidomics workflows also benefit from the novel sensitive and advanced instrumentation, however, the generated peptidomic data is vast and subsequently laborious to visualize and examine, creating a bottleneck in the analysis. Therefore, we have created Peptimetric, an application designed to allow researchers to investigate and discover differences between peptidomic samples. Peptimetric allows the user to dynamically and interactively investigate the proteins, peptides, and some general characteristics of multiple samples, and is available as a web application at https://peptimetric.herokuapp.com. To illustrate the utility of Peptimetric, we’ve applied it to a peptidomic dataset of 15 urine samples from diabetic patients and corresponding data from healthy subjects.
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spelling pubmed-95810172022-10-26 Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data Hartman, Erik Mahdavi, Simon Kjellström, Sven Schmidtchen, Artur Front Bioinform Bioinformatics Finding new sustainable means of diagnosing and treating diseases is one of the most pressing issues of our time. In recent years, several endogenous peptides have been found to be both excellent biomarkers for many diseases and to possess important physiological roles which may be utilized in treatments. The detection of peptides has been facilitated by the rapid development of biological mass spectrometry and now the combination of fast and sensitive high resolution MS instruments and stable nano HP-LC equipment sequences thousands of peptides in one single experiment. In most research conducted with these advanced systems, proteolytically cleaved proteins are analyzed and the specific peptides are identified by software dedicated for protein quantification using different proteomics workflows. Analysis of endogenous peptides with peptidomics workflows also benefit from the novel sensitive and advanced instrumentation, however, the generated peptidomic data is vast and subsequently laborious to visualize and examine, creating a bottleneck in the analysis. Therefore, we have created Peptimetric, an application designed to allow researchers to investigate and discover differences between peptidomic samples. Peptimetric allows the user to dynamically and interactively investigate the proteins, peptides, and some general characteristics of multiple samples, and is available as a web application at https://peptimetric.herokuapp.com. To illustrate the utility of Peptimetric, we’ve applied it to a peptidomic dataset of 15 urine samples from diabetic patients and corresponding data from healthy subjects. Frontiers Media S.A. 2021-08-25 /pmc/articles/PMC9581017/ /pubmed/36303760 http://dx.doi.org/10.3389/fbinf.2021.722466 Text en Copyright © 2021 Hartman, Mahdavi, Kjellström and Schmidtchen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioinformatics
Hartman, Erik
Mahdavi, Simon
Kjellström, Sven
Schmidtchen, Artur
Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data
title Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data
title_full Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data
title_fullStr Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data
title_full_unstemmed Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data
title_short Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data
title_sort peptimetric: quantifying and visualizing differences in peptidomic data
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581017/
https://www.ncbi.nlm.nih.gov/pubmed/36303760
http://dx.doi.org/10.3389/fbinf.2021.722466
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