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Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2

BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in...

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Autores principales: Law, Jeffrey N, Akers, Kyle, Tasnina, Nure, Santina, Catherine M Della, Deutsch, Shay, Kshirsagar, Meghana, Klein-Seetharaman, Judith, Crovella, Mark, Rajagopalan, Padmavathy, Kasif, Simon, Murali, T M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716363/
https://www.ncbi.nlm.nih.gov/pubmed/34966926
http://dx.doi.org/10.1093/gigascience/giab082
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author Law, Jeffrey N
Akers, Kyle
Tasnina, Nure
Santina, Catherine M Della
Deutsch, Shay
Kshirsagar, Meghana
Klein-Seetharaman, Judith
Crovella, Mark
Rajagopalan, Padmavathy
Kasif, Simon
Murali, T M
author_facet Law, Jeffrey N
Akers, Kyle
Tasnina, Nure
Santina, Catherine M Della
Deutsch, Shay
Kshirsagar, Meghana
Klein-Seetharaman, Judith
Crovella, Mark
Rajagopalan, Padmavathy
Kasif, Simon
Murali, T M
author_sort Law, Jeffrey N
collection PubMed
description BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses.
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spelling pubmed-87163632022-01-05 Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2 Law, Jeffrey N Akers, Kyle Tasnina, Nure Santina, Catherine M Della Deutsch, Shay Kshirsagar, Meghana Klein-Seetharaman, Judith Crovella, Mark Rajagopalan, Padmavathy Kasif, Simon Murali, T M Gigascience Research BACKGROUND: Network propagation has been widely used for nearly 20 years to predict gene functions and phenotypes. Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e.g., determining how much any experimental observation in the input contributes to the score of every prediction. RESULTS: We design a network propagation framework with 2 novel components and apply it to predict human proteins that directly or indirectly interact with SARS-CoV-2 proteins. First, we trace the provenance of each prediction to its experimentally validated sources, which in our case are human proteins experimentally determined to interact with viral proteins. Second, we design a technique that helps to reduce the manual adjustment of parameters by users. We find that for every top-ranking prediction, the highest contribution to its score arises from a direct neighbor in a human protein-protein interaction network. We further analyze these results to develop functional insights on SARS-CoV-2 that expand on known biology such as the connection between endoplasmic reticulum stress, HSPA5, and anti-clotting agents. CONCLUSIONS: We examine how our provenance-tracing method can be generalized to a broad class of network-based algorithms. We provide a useful resource for the SARS-CoV-2 community that implicates many previously undocumented proteins with putative functional relationships to viral infection. This resource includes potential drugs that can be opportunistically repositioned to target these proteins. We also discuss how our overall framework can be extended to other, newly emerging viruses. Oxford University Press 2021-12-29 /pmc/articles/PMC8716363/ /pubmed/34966926 http://dx.doi.org/10.1093/gigascience/giab082 Text en © The Author(s) 2021. Published by Oxford University Press GigaScience. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (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 Research
Law, Jeffrey N
Akers, Kyle
Tasnina, Nure
Santina, Catherine M Della
Deutsch, Shay
Kshirsagar, Meghana
Klein-Seetharaman, Judith
Crovella, Mark
Rajagopalan, Padmavathy
Kasif, Simon
Murali, T M
Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2
title Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2
title_full Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2
title_fullStr Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2
title_full_unstemmed Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2
title_short Interpretable network propagation with application to expanding the repertoire of human proteins that interact with SARS-CoV-2
title_sort interpretable network propagation with application to expanding the repertoire of human proteins that interact with sars-cov-2
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716363/
https://www.ncbi.nlm.nih.gov/pubmed/34966926
http://dx.doi.org/10.1093/gigascience/giab082
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