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ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery
SUMMARY: Estimating efficacy of gene–target-disease associations is a fundamental step in drug discovery. An important data source for this laborious task is RNA expression, which can provide gene–disease associations on the basis of expression fold change and statistical significance. However, the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390989/ https://www.ncbi.nlm.nih.gov/pubmed/32437556 http://dx.doi.org/10.1093/bioinformatics/btaa518 |
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author | Failli, Mario Paananen, Jussi Fortino, Vittorio |
author_facet | Failli, Mario Paananen, Jussi Fortino, Vittorio |
author_sort | Failli, Mario |
collection | PubMed |
description | SUMMARY: Estimating efficacy of gene–target-disease associations is a fundamental step in drug discovery. An important data source for this laborious task is RNA expression, which can provide gene–disease associations on the basis of expression fold change and statistical significance. However, the simply use of the log-fold change can lead to numerous false-positive associations. On the other hand, more sophisticated methods that utilize gene co-expression networks do not consider tissue specificity. Here, we introduce Transcriptome-driven Efficacy estimates for gene-based TArget discovery (ThETA), an R package that enables non-expert users to use novel efficacy scoring methods for drug–target discovery. In particular, ThETA allows users to search for gene perturbation (therapeutics) that reverse disease-gene expression and genes that are closely related to disease-genes in tissue-specific networks. ThETA also provides functions to integrate efficacy evaluations obtained with different approaches and to build an overall efficacy score, which can be used to identify and prioritize gene(target)–disease associations. Finally, ThETA implements visualizations to show tissue-specific interconnections between target and disease-genes, and to indicate biological annotations associated with the top selected genes. AVAILABILITY AND IMPLEMENTATION: ThETA is freely available for academic use at https://github.com/vittoriofortino84/ThETA. CONTACT: vittorio.fortino@uef.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-7390989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-73909892020-08-04 ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery Failli, Mario Paananen, Jussi Fortino, Vittorio Bioinformatics Applications Notes SUMMARY: Estimating efficacy of gene–target-disease associations is a fundamental step in drug discovery. An important data source for this laborious task is RNA expression, which can provide gene–disease associations on the basis of expression fold change and statistical significance. However, the simply use of the log-fold change can lead to numerous false-positive associations. On the other hand, more sophisticated methods that utilize gene co-expression networks do not consider tissue specificity. Here, we introduce Transcriptome-driven Efficacy estimates for gene-based TArget discovery (ThETA), an R package that enables non-expert users to use novel efficacy scoring methods for drug–target discovery. In particular, ThETA allows users to search for gene perturbation (therapeutics) that reverse disease-gene expression and genes that are closely related to disease-genes in tissue-specific networks. ThETA also provides functions to integrate efficacy evaluations obtained with different approaches and to build an overall efficacy score, which can be used to identify and prioritize gene(target)–disease associations. Finally, ThETA implements visualizations to show tissue-specific interconnections between target and disease-genes, and to indicate biological annotations associated with the top selected genes. AVAILABILITY AND IMPLEMENTATION: ThETA is freely available for academic use at https://github.com/vittoriofortino84/ThETA. CONTACT: vittorio.fortino@uef.fi SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2020-05-21 /pmc/articles/PMC7390989/ /pubmed/32437556 http://dx.doi.org/10.1093/bioinformatics/btaa518 Text en © The Author(s) 2020. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Applications Notes Failli, Mario Paananen, Jussi Fortino, Vittorio ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery |
title | ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery |
title_full | ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery |
title_fullStr | ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery |
title_full_unstemmed | ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery |
title_short | ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery |
title_sort | theta: transcriptome-driven efficacy estimates for gene-based target discovery |
topic | Applications Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7390989/ https://www.ncbi.nlm.nih.gov/pubmed/32437556 http://dx.doi.org/10.1093/bioinformatics/btaa518 |
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