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Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase

ABSTRACT: Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based...

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Autores principales: Wright, Adam J, Orlic-Milacic, Marija, Rothfels, Karen, Weiser, Joel, Trinh, Quang M, Jassal, Bijay, Haw, Robin A, Stein, Lincoln D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216552/
https://www.ncbi.nlm.nih.gov/pubmed/35348650
http://dx.doi.org/10.1093/database/baac009
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author Wright, Adam J
Orlic-Milacic, Marija
Rothfels, Karen
Weiser, Joel
Trinh, Quang M
Jassal, Bijay
Haw, Robin A
Stein, Lincoln D
author_facet Wright, Adam J
Orlic-Milacic, Marija
Rothfels, Karen
Weiser, Joel
Trinh, Quang M
Jassal, Bijay
Haw, Robin A
Stein, Lincoln D
author_sort Wright, Adam J
collection PubMed
description ABSTRACT: Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators’ predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for ‘Mitotic G1 phase and G1/S transition’ to 100% (curator)/94% (MP-BioPath) for ‘RAF/MAP kinase cascade’. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested. DATABASE URL: www.reactome.org
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spelling pubmed-92165522022-06-23 Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase Wright, Adam J Orlic-Milacic, Marija Rothfels, Karen Weiser, Joel Trinh, Quang M Jassal, Bijay Haw, Robin A Stein, Lincoln D Database (Oxford) Original Article ABSTRACT: Reactome is a database of human biological pathways manually curated from the primary literature and peer-reviewed by experts. To evaluate the utility of Reactome pathways for predicting functional consequences of genetic perturbations, we compared predictions of perturbation effects based on Reactome pathways against published empirical observations. Ten cancer-relevant Reactome pathways, representing diverse biological processes such as signal transduction, cell division, DNA repair and transcriptional regulation, were selected for testing. For each pathway, root input nodes and key pathway outputs were defined. We then used pathway-diagram-derived logic graphs to predict, either by inspection by biocurators or using a novel algorithm MP-BioPath, the effects of bidirectional perturbations (upregulation/activation or downregulation/inhibition) of single root inputs on the status of key outputs. These predictions were then compared to published empirical tests. In total, 4968 test cases were analyzed across 10 pathways, of which 847 were supported by published empirical findings. Out of the 847 test cases, curators’ predictions agreed with the experimental evidence in 670 and disagreed in 177 cases, resulting in ∼81% overall accuracy. MP-BioPath predictions agreed with experimental evidence for 625 and disagreed for 222 test cases, resulting in ∼75% overall accuracy. The expected accuracy of random guessing was 33%. Per-pathway accuracy did not correlate with the number of pathway edges nor the number of pathway nodes but varied across pathways, ranging from 56% (curator)/44% (MP-BioPath) for ‘Mitotic G1 phase and G1/S transition’ to 100% (curator)/94% (MP-BioPath) for ‘RAF/MAP kinase cascade’. This study highlights the potential of pathway databases such as Reactome in modeling genetic perturbations, promoting standardization of experimental pathway activity readout and supporting hypothesis-driven research by revealing relationships between pathway inputs and outputs that have not yet been directly experimentally tested. DATABASE URL: www.reactome.org Oxford University Press 2022-03-06 /pmc/articles/PMC9216552/ /pubmed/35348650 http://dx.doi.org/10.1093/database/baac009 Text en © The Author(s) 2022. Published by Oxford University Press. 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 Original Article
Wright, Adam J
Orlic-Milacic, Marija
Rothfels, Karen
Weiser, Joel
Trinh, Quang M
Jassal, Bijay
Haw, Robin A
Stein, Lincoln D
Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase
title Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase
title_full Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase
title_fullStr Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase
title_full_unstemmed Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase
title_short Evaluating the predictive accuracy of curated biological pathways in a public knowledgebase
title_sort evaluating the predictive accuracy of curated biological pathways in a public knowledgebase
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216552/
https://www.ncbi.nlm.nih.gov/pubmed/35348650
http://dx.doi.org/10.1093/database/baac009
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