Personalized and graph genomes reveal missing signal in epigenomic data

Abstract

BackgroundEpigenomic studies that use next generation sequencing experiments typically rely on the alignment of reads to a reference sequence. However, because of genetic diversity and the diploid nature of the human genome, we hypothesized that using a generic reference could lead to incorrectly mapped reads and bias downstream results.\n\nResultsWe show that accounting for genetic variation using a modified reference genome (MPG) or a denovo assembled genome (DPG) can alter histone H3K4me1 and H3K27ac ChIP-seq peak calls by either creating new personal peaks or by the loss of reference peaks. MPGs are found to alter approximately 1% of peak calls while DPGs alter up to 5% of peaks. We also show statistically significant differences in the amount of reads observed in regions associated with the new, altered and unchanged peaks. We report that short insertions and deletions (indels), followed by single nucleotide variants (SNVs), have the highest probability of modifying peak calls. A counter-balancing factor is peak width, with wider calls being less likely to be altered. Next, because high-quality DPGs remain hard to obtain, we show that using a graph personalized genome (GPG), represents a reasonable compromise between MPGs and DPGs and alters about 2.5% of peak calls. Finally, we demonstrate that altered peaks have a genomic distribution typical of other peaks. For instance, for H3K4me1, 518 personal-only peaks were replicated using at least two of three approaches, 394 of which were inside or within 10Kb of a gene.\n\nConclusionsAnalysing epigenomic datasets with personalized and graph genomes allows the recovery of new peaks enriched for indels and SNVs. These altered peaks are more likely to differ between individuals and, as such, could be relevant in the study of various human phenotypes.