Rcade

Rcade integrates ChIP-seq peak data with differential expression summary data using a Bayesian framework to identify genes targeted by specific transcription factors (TFs).

Key Features:

• Bayesian framework: Uses a Bayesian statistical model to integrate ChIP-seq peaks with differential expression summaries for probabilistic inference of TF-target interactions.
• Transcription factor target identification: Associates genes with ChIP-seq peaks and evaluates differential expression under TF-related perturbations to nominate TF targets.
• Bioconductor interoperability: Implemented within the Bioconductor ecosystem in R to interoperate with Bioconductor data structures and packages.

Scientific Applications:

• Gene regulation studies: Identification of TF-target relationships to elucidate regulatory networks governing gene expression.
• Functional genomics: Linking ChIP-seq binding events to expression changes to interpret effects of perturbations and infer functional consequences.
• Transcription factor analysis: Characterizing roles of specific TFs by combining binding (ChIP-seq) and expression (differential expression) evidence.

Methodology:

Associates genes with ChIP-seq peaks, integrates these associations with differential expression summary statistics, and applies a Bayesian framework to compute probabilistic assessments of TF-target interactions.