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KEA3: improved kinase enrichment analysis via data integration

Phosphoproteomics and proteomics experiments capture a global snapshot of the cellular signaling network, but these methods do not directly measure kinase state. Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrate...

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Autores principales: Kuleshov, Maxim V, Xie, Zhuorui, London, Alexandra B K, Yang, Janice, Evangelista, John Erol, Lachmann, Alexander, Shu, Ingrid, Torre, Denis, Ma’ayan, Avi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265130/
https://www.ncbi.nlm.nih.gov/pubmed/34019655
http://dx.doi.org/10.1093/nar/gkab359
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author Kuleshov, Maxim V
Xie, Zhuorui
London, Alexandra B K
Yang, Janice
Evangelista, John Erol
Lachmann, Alexander
Shu, Ingrid
Torre, Denis
Ma’ayan, Avi
author_facet Kuleshov, Maxim V
Xie, Zhuorui
London, Alexandra B K
Yang, Janice
Evangelista, John Erol
Lachmann, Alexander
Shu, Ingrid
Torre, Denis
Ma’ayan, Avi
author_sort Kuleshov, Maxim V
collection PubMed
description Phosphoproteomics and proteomics experiments capture a global snapshot of the cellular signaling network, but these methods do not directly measure kinase state. Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrates are in a user-inputted list of proteins. KEA3 can be applied to analyze data from phosphoproteomics and proteomics studies to predict the upstream kinases responsible for observed differential phosphorylations. The KEA3 background database contains measured and predicted kinase-substrate interactions (KSI), kinase-protein interactions (KPI), and interactions supported by co-expression and co-occurrence data. To benchmark the performance of KEA3, we examined whether KEA3 can predict the perturbed kinase from single-kinase perturbation followed by gene expression experiments, and phosphoproteomics data collected from kinase-targeting small molecules. We show that integrating KSIs and KPIs across data sources to produce a composite ranking improves the recovery of the expected kinase. The KEA3 webserver is available at https://maayanlab.cloud/kea3.
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spelling pubmed-82651302021-07-09 KEA3: improved kinase enrichment analysis via data integration Kuleshov, Maxim V Xie, Zhuorui London, Alexandra B K Yang, Janice Evangelista, John Erol Lachmann, Alexander Shu, Ingrid Torre, Denis Ma’ayan, Avi Nucleic Acids Res Web Server Issue Phosphoproteomics and proteomics experiments capture a global snapshot of the cellular signaling network, but these methods do not directly measure kinase state. Kinase Enrichment Analysis 3 (KEA3) is a webserver application that infers overrepresentation of upstream kinases whose putative substrates are in a user-inputted list of proteins. KEA3 can be applied to analyze data from phosphoproteomics and proteomics studies to predict the upstream kinases responsible for observed differential phosphorylations. The KEA3 background database contains measured and predicted kinase-substrate interactions (KSI), kinase-protein interactions (KPI), and interactions supported by co-expression and co-occurrence data. To benchmark the performance of KEA3, we examined whether KEA3 can predict the perturbed kinase from single-kinase perturbation followed by gene expression experiments, and phosphoproteomics data collected from kinase-targeting small molecules. We show that integrating KSIs and KPIs across data sources to produce a composite ranking improves the recovery of the expected kinase. The KEA3 webserver is available at https://maayanlab.cloud/kea3. Oxford University Press 2021-05-21 /pmc/articles/PMC8265130/ /pubmed/34019655 http://dx.doi.org/10.1093/nar/gkab359 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Web Server Issue
Kuleshov, Maxim V
Xie, Zhuorui
London, Alexandra B K
Yang, Janice
Evangelista, John Erol
Lachmann, Alexander
Shu, Ingrid
Torre, Denis
Ma’ayan, Avi
KEA3: improved kinase enrichment analysis via data integration
title KEA3: improved kinase enrichment analysis via data integration
title_full KEA3: improved kinase enrichment analysis via data integration
title_fullStr KEA3: improved kinase enrichment analysis via data integration
title_full_unstemmed KEA3: improved kinase enrichment analysis via data integration
title_short KEA3: improved kinase enrichment analysis via data integration
title_sort kea3: improved kinase enrichment analysis via data integration
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8265130/
https://www.ncbi.nlm.nih.gov/pubmed/34019655
http://dx.doi.org/10.1093/nar/gkab359
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