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In ChIP-Seq and DNase-Seq experiments the density of background reads can vary from sample to sample. Differences in background read densities between samples do not necessarily correspond to proportional changes of read densities in true ChIP-Seq peaks. Therefore, scaling by total library size as a means for normalizing called ChIP-Seq peaks across samples may be ineffective. We suggest a simple easily implemented alternative to scaling by total library size that scales only by the total number of reads mapped to called peaks. We then demonstrate the effectiveness of the modified scaling in K4me3 and K27ac ChIP-Seq data from the BrainSpan project as well as DNase-Seq data from the ENCODE project.