ChIPBase

ChIPBase catalogs transcriptional regulatory interactions and regulatory elements to enable analysis of transcriptional regulation of non-coding RNAs (ncRNAs) and protein-coding genes (PCGs).

Key Features:

• Regulatory Relationships: Approximately 151 million regulatory interactions between ~171,600 genes and >3,000 regulators derived from analysis of >55,000 ChIP-seq datasets.
• Motif Matrices: De novo identification of ~29,000 motif matrices for transcription factors.
• Enhancer Module: Prediction of ~1.8 million regulatory regions classified as poised, active, or super enhancers across >1,300 conditions.
• Coexpression Maps: Integration of expression profiles from ~65,000 normal and ~15,000 tumor samples to construct coexpression maps between regulators and target genes.
• Disease Module: Atlas and annotation of disease-associated variations in regulatory regions of genes.
• EpiInter Module: Exploration of potential interactions between the epitranscriptome and the epigenome.
• Network Module: Construction of extensive, gene-centered regulatory networks to identify key regulatory nodes and interactions.

Scientific Applications:

• Transcriptional regulation analysis: Study regulatory networks controlling ncRNAs and PCGs across conditions.
• Transcription factor and motif studies: Characterize TF binding patterns using de novo motif matrices.
• Enhancer dynamics: Investigate poised, active, and super enhancer activities across biological conditions.
• Disease genomics: Map disease-associated regulatory variations to gene regulatory mechanisms.
• Integrative expression analysis: Compare regulator–target coexpression between normal and tumor samples to study dysregulation.
• Epitranscriptome–epigenome interaction studies: Explore potential links between RNA modifications and chromatin regulation.

Methodology:

Analysis of >55,000 ChIP-seq datasets; de novo motif identification (~29,000 matrices); prediction and classification of ~1.8 million enhancer regions (poised, active, super); integration of ~65,000 normal and ~15,000 tumor expression profiles to build coexpression maps; compilation of ~151 million regulator–gene interactions; annotation of disease-associated regulatory variations; exploration of epitranscriptome–epigenome interactions and construction of gene-centered regulatory networks.