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Updated: Apr 30, 2026
22:27
Hi-C: A Method to Study the Three-dimensional Architecture of Genomes.
Published on: May 6, 2010
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ChIP-Atlas 3.0: a data-mining suite to explore chromosome architecture together with large-scale regulome data
Zhaonan Zou1,2, Tazro Ohta3,4,5, Shinya Oki1,2
1Institute of Resource Development and Analysis, Kumamoto University, 2-2-1 Honjo, Chuo-ku, Kumamoto 860-0811, Japan.
Nucleic Acids Research
|May 15, 2024
Summary
ChIP-Atlas 3.0 integrates diverse epigenomic data, including chromatin conformation and regulatory elements, to explore transcriptional regulation. A new tool aids in identifying differential genomic regions, advancing genetic disease research and drug discovery.
Area of Science:
- Genomics and Epigenomics
- Bioinformatics and Computational Biology
Background:
- Understanding chromatin architecture is crucial for deciphering transcriptional and phenotypic diversity.
- Existing resources for epigenomic data analysis require enhanced integration of diverse genomic features.
Purpose of the Study:
- To introduce ChIP-Atlas 3.0, an upgraded platform for analyzing epigenomic landscapes.
- To enhance the elucidation of chromatin architecture and transcriptional regulatory mechanisms.
Main Methods:
- Integration of over 376,000 public ChIP-seq, ATAC-seq, DNase-seq, and Bisulfite-seq experiments.
- Incorporation of new annotation tracks: chromosomal conformation (Hi-C, eQTL), regulatory elements (ChromHMM, FANTOM5), and genomic variants (GWAS, ClinVar).
- Development of 'Diff Analysis' tool for comparing epigenomic data and identifying differential regions.
Main Results:
- ChIP-Atlas 3.0 provides a consolidated view of epigenomic and genomic features.
- The 'annotation track' section facilitates the study of chromatin architecture.
- 'Diff Analysis' enables comparative epigenomic analysis across multiple data types.
Conclusions:
- ChIP-Atlas 3.0 is a robust resource for mining epigenomic data and understanding transcriptional regulation.
- The platform offers valuable insights for genetic disease research and drug discovery.
- Enhanced data integration and new analytical tools improve the exploration of epigenomic landscapes.
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