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 hypothesized 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 (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.Conclusions: Analysing 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

“…We constructed bovine variation-aware reference graphs using a Hereford-based linear reference sequence as backbone and variants that were filtered for allele frequency in four cattle breeds. Using both simulated and real short read data, our findings corroborate that a variation-aware reference facilitates accurate read mapping and unbiased sequence variant genotyping [22,23,[32][33][34].…”

Index and biological spectrum of accessible DNA elements in the human genome

“…We have leveraged the high positional stability of DHS summits across cell types and states (median 38bp across >700 cell contexts) to define and annotate archetypal DHS-encoding elements within the human genome, and to encompass these under a common coordinate system and nomenclature. DHS identifiers are robust to genome builds, and transferable to personal genomes and emerging graph-based approaches to genome analysis 30,31 . They also enable straightforward incorporation of cell-selectivity properties (e.g., regulatory components) and structural/functional features such as DNase footprints.…”

“…Together these features create a powerful new framework for analyses at the intersection of gene regulation and the genetics of human diseases and quantitative traits. Archetypal DHS identifiers are robust to genome builds, transferable to personal genomes and emerging graph-based genome analysis 44,45 , and enable facile incorporation of functional properties such as cell-selectivity, or finer structural annotations such as DNase I footprints. Common reference coordinates will further greatly facilitate comparisons between large experimental data sets, and between human and mouse DHSs.…”

Reference flow: reducing reference bias using multiple population 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 – 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., African-ancestry genomes contain more ALT alleles, and so can be more severely affected by reference bias [ 10 ].…”