ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
15 08 2020
15 08 2020
Historique:
received:
29
11
2019
revised:
23
04
2020
accepted:
16
05
2020
pubmed:
22
5
2020
medline:
2
2
2021
entrez:
22
5
2020
Statut:
ppublish
Résumé
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. ThETA is freely available for academic use at https://github.com/vittoriofortino84/ThETA. vittorio.fortino@uef.fi. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 32437556
pii: 5841656
doi: 10.1093/bioinformatics/btaa518
pmc: PMC7390989
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4214-4216Informations de copyright
© The Author(s) 2020. Published by Oxford University Press.
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