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At least 19 recordsLinked to original sources

PubMed2026

Graph-based pan-genome reveals structural and functional diversity across oil palm domestication gradients.

BACKGROUND: Oil palm (Elaeis guineensis Jacq.), the world's most land-efficient oil crop, underpins global vegetable oil supply yet faces mounting constraints from limited expansion, climate stress, and disease pressure. These challenges highlight the urgent need for genomic resources that capture species-wide diversity to support sustainable improvement. While recent reference assemblies have advanced trait discovery, single linear genomes fail to represent the full spectrum of structural and gene-content variation, limiting resolution of agronomic alleles. RESULTS: Here, we constructed a graph-based pan-genome from 30 diverse oil palm assemblies representing wild, semi-domesticated, and commercial accessions. We characterized structural variants, gene presence-absence variation, and copy-number gains, with focusing on functional stratification and resistance gene dynamics. The graph-based pan-genome revealed extensive structural and gene-content variation, including a large conserved core, complemented by shell and unique fractions enriched or biased toward regulatory, stress-responsive, and defense-related functions. Structural variation and duplication-derived copy-number gains contributed substantially to gene-content diversity, with semi-domesticated accessions exhibiting the greatest variability. Resistance gene repertoires showed contrasting patterns: receptor-like kinases remained comparatively stable, whereas the CNL subclass of NLR genes contributed disproportionately to shell-genome variation and duplication-associated turnover. CONCLUSIONS: This graph-based pan-genome provides a curated multi-assembly reference and comparative framework for oil palm genomics. By capturing structural variants, gene-content variations, copy-number gains, and resistance gene dynamics across domestication gradients, it establishes a foundation for future pan-GWAS analysis, functional genomics, and molecular breeding strategies aimed at improving resilience and productivity in this globally important crop.

Arecaceae↗

PubMed2025

PlantPan: A comprehensive multi-species plant pan-genome database.

The pan-genome represents the complete genomic diversity of specific species, serving as a valuable resource for studying species evolution, crop domestication, and guiding crop breeding and improvement. While there are several single-species-specific plant pan-genome databases, the availability of multi-species pan-genome databases is limited. Additionally, variations in methods and data types used for plant pan-genome analysis across different databases hinder the comparison and integration of pan-genome information from various projects at multi-species or single-species levels. To tackle this challenge, we introduce PlantPan, a comprehensive database housing the results of pan-genome analysis for 195 genomes from 11 plant species. PlantPan aims to provide extensive information, including gene-centric and sequence-centric pan-genome information, graph-based pan-genome, pan-genome openness profiles, gene functions and its variation characteristics, homologous genes, and gene clusters across different species. Statistically, PlantPan incorporates 9 163 011 genes, 694 191 gene clusters, 526 973 370 genome variations, and 1 616 089 non-redundant genome variation groups at the species level, 33 455,098 genome synteny, and 177 827 non-redundant genome synteny groups at the species level. Regarding functional genes, PlantPan contains 5 222 720 genes related to transcription factors, 395 247 literature-reported resistance genes, 455 748 predicted microbial/disease resistance genes, and 1 612 112 genes related to molecular pathways. In summary, PlantPan is a vital platform for advancing the application of pan-genomes in molecular breeding for crops and evolutionary research for plants.

Genome, Plant↗

PubMed2026

Seqwin: ultrafast identification of signature sequences in microbial genomes.

MOTIVATION: Polymerase chain reaction (PCR) enables rapid, cost-effective diagnostics but requires prior identification of genomic regions that allow sensitive and specific detection of target microbial groups, herein referred to as microbial signature sequences. We introduce Seqwin, an open-source framework designed to automate microbial genome signature discovery. Tens of thousands of microbial genomes are now available for a single species, limiting the application of existing manual and automated approaches for identifying signatures. Modern approaches that are capable of leveraging all available microbial genomes will ensure sensitive and accurate DNA signature identification and enable robust pathogen detection for clinical, environmental, and public health applications. RESULTS: Seqwin builds weighted pan-genome minimizer graphs and uses a traversal algorithm to identify signature sequences that occur frequently in target genomes but remain rare in non-targets. Unlike earlier tools that depend on strict presence or absence of sequences, Seqwin accommodates natural sequence variation and scales to very large genome collections. When applied to genomes from C. difficile, M. tuberculosis, and S. enterica, Seqwin recovered more high-quality signatures than alternative methods with lower computational burden. Seqwin's analysis of nearly 15 000 S. enterica genomes yielded over 200 candidate signatures in three minutes. Seqwin provides an open-source solution for the long-standing need for scalable microbial signature discovery and diagnostic assay design. AVAILABILITY AND IMPLEMENTATION: Seqwin is available on GitHub (https://github.com/treangenlab/Seqwin) and can be installed via Bioconda (https://bioconda.github.io/recipes/seqwin/README.html). Benchmarking datasets, outputs, and scripts are available on Zenodo (https://doi.org/10.5281/zenodo.19874011).

Software↗

PubMed2026

Analysis of deep-resequencing data of 984 soybean accessions reveals structural variations underlying agronomic traits.

Genomic structural variants (SVs) are major sources of genetic variation and have profound impacts on phenotypic traits. However, their functional effects remain largely unexplored in soybean. Here, we resequence 940 soybean accessions. Together with 44 publicly available datasets, we identify 602,281 SVs. Using a graph-based genome, we detect an additional 58,760 presence/absence variations (PAVs) that broadly affect gene expression. Population genomic analyses reveal that SVs serve as a core driving force for soybean domestication and improvement. Integrating SVs with QTLs for oil and protein content, and performing GWAS on 27 traits, we identify key functional SVs. These include transposable element insertions altering seed coat color, multiple insertions within a cytochrome P450 gene modifying flower and hypocotyl color, and a GmMATE1 deletion enhancing seed size. Together, our study establishes a comprehensive SV map of soybean, offering a valuable resource for dissecting the genetic basis of complex traits to accelerate molecular breeding.

Glycine max↗

PubMed2025

A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.

Accurate genome binning is essential for resolving microbial community structure and functional potential from metagenomic data. However, existing approaches-primarily reliant on tetranucleotide frequency (TNF) and abundance profiles-often perform sub-optimally in the face of complex community compositions, low-abundance taxa, and long-read sequencing datasets. To address these limitations, we present MBGCCA, a novel metagenomic binning framework that synergistically integrates graph neural networks (GNNs), contrastive learning, and information-theoretic regularization to enhance binning accuracy, robustness, and biological coherence. MBGCCA operates in two stages: (1) multimodal information integration, where TNF and abundance profiles are fused via a deep neural network trained using a multi-view contrastive loss, and (2) self-supervised graph representation learning, which leverages assembly graph topology to refine contig embeddings. The contrastive learning objective follows the InfoMax principle by maximizing mutual information across augmented views and modalities, encouraging the model to extract globally consistent and high-information representations. By aligning perturbed graph views while preserving topological structure, MBGCCA effectively captures both global genomic characteristics and local contig relationships. Comprehensive evaluations using both synthetic and real-world datasets-including wastewater and soil microbiomes-demonstrate that MBGCCA consistently outperforms state-of-the-art binning methods, particularly in challenging scenarios marked by sparse data and high community complexity. These results highlight the value of entropy-aware, topology-preserving learning for advancing metagenomic genome reconstruction.

canonical correlation analysis↗

PubMed2025

Robust and accurate Bayesian inference of genome-wide genealogies for hundreds of genomes.

The Ancestral Recombination Graph (ARG), which describes the genealogical history of a sample of genomes, is a vital tool in population genomics and biomedical research. Recent advancements have substantially increased ARG reconstruction scalability, but they rely on approximations that can reduce accuracy, especially under model misspecification. Moreover, they reconstruct only a single ARG topology and cannot quantify the considerable uncertainty associated with ARG inferences. Here, to address these challenges, we introduce SINGER (sampling and inferring of genealogies with recombination), a method that accelerates ARG sampling from the posterior distribution by two orders of magnitude, enabling accurate inference and uncertainty quantification for hundreds of whole-genome sequences. Through extensive simulations, we demonstrate SINGER's enhanced accuracy and robustness to model misspecification compared to existing methods. We demonstrate the utility of SINGER by applying it to individuals of British and African descent within the 1000 Genomes Project, identifying signals of population differentiation, archaic introgression and strong support for ancient polymorphism in the human leukocyte antigen region shared across primates.

Humans↗

PubMed2026

VirBinn improves viral genome binning from metagenomic Hi-C through graph diffusion.

MOTIVATION: Metagenomic Hi-C provides in situ proximity signals that can improve genome binning and enable virus-host-association analysis. However, viral genome recovery remains difficult because virus-virus Hi-C contact matrices are extremely sparse. Viral genomes are small, often low-abundance, and frequently assemble into short contigs, leaving many true within-genome links unobserved and causing viral bins to fragment. RESULTS: We present VirBinn, a graph-diffusion framework for viral binning from metagenomic Hi-C. VirBinn enhances virus-virus connectivity through two complementary mechanisms: random-walk-with-restart enhancement on the sparse virus-virus contact graph and host-guided diffusion that propagates viral seeds through the host network to infer indirect virus-virus associations. The enhanced views are integrated and clustered using Leiden community detection to produce viral metagenome-assembled genomes (vMAGs). On dataset-specific simulation benchmarks with ground truth, VirBinn consistently recovers more high-quality vMAGs than Hi-C-based and shotgun-based baselines and substantially increases the number of near-complete genomes. On four real metagenomic Hi-C datasets spanning human gut, pig gut, sheep gut (long-read assembly), and wastewater, VirBinn yields more high-completeness vMAGs under CheckV and produces bins with strong within-cluster contact support. Finally, host linkage analysis using reconstructed host MAGs reveals habitat-specific host-association patterns and plausible host taxonomic profiles. AVAILABILITY AND IMPLEMENTATION: VirBinn is available at https://github.com/dyxstat/VirBinn. The scripts to reproduce the results and figures in this article are available at https://github.com/dyxstat/Reproduce_VirBinn.

Genome, Viral↗

PubMed2026

PangyPlot: multi-scale interactive visualization of pangenome variation graphs.

SUMMARY: Pangenome variation graphs integrate multiple samples into a unified representation, mitigating the reference bias inherent to linear genomes. However, these graphs can be large and structurally complex. Existing visualization tools are each confined to a fixed scale of resolution, requiring researchers to switch between multiple tools to examine variation at different levels of detail. PangyPlot is an interactive pangenome browser designed for multi-scale exploration of reference variation graphs from full chromosome to nucleotide-level sequence segments. PangyPlot anchors navigation to linear reference coordinates, organizes variation into hierarchical bubble structures, and uses a force-directed layout engine for automatic node arrangement. AVAILABILITY AND IMPLEMENTATION: An instance preloaded with data is available at https://pangyplot.research.sickkids.ca. Source code and documentation are openly available at https://github.com/strug-hub/pangyplot under the MIT License.

Software↗

PubMed2026

gaftools: a toolkit for analyzing and manipulating pangenome alignments.

MOTIVATION: Linear reference genomes are ubiquitously used in genomics research, despite known biases associated with their use. In recent years, there has been a shift towards graph-based reference genomes to address some of these biases, which has required development of new algorithms and file formats. This has created a necessity for new tools capable of utilizing these formats and performing operations similar to those carried out by traditional methods. RESULTS: In this paper we present "gaftools," a multi-purpose tool that introduces several utilities for processing graph alignments in GAF format. gaftools enables users to index and sort alignments, with graph ordering serving as a necessary step for the sorting process. Additionally, it allows users to view subsets of alignments and perform realignment using the wavefront alignment algorithm, among other features. Many of these functionalities are inspired by SAMtools, which provides similar operations for linear genomes, while gaftools adapts and extends them for pangenomes. AVAILABILITY: gaftools is available under MIT license at https://github.com/marschall-lab/gaftools.

Software↗

PubMed2025

SPC: a SPectral Component approach leveraging Identity-by-Descent graphs to address recent population structure in genomic analysis.

Population structure is a well-known confounder in statistical genetics, particularly in genome-wide association studies (GWAS), where it can lead to inflated test statistics and spurious associations. Traditional methods, such as principal components (PCs), commonly used to adjust for population structure, are limited in capturing fine-scale, non-linear patterns that arise from recent demographic events - patterns that are crucial for understanding rare variant effects. To address this challenge, we propose a novel method called SPectral Components (SPCs), which leverages identity-by-descent (IBD) graphs to capture and transform local, non-linear fine-scale population structure into continuous representations that can be seamlessly integrated into genetic analysis pipelines. Using both simulated datasets and empirical data from the UK Biobank (N ≈ 420,000), we demonstrate that SPCs outperform PCs in adjusting for fine-scale population structure. In simulations, SPCs explained over 90% of the fine-scale population structure with fewer components, while PCs captured less than 5%. In the UK Biobank, SPCs reduced the inflation of p-values in the GWAS of an environmental-driven phenotype by 12% compared to PCs, while maintaining a similar performance to PCs in height, a highly heritable phenotype. Additionally, SPCs improved rare variant association analyses, reducing genomic inflation (e.g., from 7.6 to 1.2 in one analysis), and provided more accurate heritability estimates. Spatial autocorrelation analysis further confirmed the ability of SPCs to account for environmental effects, reducing Moran's I for both environmental and heritable phenotypes more effectively than PCs. Overall, our findings demonstrate that SPCs provide a robust, scalable adjustment for recent population structure, offering a powerful alternative or complement to PCs in large-scale biobank studies.

GWAS↗

PubMed2025

Pairwise graph edit distance characterizes the impact of the construction method on pangenome graphs.

MOTIVATION: Pangenome variation graphs are an increasingly used tool to perform genome analysis, aiming to replace a linear reference in a wide variety of genomic analyses. The construction of a variation graph from a collection of chromosome-size genome sequences is a difficult task that is generally addressed using a number of heuristics. The question that arises is to what extent the construction method influences the resulting graph, and the characterization of variability. RESULTS: We aim to characterize the differences between variation graphs derived from the same set of genomes with a metric which expresses and pinpoint differences. We designed a pairwise variation graph comparison algorithm, which establishes an edit distance between variation graphs, threading the genomes through both graphs. We applied our method to pangenome graphs built from yeast and human chromosome collections, and demonstrate that our method effectively characterizes discordances between pangenome graph construction methods and scales to real datasets. AVAILABILITY AND IMPLEMENTATION: pancat compare is published as free Rust software under the AGPL3.0 open source license. Source code and documentation are available at https://github.com/dubssieg/rs-pancat-compare. Snapshot available on Software Heritage at swh:1:dir:61acda8ba3dac1709ed60530147d3871831be629.

Algorithms↗

PubMed2025

Reference-Free Variant Calling with Local Graph Construction with ska lo (SKA).

The study of genomic variants is increasingly important for public health surveillance of pathogens. Traditional variant-calling methods from whole-genome sequencing data rely on reference-based alignment, which can introduce biases and require significant computational resources. Alignment- and reference-free approaches offer an alternative by leveraging k-mer-based methods, but existing implementations often suffer from sensitivity limitations, particularly in high mutation density genomic regions. Here, we present ska lo, a graph-based algorithm that aims to identify within-strain variants in pathogen whole-genome sequencing data by traversing a colored De Bruijn graph and building variant groups (i.e. sets of variant combinations). Through in silico benchmarking and real-world dataset analyses, we demonstrate that ska lo achieves high sensitivity in single-nucleotide polymorphism (SNP) calls while also enabling the detection of insertions and deletions, as well as SNP positioning on a reference genome for recombination analyses. These findings highlight ska lo as a simple, fast, and effective tool for pathogen genomic epidemiology, extending the range of reference-free variant-calling approaches. ska lo is freely available as part of the SKA program (https://github.com/bacpop/ska.rust).

Polymorphism, Single Nucleotide↗

PubMed2026

Phylogenetic distribution and longitudinal persistence of plasmids in Mycobacterium abscessus.

Mycobacterium abscessus, a non-tuberculous mycobacterium, is a cause of severe respiratory infections, notably in individuals with underlying lung conditions. Its high levels of intrinsic and acquired antimicrobial resistance make it particularly difficult to treat and horizontally acquired genetic elements may facilitate the spread of resistance. A small number of plasmids have been identified in this species, but their distribution, transmission dynamics across subspecies and clonal lineages remain poorly characterized. We analysed short-read genomic data from 3,060 M. abscessus isolates, including longitudinal samples, to characterize plasmid diversity and dynamics. Using a graph-based pan-genome approach, we identified 28 plasmids, including 15 previously unreported plasmids, mapped their distribution onto the species phylogeny and assessed their functional potential. Overall, 23.1% of isolates carried at least one plasmid, with higher prevalence in dominant circulating clones (DCCs) compared with non-DCCs. Plasmid carriage varied across subspecies and clonal backgrounds, and plasmids encoded numerous genes which may be linked to bacterial adaptation. Several plasmids persisted across multiple time points within individual patients, suggesting they can be highly stable over the course of a chronic infection.

Plasmids↗

PubMed2019

Identifying loci under positive selection in complex population histories.

Detailed modeling of a species' history is of prime importance for understanding how natural selection operates over time. Most methods designed to detect positive selection along sequenced genomes, however, use simplified representations of past histories as null models of genetic drift. Here, we present the first method that can detect signatures of strong local adaptation across the genome using arbitrarily complex admixture graphs, which are typically used to describe the history of past divergence and admixture events among any number of populations. The method-called graph-aware retrieval of selective sweeps (GRoSS)-has good power to detect loci in the genome with strong evidence for past selective sweeps and can also identify which branch of the graph was most affected by the sweep. As evidence of its utility, we apply the method to bovine, codfish, and human population genomic data containing panels of multiple populations related in complex ways. We find new candidate genes for important adaptive functions, including immunity and metabolism in understudied human populations, as well as muscle mass, milk production, and tameness in specific bovine breeds. We are also able to pinpoint the emergence of large regions of differentiation owing to inversions in the history of Atlantic codfish.

Animals↗

PubMed2026

Pangenomic analyses in the cultivated grapevine confirm high genomic collinearity and extensive dispensable gene content likely involved in adaptation.

Pangenomes have now been developed for several horticultural crops, yet the extent to which genome diversity in sequence and organization contribute to plant adaptation and major agronomic traits remains poorly understood. Here, we assembled the genomes of 9 cultivated grapevine varieties and compared the genomes of 15 cultivated grapevine varieties for variation in gene and TE content. We found that genomic collinearity is highly conserved among varieties. We still observed substantial variation across genomes. Notably, we identified across varieties 55,662 orthologous genes, of which 55.3% appears to be dispensable. Dispensable genes are enriched for functions related to adaptation to biotic and abiotic constraints, suggesting that they may play a role in adaptation. Comparing our results with a recently published study, we found substantial differences with ∼12.6% of the genes we classified as core genes being classified as dispensable genes in this other study. We then constructed a pangenome graph and used it to performed genome-wide association studies for 3 important traits in grapevine production, which allowed us to include large structural variants as markers in the analyses. We identified 32 loci that we did not detect when we used the PN40024 genome as a reference, 20 of which are newly reported associations. Overall, our results indicates that despite recent advances in characterizing plant pangenomes, current gene classification into core and dispensable gene categories should be taken with caution. They also highlight the value of incorporating structural variants into GWAS, to better characterize the genetic architecture of agronomic traits.

Vitis↗

PubMed2026

Pan-genome-based resequencing of 2,320 accessions reveals structural variations and accelerates breeding advances in cultivated peanut.

The cultivated peanut is a crucial global legume crop that is essential for food security and nutrition, particularly in developing regions. However, its limited genetic variation hampers breeding progress and yield improvement. Here we constructed a graph-based pan-genome for peanut, incorporating 14 genomes that represent all 6 peanut varieties. Using this pan-genome, we genotyped 2,320 accessions, covering 88.03% of ICRISAT and 59.21% of USDA core germplasm, enriching valuable resources for genomic studies and breeding. We cataloged genomic structural variations and investigated the role of homoeologous exchanges in population divergence. Through our pan-genome approach, we overcame the challenges of genotyping posed by homoeologous exchanges and identified key genes associated with flowering and dwarfism in peanut. By integrating superior haplotypes and germplasm resources guided by the pan-genome, we further developed high-yield dwarf lines. This work provides essential genomic resources to accelerate functional gene discovery and modern peanut breeding.

Journal Article↗

PubMed2026

Pan-genomics and multi-omics for deciphering genetic variation and accelerating genetic improvement in ruminant livestock.

Livestock reference genomes have transformed the discovery of variants associated with production, reproduction, health, and environmental adaptation. Nevertheless, a single linear reference represents only one mosaic haplotype and incompletely captures sequence diversity within a species, particularly structural variants, copy-number changes, repeat-rich regions, and breed-specific sequences. Pangenomes address this limitation by integrating multiple high-quality assemblies or population-scale variants into a unified sequence or graph representation. Concurrently, multi-omics approaches connect genomic variation with transcriptomic, epigenomic, manuscriptproteomic, metabolomic, and microbiome responses, thereby improving biological interpretation of genotype-phenotype relationships. This review synthesizes recent progress in livestock pangenomics and multi-omics, with emphasis on cattle, goats, sheep, water buffalo, and chickens. It describes advances in long-read and haplotype-resolved sequencing, graph construction, structural-variant discovery and genotyping, functional annotation, and integrative analysis. Recent pangenome studies have uncovered substantial non-reference sequence, reduced reference bias, identified breed- and population-specific structural variants, and resolved candidate variants underlying pigmentation, body size, tail morphology, cashmere production, altitude adaptation, and other economically relevant traits. However, translation into routine breeding remains constrained by uneven population representation, inconsistent structural-variant definitions, limited functional annotation, computational demands, and insufficient validation across environments. Future progress will depend on diverse near-complete assemblies, graph-aware imputation and genomic prediction, long-read transcriptomics, single-cell and spatial omics, rigorous causal validation, and open, interoperable resources. Together, these developments can support more accurate, resilient, and biologically informed livestock improvement. Importantly, current dairy-cattle evidence indicates that pangenome-derived structural variants can substantially improve variant discovery and functional interpretation while yielding only marginal average gains in routine genomic prediction, favoring targeted augmentation rather than wholesale replacement of established SNP-based evaluations.

Animals↗

PubMed2026

Exploring differences across pangenome-graph representations using Escherichia coli O157:H7 as a model.

Pangenome graphs are increasingly used to represent population-scale bacterial diversity, yet construction methods span fundamentally different representation paradigms whose outputs and sensitivities to assembly quality remain poorly quantified. We systematically reviewed microbial pangenome graph tools and benchmarked seven representative methods spanning gene-cluster, compacted coloured de Bruijn graph, one hybrid approach and one multiple sequence alignment method. Using a repeat-rich Escherichia coli O157:H7 dataset with complete genomes and matched short-read data, we constructed graphs from identical inputs and observed orders-of-magnitude differences in graph size and fragmentation, indicating that global topology is driven by representation strategy. Varying completeness composition revealed that assembly fragmentation is a first-order determinant of graph structure: gene-cluster graphs contracted as draft assemblies replaced complete genomes, whereas compacted coloured de Bruijn graphs expanded, with distinct degree-prevalence fingerprints across tools. In contrast, the multiple sequence alignment method could not be evaluated across fragmented inputs because it did not run reliably on draft-assembly datasets. Computational cost mirrored these shifts and depended strongly on completeness composition, including a pronounced runtime penalty for one compacted coloured de Bruijn graph implementation on all-draft inputs. Finally, analysis of Shiga toxin loci showed that pangenome-level reconciliation by gene-cluster-based tools does not reliably correct assembly artefacts at challenging multi-copy genes and that performance varies by locus. Together, these findings show that pangenome graphs are representation-dependent models of bacterial diversity, and that, in this repeat-rich O157:H7 benchmark dataset, assembly completeness is a primary determinant of their topology, scalability, and locus-level accuracy.

Escherichia coli O157↗