The hierarchical 3D organization of chromatin in eukaryotic cells plays a crucial role in gene regulation and adapts during development, in response to stimuli, and in disease. While large-scale chromatin domains at scales larger than TADs (up to hundreds of nanometers) have been observed using microscopy, recent advances such as PCC-seq enable high-throughput measurement of chromatin compaction at kilobase resolution, revealing local variability, including those around transcription start sites (TSS).
Here, we applied machine learning approaches, including Logistic Regression, Random Forest, and Histogram-based Gradient Boosting, to classify genomic regions according to chromatin compaction levels. Models were trained for regions of TSS (±1 kb, ±5 kb), genome-wide (1 kb, 100 kb), considering 2, 3, and 5 compaction classes. Predictions were based on 519 genomic features, including proteins and histone modifications ChIP-seq data, chromatin accessibility (ATAC-seq, DNA-seq), and transcription (BRU-seq, RNA-seq). As a baseline comparison, an analogous classification was performed for ATAC-seq signal in TSS ±1 kb regions.
Using permutation feature importance and single-feature ROC AUC scores, we ranked genomic marks associated with chromatin compaction. In TSS ±1 kb regions, EP400, ELF4, POLR2H, H2AFZ, and ATAC-seq signals were associated with decompaction, while H3K27me3 and STAG1 correlated with compaction. Moreover, in TSS ±5 kb regions, H3K27ac and PCBP1 were linked to decompaction, whereas MCM3 correlated with compaction. At whole genome scales, in 100 kb bins THRAP3 and MBD1 were associated with decompaction, while ZBTB33 and H3K9me3 correlated with compaction. At 1 kb resolution, SETDB1, ZC3H4, and ZNF263 were associated with increased compaction.

Co-authors: Ryan Burke, Shuting Liu, Andrew Belmont, Ewelina Holm Bidstrup, Monika Staniszewska, Ilaria Lupi