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PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms

Abstract : Abstract The urinary proteome is a promising pool of biomarkers of kidney disease. However, the protein changes observed in urine only partially reflect the deregulated mechanisms within kidney tissue. In order to improve on the mechanistic insight based on the urinary protein changes, we developed a new prioritization strategy called PRYNT (PRioritization bY protein NeTwork) that employs a combination of two closeness-based algorithms, shortest-path and random walk, and a contextualized protein–protein interaction (PPI) network, mainly based on clique consolidation of STRING network. To assess the performance of our approach, we evaluated both precision and specificity of PRYNT in prioritizing kidney disease candidates. Using four urinary proteome datasets, PRYNT prioritization performed better than other prioritization methods and tools available in the literature. Moreover, PRYNT performed to a similar, but complementary, extent compared to the upstream regulator analysis from the commercial Ingenuity Pathway Analysis software. In conclusion, PRYNT appears to be a valuable freely accessible tool to predict key proteins indirectly from urinary proteome data. In the future, PRYNT approach could be applied to other biofluids, molecular traits and diseases. The source code is freely available on GitHub at: https://github.com/Boizard/PRYNT and has been integrated as an interactive web apps to improved accessibility ( https://github.com/Boizard/PRYNT/tree/master/AppPRYNT ).
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https://hal.archives-ouvertes.fr/hal-03268300
Contributor : Joost Schanstra Connect in order to contact the contributor
Submitted on : Wednesday, June 23, 2021 - 9:23:04 AM
Last modification on : Tuesday, June 29, 2021 - 11:37:33 AM

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Franck Boizard, Bénédicte Buffin-Meyer, Julien Aligon, Olivier Teste, Joost Schanstra, et al.. PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms. Scientific Reports, Nature Publishing Group, 2021, 11 (1), pp.Article number: 5764 (2021). ⟨10.1038/s41598-021-85135-3⟩. ⟨hal-03268300⟩

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