The quality of sequenced reads (50 bp, paired-end for ChIP-seq; 100 bp, paired-end for ATAC-seq) was measured using FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc/), and low-quality portions of the sequenced reads were trimmed using Trim Galore (version 0.4.2; www.bioinformatics.babraham.ac.uk/projects/trim:galore/) with Cutadapt (version 1.12) (MARTIN, Marcel. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, [S.l.], v. 17, n. 1, p. pp. 10–12, May 2011. ISSN 2226–6089. Available at: http://journal.embnet.org/index.php/embnetjournal/article/view/200/479. Date accessed: 28 Sept. 2018. doi: https://doi.org/10.14806/ej.17.1.200). The trimmed reads were mapped to the mouse genome (reference assembly mm10) using HISAT2 (version 2.1.0) (37). The mapped reads were sorted by genomic positions using SAMtools (version 1.3) (38), and duplicated mapped reads were removed using Sambamba (version 0.6.5) (39). Biological replicates for each condition were merged, and the genomic binding sites (peaks) of a given protein were identified using HOMER (version 4.10.1) with an FDR-adjusted P value cutoff of 0.001 (40). The peaks that overlapped with the blacklisted regions that show anomalous, unstructured, and high signal/read counts (www.encodeproject.org/annotations/ENCSR636HFF/) were discarded. To identify reliable target genes of a given protein, we further removed peaks located 20 kb outside of the gene’s TSSs. To identify common or specific peaks between given conditions, the identified peaks in each condition were merged. Then, the merged peaks were used to define common or specific peaks using HOMER (getDifferentialPeaks) with default parameters, which were further adjusted upon manual inspection. deepTools (version 2.5.3-2-503c71b) (41) was used to draw heatmaps.

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