tfLDA

tfLDA applies topic modeling to analyze large-scale chromatin immunoprecipitation sequencing (ChIP-Seq) datasets and identify recurrent transcriptional regulatory modules (TRMs) that represent combinatorial transcription factor (TF) binding patterns.

Key Features:

• Comprehensive Analysis: Joint analysis of thousands of genome-wide ChIP-Seq datasets across numerous transcription factors and cell lines to capture broad regulatory patterns.
• Discovery of TRMs: Identification of potential interactions and cooperations among TFs by learning high-order combinatorial binding patterns from multiple ChIP-Seq profiles.
• Interpretation and Visualization: Computational methods for interpreting and visualizing identified TRMs and their constituent TF co-binding events.
• Application to Diverse Cell Lines: Applicable to cell lines with extensive TF datasets, enabling recovery of well-known TRMs and related co-binding events among TFs.

Scientific Applications:

• Transcriptional Regulation Research: Elucidation of combinatorial regulatory mechanisms that govern gene expression through discovery of TRMs from ChIP-Seq data.
• TF Interaction Studies: Characterization of transcription factor interactions and cooperativity within regulatory modules across cell types.

Methodology:

Employs state-of-the-art topic models to analyze ChIP-Seq datasets and learns from multiple ChIP-Seq profiles to identify high-order combinatorial binding patterns that are interpreted and visualized as transcriptional regulatory modules.