AUTHOR=Hartman Erik , Mahdavi Simon , Kjellström Sven , Schmidtchen Artur TITLE=Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data JOURNAL=Frontiers in Bioinformatics VOLUME=1 YEAR=2021 URL=https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2021.722466 DOI=10.3389/fbinf.2021.722466 ISSN=2673-7647 ABSTRACT=

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.