hTFtarget

hTFtarget catalogs human transcription factor–target regulatory relationships by integrating Chromatin immunoprecipitation sequencing (ChIP-Seq) data and epigenetic information to support analysis of TF binding and regulatory mechanisms.

Key Features:

• ChIP-Seq integration: Integration of 7,190 ChIP-Seq samples from 659 transcription factors to aggregate TF binding data.
• High-confidence binding sites: Generation or provision of high-confidence binding sites for 699 transcription factors.
• Epigenetic context: Incorporation of epigenetic modification information to refine predicted TF-target regulations.
• Dataset-, tissue-, and cell line–specific regulations: Representation of TF-target regulations within particular datasets, tissues, or cell lines for context-specific analysis.
• TF identification for targets and ncRNAs: Analytical linkage of potential transcription factors to given target genes and non-coding RNAs (ncRNAs).
• TF co-association and co-regulation analysis: Examination of co-associations between transcription factors and potential co-regulations among target genes or TFs across conditions.
• TFBS prediction: Prediction of candidate Transcription Factor Binding Sites (TFBS) on specified DNA sequences.
• ChIP-Seq peak visualization integration: Integration with genome browsers to visualize ChIP-Seq peaks for transcription factors under different conditions.

Scientific Applications:

• Gene regulatory network analysis: Support for detailed analyses of TF-target interactions across different biological conditions to inform network reconstruction.
• Discovery of regulatory mechanisms: Identification of novel TF-target regulatory mechanisms using integrated ChIP-Seq and epigenetic data.
• Disease and therapeutic research: Elucidation of disease-associated transcriptional changes and discovery of potential therapeutic targets via modulation of transcription factor activity.

Methodology:

Integration and processing of 7,190 ChIP-Seq samples from 659 TFs to derive high-confidence binding sites for 699 TFs, incorporation of epigenetic modification data, prediction of TFBS on query DNA sequences, and integration with genome browsers for ChIP-Seq peak visualization.