Methods and Developments in Graphical Pangenomics

Abstract

Pangenomes are organized collections of the genomic information from related individuals or groups. Graphical pangenomics is the study of these pangenomes using graphical methods to identify and analyze genes, regions, and mutations of interest to an array of biological questions. This field has seen significant progress in recent years including the development of graph based models that better resolve biological phenomena, and an explosion of new tools for mapping reads, creating graphical genomes, and performing pangenome analysis. In this review, we discuss recent developments in models, algorithms associated with graphical genomes, and comparisons between similar tools. In addition we briefly discuss what these developments may mean for the future of genomics.

Investigating structural variant, indel and single nucleotide polymorphism differentiation between locally adapted Atlantic salmon populations

“…As stated above, the higher breakpoint imprecision for long-read SVs might also increase (50-60%;de Boer et al, 2007). We can expect that these features also impact pangenome-based mapping and genotyping to some extent, especially repeats (Chen et al, 2019;Outten & Warren, 2021).…”

Population-scale genotyping of structural variation in the era of long-read sequencing

“…In contrast to adding genetic variations to the reference genome [67] , Sirén et al demonstrated an alternative strategy to genotype a new sample by tracing haplotype paths through the sequence graph [64] . They developed Giraffe, a haplotype-aware pangenome mapper that prioritizes alignments under supervision from known haplotypes to avoid search-space explosion caused by combinations of biologically unlikely alleles [43] , [44] , [49] , [64] , [70] . On average, it took about 194 CPU hours to genotype a sample with a median coverage of 20× by the combination of toil-vg [71] and Giraffe [64] .…”

“…The main advantage of pan-genomic approaches is that they can more accurately represent the complex variability of the genome [22] and improve genotyping of nested SVs in complex genetic backgrounds [4] , [64] . However, there is still an urgent need for efficient tools to construct complex graphs and perform sequence-to-graph alignments [42] , [43] , [44] . In the following sections, we summarize the characteristics of graph-based genotypers.…”

“…Similar to the detection strategy, traditional mapping-based genotypers extract SV signatures around the known breakpoints and determine the presence of alternative or reference alleles [39] . However, alignment against a single reference genome is biased towards the reference allele [22] , [42] , [43] , [44] , [45] , so sequences containing large deviated alternative alleles are prone to mismatches or multiple alignments [46] . As reported in several population-scale SV studies, many of the SVs identified by LRS were classified as homozygous references in SRS samples [12] , [14] , [32] , which severely impacts genotype-based population genetic studies or genome-wide association studies of complex diseases.…”

“…The graphical representation can describe all nested variants in the sequence more accurately than the linear structure [49] . However, appropriate tools are required to construct the variation graph and perform sequence-to-graph alignment and genotyping [42] , [43] , [44] . Finally, methods based on both strategies calculate support counts for the reference or alternative allele and then estimate the genotype through probabilistic [50] or machine learning models [12] .…”