GoKawiilThis study did not involve human participants or animal subjects. The combinatorial enhancer perturbation experiment in this study used established cell lines obtained from commercial or publicly available sources. All other computational analyses used publicly available data, and are not considered human subjects research. Refer to the Supplementary Methods for more detailed descriptions of each section in Methods.
Genome build and gene annotations
All coordinates are reported in GRCh38 unless otherwise specified. We used a curated set of 20,678 gene promoters (one per gene symbol), defined as 500-bp regions centred on the RefSeq TSS with the largest number of coding isoforms. Gene symbols were matched to ENSEMBL IDs using the HUGO database72 and GENCODE v.29, retaining genes annotated as protein_coding, processed_transcript or lincRNA by GENCODE.
Biosamples and DNase-seq data processing
We computed ENCODE-rE2G and ABC predictions for 1,458 ENCODE DNase-seq experiments covering 369 unique cell types and tissues (Supplementary Table 12). Using the ENCODE API, we downloaded metadata and BAM files for released DNase-seq experiments, filtering for ENCODE4 analysis versions in GRCh38. Sequencing run type (single- or paired-ended) was determined by merging FASTQ metadata with the corresponding BAM file records. Single-ended BAM files were filtered to remove low-quality and multi-mapping reads (samtools view -F 780 -q 30) without PCR-duplicate removal, as duplicate reads are difficult to detect reliably in single-ended high-complexity DNase-seq data. Paired-ended BAM files were obtained as filtered alignments from the ENCODE pipeline, with filtering out low-quality, multi-mapping and PCR-duplicate reads (samtools view -F 1804 -q 30 -f 2 and Picard MarkDuplicates v.1.126). For experiments with mixed run types, only unfiltered alignments from single-ended reads were used. For ENCODE-rE2GExtended models, additional assay input files were selected manually (Supplementary Table 13).
Defining candidate elements and element–gene pairs
Candidate elements were defined from DNase-seq or ATAC-seq data using MACS2 (--shift −75 --extsize 150 --nomodel) on pooled replicates. After removing blacklisted regions, the top 150,000 peaks by read count were retained and resized to 500 bp centred on peak summits. TSS-centred 500-bp regions for all genes were added and overlapping regions were merged. Elements were classified as promoter (within 500 bp of any TSS), genic or intergenic. Candidate element–gene pairs included all pairs within 5 Mb.
Annotating enhancer–promoter pairs with epigenomic and genomic features
We computed features for each element–gene pair encompassing chromatin state, 3D contact frequency and genomic position (Supplementary Table 2).
Chromatin state
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