Pangenome Graph Construction from Genome Alignment with Minigraph-Cactus

Abstract

Reference genomes provide mapping targets and coordinate systems but introduce biases when samples under study diverge sufficiently from them. Pangenome references seek to address this by storing a representative set of diverse haplotypes and their alignment, usually as a graph. Alternate alleles determined by variant callers can be used to construct pangenome graphs, but thanks to advances in long-read sequencing, high-quality phased assemblies are becoming widely available. Constructing a pangenome graph directly from assemblies, as opposed to variant calls, leverages the graph's ability to consistently represent variation at different scales and reduces biases introduced by reference-based variant calls. Pangenome construction in this way is equivalent to multiple genome alignment, which is extremely computationally demanding. Here we present the Minigraph-Cactus pangenome pipeline and demonstrate its ability to build a pangenome graph by aligning 90 human haplotypes. This tool was designed to build graphs with as much information as possible while still being practical for use with current mapping and genotyping tools. We show that this graph is useful both for studying variation within the input haplotypes, but also as a basis for mapping and variant calling. We also demonstrate that this method can apply to nonhuman data by showing improved mapping and variant detection sensitivity with a Drosophila melanogaster pangenome.

Comparing Genomic and Epigenomic Features across Species Using the WashU Comparative Epigenome Browser

“…In this case, the presence and hypermethylation of the Alu in HG002 is only visible when the reads were mapped to the CHM13 reference genome (Foox et al 2021; Nurk et al 2021). Therefore, the WashU Comparative Epigenome Browser provides a near-term, conventional visualization of differential mapping results before the maturation of pangenome graph mapping and subsequent visualization (Miga and Wang 2021; Wang et al 2022; Liao et al 2022; Hickey et al 2022; Guarracino et al 2021).…”

“…The UCSC Genome Browser, equipped with comprehensive annotations and intuitive navigation, gained widespread popularity in the community (Kent et al 2002;Lee et al 2022). In addition to the UCSC Genome Browser, there are multiple other tools available to visualize genomes each with its own advantages and focuses (e.g., Ensembl (Fernández-Suárez and Schuster 2010; Cunningham et al 2022), GBrowse (Stein et al 2002), WashU Epigenome Browser (Li et al 2019(Li et al , 2022Zhou et al 2011), IGV (Robinson et al 2011(Robinson et al , 2022, and JBrowse (Buels et al 2016;Diesh et al 2022)).…”

“…The WashU Epigenome Browser was developed in 2010 to host and display massive epigenomics datasets (Zhou et al 2011;Li et al 2019Li et al , 2022 (Bujold et al 2016), The Cancer Genome Atlas (TCGA) (Hutter and Zenklusen 2018), Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET) (Wang et al 2018), and 4D Nucleome Project (4DN) (Dekker et al 2017). We recently refactored the browser and vastly improved its performance (Li et al 2019(Li et al , 2022 Build upon the WashU Epigenome Browser, we developed the WashU Comparative Epigenome Browser based on four principles: 1, each assembly uses its own coordinates to anchor annotation and datasets mapped to it; 2, orthologous relationship and genetic variations between assemblies are intuitively illustrated; 3, adaptable to display any whole genome alignment at different scales and resolution; 4, inherits all features of modern genome browsers to facilitate user experience. Here we present the WashU Comparative Epigenome Browser to address the needs to navigate multiple genomes at once and visualize comparative genomics/epigenomics data.…”

Building pangenome graphs

“…They allow us to identify variation, measure conservation, detect recombination events, and infer phylogenetic relationships, making them valuable tools for studying sequence evolution and variation in diverse species [5,6]. However, existing methods for constructing pangenome graphs [7,8] are biased due to their reference and tree-guided approaches [5,9], which can lead to incomplete and unstable representations of genetic variation [10]. Meanwhile, although approaches for unbiased pangenome graph construction have been proposed [10,11], these have been limited to the graph induction step, while experience shows that specialized techniques for pangenome alignment and refinement are required to obtain high-quality pangenome builds [12].…”

“…Meanwhile, although approaches for unbiased pangenome graph construction have been proposed [10,11], these have been limited to the graph induction step, while experience shows that specialized techniques for pangenome alignment and refinement are required to obtain high-quality pangenome builds [12]. Bias results from attempts to tackle the inevitable computational complexity that arises when building pangenome graphs, which imply all-to-all comparisons that scale quadratically with the number of included genomes [5,7], or from a goal to structure the resulting graphs so that they are easier to use during read alignment [8].…”