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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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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. |
format | Online Article Text |
id | pubmed-8265130 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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