ChIP-seq data mapping and normalization

ChIP-seq dataset for DPY30 and MLL1 were downloaded from GEO {"type":"entrez-geo","attrs":{"text":"GSE26136","term_id":"26136"}}GSE26136 and GEO {"type":"entrez-geo","attrs":{"text":"GSE107406","term_id":"107406"}}GSE107406, respectively. Paired-end sequencing reads were trimmed with trim_galore to remove adaptor sequences. We kept reads that were 20 bp or longer after trimming and paired between the mates. All ChIP-seq data were mapped to the mouse mm10 genome by using Bowtie2 (v2-2.2.4)¹⁰² with parameters “-q --phred33 --very-sensitive -p 10”. Duplicated reads were removed using SAMtools (v1.5)¹⁰³. The bigwig files for IP/input ratio were generated from BAM files by using deepTools3 (v3.2.1)¹⁰⁴ with command “bamCompare -b1 ChIP-bam -b2 Input-bam --ignoreDuplicates --minMappingQuality 30 --normalizeUsing RPKM --binSize 1 --operation ratio --scaleFactorsMethod None -p 20”. BAM files for mapping results were merged using SAMtools and converted to BED format using BEDTools¹⁰⁵. Peaks were called from bed files using MACS (v 1.4.2)¹⁰⁶ with parameters “-w -S -p 0.00001 -g mm”. The input signal was used as the control for peak calling. Heatmap of ChIP-seq signals were visualized using deepTools3.