Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators.
Nucleic Acids Res
; 47(22): 11563-11573, 2019 12 16.
Article
em En
| MEDLINE
| ID: mdl-31701125
ABSTRACT
Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator-gene interactions. Several commercial platforms provide large and manually curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments. Our GGM approach identifies high confidence transcription factor (TF)-gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually curated databases of TF-gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Regulação da Expressão Gênica
/
Biologia Computacional
/
Perfilação da Expressão Gênica
/
Redes Reguladoras de Genes
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Nucleic Acids Res
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Estados Unidos