SEPIRA

SEPIRA infers sample-specific transcription factor (TF) activity from genome-wide mRNA expression and DNA methylation (DNAm) profiles using large RNA-sequencing expression compendia to map regulatory activity for epigenomic studies and lung carcinogenesis research.

Key Features:

• Inference from expression and methylation data: Infers TF-binding activity landscapes from mRNA expression profiles and DNAm data.
• Systems-epigenomics approach: Integrates large-scale RNA-sequencing expression compendia to derive sample-specific regulatory activity signatures.
• Application to lung carcinogenesis: Applied to study transcriptional changes during progression from normal tissue and precursor lesions to lung cancer, highlighting TFs such as AHR and FOXJ1.
• Identification of regulatory factors: Identifies TF subsets including AHR, its repressor AHRR, and FOXJ1 linked to smoking-related molecular alterations and lung cancer etiology.
• Utility for EWAS: Enables inference of regulatory activities from DNAm alone, supporting epigenome-wide association studies (EWAS).

Scientific Applications:

• Lung Cancer Etiology: Maps activity and inactivation patterns of lung-specific TFs to investigate molecular mechanisms of lung cancer development.
• Epigenetic Regulation Studies: Infers regulatory activity from epigenomic (DNAm) and transcriptomic data to study altered gene regulation in disease.
• Transcription Factor Dynamics: Profiles dynamics of TFs such as AHR, AHRR, and FOXJ1 during carcinogenesis to inform mechanistic hypotheses.

Methodology:

Implements a systems-epigenomics algorithm that uses large RNA-sequencing expression compendia to infer regulatory activities from mRNA expression profiles and DNAm data, with analyses focused on lung-specific transcription factors.