CENTDIST

CENTDIST identifies co-transcription factors by analyzing the spatial distribution of transcription factor (TF) motifs around ChIP-seq-derived TF binding sites to elucidate combinatorial transcriptional regulation.

Key Features:

• ChIP-seq motif distribution analysis: Analyzes distribution patterns of TF motifs around transcription factor binding sites derived from ChIP-seq data to detect co-TFs.
• Parameter-free operation: Operates without predefined background models or user-specified parameters.
• Automatic parameter optimization: Automatically determines enrichment window size and position weight matrix (PWM) score cut-offs for analysis.
• Ranking system: Ranks candidate co-TF motifs by their proximity and frequency around ChIP-seq peaks.
• Imbalanced-binding exploitation: Exploits the imbalanced nature of co-TF binding around transcription factor peaks to enhance detection sensitivity.

Scientific Applications:

• Transcriptional regulation studies: Identification of co-TFs to investigate combinatorial mechanisms controlling gene expression.
• ChIP-seq dataset analysis: Applied and validated across multiple ChIP-seq datasets to detect co-regulatory motifs.
• Androgen Receptor (AR) analysis: In AR ChIP-seq from a prostate cancer cell line, identified known AR co-TFs within the top 20 ranked hits and revealed AP4 as a novel AR co-TF.
• Candidate prioritization: Provides ranked co-TF candidates for downstream experimental validation.

Methodology:

Computationally analyzes TF motif distributions around ChIP-seq peaks, automatically optimizes enrichment window size and PWM score cut-offs, exploits imbalanced co-TF binding patterns, and does not use predefined background models or user-specified parameters.