TFARM

TFARM infers interactions among transcription factors within defined genomic regions from ChIP-seq data using association rule mining and a quantitative Importance Index.

Key Features:

• Association Rule Computation: Uses association rule mining to identify significant co-occurrence patterns of transcription factors within user-defined genomic regions from ChIP-seq datasets.
• Importance Index: Implements a novel Importance Index that quantifies the relevance and significance of identified transcription factor interactions.
• Pre-processing Pipeline: Includes a pre-processing pipeline for extraction and preparation of input data derived from ChIP-seq experiments.
• Validation with ENCODE Data: Demonstrated performance on ENCODE ChIP-seq datasets to detect known transcription factor interactions.

Scientific Applications:

• Combinatorial TF and chromatin-associated protein analysis: Identifies functional interaction networks among transcription factors and chromatin-associated proteins to map regulatory frameworks in genomic regions.
• ChIP-seq data interpretation: Reveals co-binding patterns and combinatorial regulatory relationships from ChIP-seq datasets.
• Study of cellular processes: Supports investigation of gene regulation underlying differentiation, proliferation, and adaptation to external stimuli by characterizing TF interaction patterns.
• Benchmarking against reference datasets: Enables comparison and validation of inferred interactions using reference resources such as ENCODE.

Methodology:

Integrates association rule mining with a novel Importance Index applied to ChIP-seq data and incorporates a pre-processing pipeline for extraction and preparation of ChIP-seq input data; the approach is applied to large-scale ChIP-seq datasets.