TEffectR

TEffectR analyzes the regulatory effects of transposable elements on proximal gene expression by applying linear regression models to RNA-sequencing data.

Key Features:

• Linear Regression Model: Employs a robust linear regression framework to assess the influence of different TE species on nearby gene transcription levels.
• Integration with Annotations: Leverages RepeatMasker transposable element annotations and Ensembl gene annotations for comprehensive TE-to-gene mapping.
• R Ecosystem Integration: Incorporates functions from other established R packages to support analysis and modeling.
• BAM File Processing: Processes sorted and indexed genome-aligned BAM files to calculate total read counts for transposable elements.
• Statistical Analysis: Identifies statistically significant relationships between TE expression and transcription of adjacent genes across biological conditions.

Scientific Applications:

• Oncology: Facilitates analysis of RNA-sequencing from normal and tumor specimens to elucidate how TEs contribute to gene expression changes associated with cancer.
• Developmental Biology: Enables investigation of TE-associated gene regulation across developmental contexts using RNA-sequencing data.

Methodology:

Calculates TE read counts from sorted and indexed genome-aligned BAM files using RepeatMasker and Ensembl annotations, then applies linear regression models in R to test associations between TE expression and proximal gene transcription.