bioRxiv Science⌕ Search

bioRxiv · 10.64898/2026.08.31.748368

Visualizing and integrating linear and graph pangenomes at the Maize Genetics and Genomics Database

Abstract

Genome visualization has existed for as long as assembled genomes, with an array of tools and approaches to meet researchers' needs. The genome browser is one of these tools, and it has become a critical analytical resource for geneticists, genome biologists, and breeders. The evolution of genome browser capabilities is ongoing, and the USDA-ARS Maize Genetics and Genomics Database (MaizeGDB) has implemented JBrowse2, the most recent iteration of JBrowse. It includes multiple genome browser and alignment views, demonstrating pangenome visualization capability and permitting sophisticated functional characterization of maize loci. Described here also is MaizeGDB's new Pangenome Viewer, which integrates pangenome graph views with linear browser visualization to obtain an on-the-fly, interactive visual snapshot of structural variation across a pangenome at user-selected loci, with zoom capabilities and statistical and variant information for each subgraph. Finally, we demonstrate how different MaizeGDB pangenome pipelines complement one another to help guide accurate analyses. This functionality can lead to more precise characterization of loci that confer important agronomic traits, resulting in better outcomes for farmers and the public.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Portwood, J. L., Cannon, E. K., Haley, O. C., Tibbs-Cortes, L. E., Andorf, C. M., Woodhouse, M. R.. 2026-09-04. Visualizing and integrating linear and graph pangenomes at the Maize Genetics and Genomics Database. https://doi.org/10.64898/2026.08.31.748368

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

MicroDUET: a dual-headed genomic foundation model for pathogen surveillance

Pathogen genomic surveillance works from raw reads, assembled genomes and alignments, representations that are slow and costly to compare at scale. Compact embeddings from genomic language models offer an alternative, but surveillance requires resolving both evolutionary ancestry (phylogeny) and phenotypic capability (function) - which need not co-vary. Here we present MicroDUET, which adapts a 26.6-million-parameter ProkBERT backbone with two 3.5-million-parameter adapter heads: a phylogeny head trained contrastively on genome windows from the same microbes, and a function head trained to identify genes across taxa. On held-out retrieval benchmarks, the phylogeny head returned the correct species for 80.3% and 66.6% of cellular and viral queries, and the function head the correct gene family for 97.3% and 97.2%, reaching 74.4% accuracy on an independent antimicrobial resistance-class benchmark. Across all three benchmarks MicroDUET outranked 17 other genomic encoders. Without training on lineage, clade or drug-susceptibility tasks, MicroDUET identified Mycobacterium tuberculosis and mpox virus lineages and clades (0.965, 0.975 accuracy), discriminated rifampicin resistance without lineage adjustment (AUROC 0.985), and retrospectively identified an emerging mpox lineage as anomalous (AUROC 0.974). MicroDUET operates 10^3 - 10^4 times faster than single-threaded sequence-based alignments. MicroDUET therefore provides a compact, reusable representation for scalable pathogen surveillance and sequence triage.

genomics↗

A chromosome-level genome assembly of northern dragonhead, Dracocephalum ruyschiana L., 1753

We present a chromosome-level genome assembly for the vascular plant northern dragonhead, Dracocephalum ruyschiana. The assembly comprises two pseudo-haplotypes, hap1 and hap2, of 661 Mb and 681 Mb, with 93.6% and 92.5%, respectively, scaffolded into seven chromosomes each - in line with the known karyotype. Assembly quality was high, with conserved single-copy ortholog completeness scores of 97.9% and 97.8%, combined k-mer completeness of 98.1%, and 38,141 and 38,617 annotated protein-coding genes for hap1 and hap2, respectively. This assembly provides a high-quality genomic resource for future research and conservation of northern dragonhead, as well as the endangered northern dragonhead sap beetle, which depends on the plant for reproduction and whose genome assembly is described in a complementary genome note.

genomics↗

Elucidating Bat SuperImmunity: Insights from the Bent-Wing Bat Genome Assembly and Multi-Omics Analysis

Bats have evolved a range of unique adaptations, including powered flight, echolocation, and exceptional longevity. They have also attracted considerable scientific attention, particularly since the COVID-19 pandemic, due to their association with a broad diversity of viruses and their potential role in zoonotic transmission and emerging infectious diseases. Despite hosting numerous viruses, bats often exhibit limited clinical manifestations of infection, suggesting the evolution of distinctive mechanisms of immune regulation and tolerance. Understanding the molecular basis of these mechanisms has become a major focus of current research, with genome-wide analyses providing important insights into the genetic adaptations underlying bat immunity. Nevertheless, the regulatory complexity of bat genomes, particularly the contribution of non-coding RNAs and post-transcriptional RNA editing, remains comparatively underexplored. Here, we present an integrated multi-omic framework for the common bent-wing bat (Miniopterus schreibersii), combining a contiguous chromosome-scale genome assembly with multi-tissue transcriptomic profiling, long non-coding RNA (lncRNA) identification and synteny analysis, endogenous viral elements (EVE) mapping, and same-individual RNA editomic analyses. This study provides a comprehensive resource for investigating genome organization, transcriptional regulation, evolutionary relationships, and post-transcriptional modifications in this species. In particular, it offers an opportunity to explore the potential contribution of non-coding regulatory elements and RNA editing to the molecular mechanisms underlying viral interactions and immune regulation in bats. Collectively, these resources provide a valuable foundation for advancing our understanding of bat genome evolution and the regulatory mechanisms potentially associated with their distinctive responses to viral infections.

genomics↗