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

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Franck Boizard, Bénédicte Buffin-Meyer, Julien Aligon, Olivier Teste, Joost P. Schanstra, Julie Klein
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/769a28af533e4a639ca9f877c782b024
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:769a28af533e4a639ca9f877c782b024
record_format	dspace
spelling	oai:doaj.org-article:769a28af533e4a639ca9f877c782b0242021-12-02T13:30:11ZPRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms10.1038/s41598-021-85135-32045-2322https://doaj.org/article/769a28af533e4a639ca9f877c782b0242021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85135-3https://doaj.org/toc/2045-2322Abstract 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 ).Franck BoizardBénédicte Buffin-MeyerJulien AligonOlivier TesteJoost P. SchanstraJulie KleinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Franck Boizard Bénédicte Buffin-Meyer Julien Aligon Olivier Teste Joost P. Schanstra Julie Klein PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms
description	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 ).
format	article
author	Franck Boizard Bénédicte Buffin-Meyer Julien Aligon Olivier Teste Joost P. Schanstra Julie Klein
author_facet	Franck Boizard Bénédicte Buffin-Meyer Julien Aligon Olivier Teste Joost P. Schanstra Julie Klein
author_sort	Franck Boizard
title	PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms
title_short	PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms
title_full	PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms
title_fullStr	PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms
title_full_unstemmed	PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms
title_sort	prynt: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/769a28af533e4a639ca9f877c782b024
work_keys_str_mv	AT franckboizard pryntatoolforprioritizationofdiseasecandidatesfromproteomicsdatausingacombinationofshortestpathandrandomwalkalgorithms AT benedictebuffinmeyer pryntatoolforprioritizationofdiseasecandidatesfromproteomicsdatausingacombinationofshortestpathandrandomwalkalgorithms AT julienaligon pryntatoolforprioritizationofdiseasecandidatesfromproteomicsdatausingacombinationofshortestpathandrandomwalkalgorithms AT olivierteste pryntatoolforprioritizationofdiseasecandidatesfromproteomicsdatausingacombinationofshortestpathandrandomwalkalgorithms AT joostpschanstra pryntatoolforprioritizationofdiseasecandidatesfromproteomicsdatausingacombinationofshortestpathandrandomwalkalgorithms AT julieklein pryntatoolforprioritizationofdiseasecandidatesfromproteomicsdatausingacombinationofshortestpathandrandomwalkalgorithms
_version_	1718392982379429888

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

Ejemplares similares