Genome-wide ChIP-seq of sample AG2_H3K27ac
We systematically determined the antibody and concentrations that produce the highest signal to noise ratio for MYB ChIP-seq using our automated ChIP-seq methodology and two MYB antibodies: abcam ab45150 and Sigma SAB4501936. We split the sheared chromatin between 3 ratios of antibody/chromatin (0.5µl, 1 µl and 5 µl of each antibody/1,000,000 cells), and performed ChIP-seq as previously described [Garber M et al.,Mol Cell,2012]. As a positive control, we included an antibody targeting H3K27ac (Cell Signaling Technologies D5E4, optimized at 1 µl/1,000,000 cells). We found 1 µl ab45150/1,000,000 cells to be optimal. ChIP libraries were indexed, pooled and sequenced on Illumina Hi-seq-2000 sequencers. Raw data was aligned to the mouse reference genome MM9 using Picard tools (http://broadinstitute.github.io/picard/). Raw sequencing data was mapped to the reference genome using bowtie2 version 2.2.1 with parameters -p 4 -k 1. Peaks were called using MACS version 1.4.2 over an input control. A p-value threshold of enrichment of 1E-6 was used. Density of genomic regions was calculated using bamliquidator_batch, version 1.1.0 (https://github.com/BradnerLab/pipeline/wiki/bamliquidator). Reads were extended 200-bp and normalized to read-density in units of reads per million mapped reads per bp (rpm/bp). To calculate genome-wide overlap, all enriched H3K27ac peaks were extended 5kb in each direction, divided into 50 bins, and read density was calculated in each bin. Density was normalized to the largest value observed in each experiment genome-wide and plotted as a heat map. Peaks and alignments were converted to TDFs by IGV tools and visualized by IGV. Bed files of published ChIP-seq data of H3K27ac chromatin maps from normal brain [Zhu J et al.,Cell,2013] were downloaded and visualized in IGV [Robinson JT et al.,Nat Biotech,2011].