EAT-UpTF

EAT-UpTF identifies enriched upstream transcription factors for a specified gene set by comparing genes-of-interest to empirically determined TF-target lists derived from DAP-seq and ChIP-seq genome-wide datasets.

Key Features:

• Python implementation: Implemented as a Python-based script for programmatic enrichment analysis.
• Single-step empirical approach: Performs enrichment directly using empirically determined TF-target gene lists rather than a two-step cis-motif prediction followed by TF binding inference.
• Data sources: Utilizes genome-wide TF-target datasets derived from DNA affinity purification sequencing (DAP-seq) and chromatin immunoprecipitation sequencing (ChIP-seq).
• Focus on experimentally validated interactions: Operates on experimentally determined TF-target interactions to enhance analysis reliability.
• Integration of high-throughput sequencing data: Incorporates TF-target lists obtained from high-throughput sequencing datasets into enrichment analyses.
• Enrichment analysis of GOIs: Identifies upstream TFs enriched within a specified group of genes-of-interest (GOIs).

Scientific Applications:

• Upstream TF identification: Identification of transcription factors that are statistically enriched as regulators of a given gene set.
• Gene regulatory network exploration: Linking TFs to target gene sets to support reconstruction and analysis of regulatory networks.
• Interpretation of DAP-seq and ChIP-seq data: Facilitating biological interpretation of genome-wide TF-binding datasets from DAP-seq and ChIP-seq experiments.
• Functional analysis of regulatory mechanisms: Assisting studies of transcriptional regulation underlying specific biological processes by providing candidate upstream TFs.

Methodology:

A Python-based script performs enrichment analysis by comparing user-specified GOIs to empirically determined TF-target gene lists derived from genome-wide DAP-seq and ChIP-seq datasets.