ThETA: transcriptome-driven efficacy estimates for gene-based TArget discovery.
Bioinformatics
; 36(14): 4214-4216, 2020 08 15.
Article
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| MEDLINE
| ID: mdl-32437556
ABSTRACT
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.
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MEDLINE
Asunto principal:
Programas Informáticos
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Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2020
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Article
País de afiliación:
Finlandia