Personalized and graph genomes reveal missing signal in epigenomic data

Abstract

Background: Epigenomic 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 hypothesize that using a generic reference could lead to incorrectly mapped reads and bias downstream results. Results: We show that accounting for genetic variation using a modified reference genome or a de novo assembled genome can alter histone H3K4me1 and H3K27ac ChIP-seq peak calls either by creating new personal peaks or by the loss of reference peaks. Using permissive cutoffs, modified reference genomes are found to alter approximately 1% of peak calls while de novo assembled genomes 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. We show that using a graph personalized genome represents a reasonable compromise between modified reference genomes and de novo assembled genomes. We demonstrate that altered peaks have a genomic distribution typical of other peaks. Conclusions: Analyzing 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.

Bovine breed-specific augmented reference graphs facilitate accurate sequence read mapping and unbiased variant discovery

“…Graph-based [ 17 , 22 ] and personalized reference genomes [ 5 , 23 ] mitigate reference allele bias. Existing linear reference coordinates can serve as backbones for variation-aware genome graphs.…”

Genome graphs detect human polymorphisms in active epigenomic state during influenza infection

“…The extra peaks are influenced by rescued unmapped reads and by the shift of peaks across the statistical significance threshold of the peak caller [29]. This multi-sample epigenomic dataset is an opportunity to better understand how reliable the altered peaks are compared to common peaks.…”

“…Previously, we introduced an axis that orders reference genomes according to how similar they are to the genome they are meant to represent [29]. Our axis ranged from the least similar sequence (the reference genome) to the most similar (the de novo assembly).…”

“…We have previously shown that genome graphs can recover epigenomic signal that would have been missed in genetically variable regions of the genome [29]. However, this only considered the impact of small variants on peak calls in a single genome, in an undefined biological context and did not explore the effects of such an approach on putatively functional regulatory regions.…”

Reducing reference bias using multiple population reference genomes

“…But linearity leads to reference bias: a tendency to miss alignments or report incorrect alignments for reads containing non-reference alleles. This can ultimately lead to confounding of scientific results, especially for analyses concerned with hypervariable regions 2 , allele-specific effects [3][4][5][6] , ancient DNA analysis 7,8 or epigenenomic signals 9 . These problems can be more or less adverse depending on the individual under study; e.g.…”