BARTweb

BARTweb identifies transcription factors and chromatin regulators that bind cis-regulatory regions by associating input genomic features or gene sets with public ChIP-seq binding profiles in human and mouse.

Key Features:

• TF and chromatin regulator identification: Identifies transcription factors (TFs) and chromatin regulators that bind at cis-regulatory regions and influence gene expression in human and mouse genomes.
• ChIP-seq repository: Leverages over 13,000 public ChIP-seq datasets for human and mouse to enable comprehensive association analyses.
• Accepted input types: Accepts query gene lists, ChIP-seq datasets, or genomic region sets as inputs for association with TF binding profiles.
• Enhanced BART algorithm: Utilizes an enhanced version of the Binding Analysis for Regulation of Transcription (BART) algorithm to identify functional TFs linked to input features.
• Motif-independent association analysis: Performs association analysis that bypasses traditional DNA sequence motif analysis by correlating binding patterns with input genomic features.
• Performance and efficiency: Reduces execution time by up to two orders of magnitude compared to its predecessor.
• Improved accuracy: Demonstrates superior ability to identify true TFs from experimental datasets relative to existing tools.

Scientific Applications:

• Functional analysis of gene regulation: Elucidates transcription factors associated with specific genomic features to investigate mechanisms of differential gene expression.
• Integration with ChIP-seq data: Correlates TF binding profiles with ChIP-seq datasets to explore regulatory networks and chromatin landscapes.
• Cross-species analysis: Supports analyses in both human and mouse genomes for comparative and translational studies.

Methodology:

Applies an association analysis using an enhanced BART algorithm that leverages over 13,000 public ChIP-seq datasets to identify TFs and chromatin regulators whose binding patterns are associated with input gene lists, ChIP-seq data, or genomic region sets, bypassing traditional DNA sequence motif analysis.