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T1000: a reduced gene set prioritized for toxicogenomic studies
There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to ident...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824333/ https://www.ncbi.nlm.nih.gov/pubmed/31681519 http://dx.doi.org/10.7717/peerj.7975 |
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author | Soufan, Othman Ewald, Jessica Viau, Charles Crump, Doug Hecker, Markus Basu, Niladri Xia, Jianguo |
author_facet | Soufan, Othman Ewald, Jessica Viau, Charles Crump, Doug Hecker, Markus Basu, Niladri Xia, Jianguo |
author_sort | Soufan, Othman |
collection | PubMed |
description | There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1,000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210 genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets based on the rat model. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g., in vitro and in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available. |
format | Online Article Text |
id | pubmed-6824333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68243332019-11-01 T1000: a reduced gene set prioritized for toxicogenomic studies Soufan, Othman Ewald, Jessica Viau, Charles Crump, Doug Hecker, Markus Basu, Niladri Xia, Jianguo PeerJ Bioinformatics There is growing interest within regulatory agencies and toxicological research communities to develop, test, and apply new approaches, such as toxicogenomics, to more efficiently evaluate chemical hazards. Given the complexity of analyzing thousands of genes simultaneously, there is a need to identify reduced gene sets. Though several gene sets have been defined for toxicological applications, few of these were purposefully derived using toxicogenomics data. Here, we developed and applied a systematic approach to identify 1,000 genes (called Toxicogenomics-1000 or T1000) highly responsive to chemical exposures. First, a co-expression network of 11,210 genes was built by leveraging microarray data from the Open TG-GATEs program. This network was then re-weighted based on prior knowledge of their biological (KEGG, MSigDB) and toxicological (CTD) relevance. Finally, weighted correlation network analysis was applied to identify 258 gene clusters. T1000 was defined by selecting genes from each cluster that were most associated with outcome measures. For model evaluation, we compared the performance of T1000 to that of other gene sets (L1000, S1500, Genes selected by Limma, and random set) using two external datasets based on the rat model. Additionally, a smaller (T384) and a larger version (T1500) of T1000 were used for dose-response modeling to test the effect of gene set size. Our findings demonstrated that the T1000 gene set is predictive of apical outcomes across a range of conditions (e.g., in vitro and in vivo, dose-response, multiple species, tissues, and chemicals), and generally performs as well, or better than other gene sets available. PeerJ Inc. 2019-10-29 /pmc/articles/PMC6824333/ /pubmed/31681519 http://dx.doi.org/10.7717/peerj.7975 Text en ©2019 Soufan et al. 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 use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Soufan, Othman Ewald, Jessica Viau, Charles Crump, Doug Hecker, Markus Basu, Niladri Xia, Jianguo T1000: a reduced gene set prioritized for toxicogenomic studies |
title | T1000: a reduced gene set prioritized for toxicogenomic studies |
title_full | T1000: a reduced gene set prioritized for toxicogenomic studies |
title_fullStr | T1000: a reduced gene set prioritized for toxicogenomic studies |
title_full_unstemmed | T1000: a reduced gene set prioritized for toxicogenomic studies |
title_short | T1000: a reduced gene set prioritized for toxicogenomic studies |
title_sort | t1000: a reduced gene set prioritized for toxicogenomic studies |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6824333/ https://www.ncbi.nlm.nih.gov/pubmed/31681519 http://dx.doi.org/10.7717/peerj.7975 |
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