TF-Prioritizer

TF-Prioritizer prioritizes condition-specific transcription factors by integrating chromatin state data (ChIP, ATAC, DNase sequencing) and RNA sequencing to identify TFs and regulatory elements associated with differential activity.

Key Features:

• Multimodal Data Integration: Integrates chromatin state data (ChIP, ATAC, DNase sequencing) with RNA sequencing to analyze cis-regulatory elements including promoters and enhancers.
• Identification of Differential TF Activity: Identifies transcription factors with differential activity across conditions by combining chromatin state and RNA sequencing data to detect tissue- and developmental-specific activities.
• Automated Analysis Pipeline: Automates processing and prioritization of transcription factors from large-scale genomic datasets.
• Input Support: Accepts ATAC, DNase, or ChIP sequencing data alongside RNA sequencing as inputs.
• Implementation: Implemented as a Java-based pipeline.
• CRE Regulatory Potential Assessment: Links accessibility of cis-regulatory elements to gene expression to prioritize regulatory elements and their associated TFs.

Scientific Applications:

• Condition-specific TF discovery: Identifies known and novel transcription factors active under specific conditions such as lactating mouse mammary gland.
• ENCODE dataset analysis: Facilitates analysis of ENCODE datasets for cell lines such as K562 and MCF-7 to explore genome-wide gene regulation patterns.
• Pathogenesis and target inference: Supports investigation of pathogenesis mechanisms and potential therapeutic targets by elucidating condition-specific TF activities.

Methodology:

Accepts ATAC, DNase, or ChIP sequencing and RNA sequencing inputs and processes these data to identify transcription factors exhibiting differential activity across conditions, linking CRE accessibility to gene expression.