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Abstract ChIP-Atlas (https://chip-atlas.org/) presents a suite of data-mining tools for analyzing epigenomic landscapes, powered by the comprehensive integration of over 376 000 public ChIP-seq, ATAC-seq, DNase-seq and Bisulfite-seq experiments from six representative model organisms. To unravel the intricacies of chromatin architecture that mediates the regulome-initiated generation of transcriptional and phenotypic diversity within cells, we report ChIP-Atlas 3.0 that enhances clarity by incorporating additional tracks for genomic and epigenomic features within a newly consolidated ‘annotation track’ section. The tracks include chromosomal conformation (Hi-C and eQTL datasets), transcriptional regulatory elements (ChromHMM and FANTOM5 enhancers), and genomic variants associated with diseases and phenotypes (GWAS SNPs and ClinVar variants). These annotation tracks are easily accessible alongside other experimental tracks, facilitating better elucidation of chromatin architecture underlying the diversification of transcriptional and phenotypic traits. Furthermore, ‘Diff Analysis,’ a new online tool, compares the query epigenome data to identify differentially bound, accessible, and methylated regions using ChIP-seq, ATAC-seq and DNase-seq, and Bisulfite-seq datasets, respectively. The integration of annotation tracks and the Diff Analysis tool, coupled with continuous data expansion, renders ChIP-Atlas 3.0 a robust resource for mining the landscape of transcriptional regulatory mechanisms, thereby offering valuable perspectives, particularly for genetic disease research and drug discovery.
Zou et al. (Thu,) studied this question.