SIGNET: transcriptome-wide causal inference for gene regulatory networks.
Sci Rep
; 13(1): 19371, 2023 11 08.
Article
em En
| MEDLINE
| ID: mdl-37938594
ABSTRACT
Gene regulation plays an important role in understanding the mechanisms of human biology and diseases. However, inferring causal relationships between all genes is challenging due to the large number of genes in the transcriptome. Here, we present SIGNET (Statistical Inference on Gene Regulatory Networks), a flexible software package that reveals networks of causal regulation between genes built upon large-scale transcriptomic and genotypic data at the population level. Like Mendelian randomization, SIGNET uses genotypic variants as natural instrumental variables to establish such causal relationships but constructs a transcriptome-wide gene regulatory network with high confidence. SIGNET makes such a computationally heavy task feasible by deploying a well-designed statistical algorithm over a parallel computing environment. It also provides a user-friendly interface allowing for parameter tuning, efficient parallel computing scheduling, interactive network visualization, and confirmatory results retrieval. The Open source SIGNET software is freely available ( https//www.zstats.org/signet/ ).
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Reguladoras de Genes
/
Transcriptoma
Limite:
Humans
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Estados Unidos