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Genomics

Explore bioRxiv genomics preprints on sequencing, gene expression and genetic variation. Read source abstracts and verify methods in the manuscript; preprints are not peer reviewed.

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bioRxiv2025

Circos Plots for Genome Level Interpretation of Genomic Prediction Models

Ensemble of multiple genomic prediction models have grown in popularity due to consistent prediction performance improvements in crop breeding. However, technical tools that analyse the predictive behaviour at the genome level are lacking. Here, we develop a computational tool called Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP) that uses circos plots to visualise how different genomic prediction models quantify contributions of marker effects to trait phenotypes. As a demonstration of EasiGP, multiple genomic prediction models, spanning conventional statistical and machine learning algorithms, were used to infer the genetic architecture of days to anthesis (DTA) in a maize mapping population. The results indicate that genomic prediction models can capture different views of trait genetic architecture, even when their overall profiles of prediction accuracy are similar. Combinations of diverse views of the genetic architecture for the DTA trait in the TeoNAM study might explain the improved prediction performance achieved by ensembles, aligned with the implication of the Diversity Prediction Theorem. In addition to identifying well-known genomic regions contributing to the genetic architecture of DTA in maize, the ensemble of genomic prediction models highlighted several new genomic regions that have not been previously reported for DTA. Finally, different views of trait genetic architecture were observed across sub-populations, highlighting challenges for between-population genomic prediction. A deeper understanding of genomic prediction models with enhanced interpretability using EasiGP can reveal several critical findings at the genome level from the inferred genetic architecture, providing insights into the improvement of genomic prediction for crop breeding programs. Plain Language SummaryWhile an ensemble of genomic prediction models has been applied in crop breeding, the prediction mechanism has not been well-investigated due to the lack of a computational tool to interpret the predictive behaviour. It is critical to investigate prediction models at the genome level to understand how each model quantifies genomic marker effects contributing to the trait genetic architecture. Hence, we developed a computational tool, the Ensemble AnalySis with Interpretable Genomic Prediction (EasiGP), to investigate the genome features and predictive behaviours of the ensemble. Here, we demonstrate the utility of EasiGP using a maize breeding dataset. EasiGP visualised the genetic architecture from diverse interpretable genomic prediction models and identified several well-known key maize genes. EasiGP also revealed several potential new genomic regions for further investigation. EasiGP helps us investigate the trait genetic architecture that can be utilised to benefit crop breeding. Core ideasO_LIA new computational tool, EasiGP, was created to interpret multiple genomic prediction models at the genomic level C_LIO_LIEasiGP visualises the inferred trait genetic architecture from multiple genomic prediction models with circos plots C_LIO_LIAs a case study, EasiGP highlighted several well-known genes regulating the target trait, days to anthesis C_LIO_LIEasiGP can facilitate the discovery of novel genome regions underlying target traits for further investigation C_LI

bioinformatics↗

bioRxiv2021

AnchorWave: sensitive alignment of genomes with high diversity, structural polymorphism and whole-genome duplication variation

Millions of species are currently being sequenced and their genomes are being compared. Many of them have more complex genomes than model systems and raised novel challenges for genome alignment. Widely used local alignment strategies often produce limited or incongruous results when applied to genomes with dispersed repeats, long indels, and highly diverse sequences. Moreover, alignment using many-to-many or reciprocal best hit approaches conflicts with well-studied patterns between species with different rounds of whole-genome duplication or polyploidy levels. Here we introduce AnchorWave, which performs whole-genome duplication informed collinear anchor identification between genomes and performs base-pair resolution global alignments for collinear blocks using the wavefront algorithm and a 2-piece affine gap cost strategy. This strategy enables AnchorWave to precisely identify multi-kilobase indels generated by transposable element (TE) presence/absence variants (PAVs). When aligning two maize genomes, AnchorWave successfully recalled 87% of previously reported TE PAVs between two maize lines. By contrast, other genome alignment tools showed almost zero power for TE PAV recall. AnchorWave precisely aligns up to three times more of the genome than the closest competitive approach, when comparing diverse genomes. Moreover, AnchorWave recalls transcription factor binding sites (TFBSs) at a rate of 1.05-74.85 fold higher than other tools, while with significantly lower false positive alignments. AnchorWave shows obvious improvement when applied to genomes with dispersed repeats, active transposable elements, high sequence diversity and whole-genome duplication variation. Significance statementOne fundamental analysis needed to interpret genome assemblies is genome alignment. Yet, accurately aligning regulatory and transposon regions outside of genes remains challenging. We introduce AnchorWave, which implements a genome duplication informed longest path algorithm to identify collinear regions and performs base-pair resolved, end-to-end alignment for collinear blocks using an efficient 2-piece affine gap cost strategy. AnchorWave improves alignment of partially synthetic and real genomes under a number of scenarios: genomes with high similarity, large genomes with high TE activity, genomes with many inversions, and alignments between species with deeper evolutionary divergence and different whole-genome duplication histories. Potential use cases for the method include genome comparison for evolutionary analysis of non-genic sequences and population genetics of taxa with complex genomes.

genomics↗

bioRxiv2024

PSAP-genomic-regions: a method leveraging population data to prioritize coding and non-coding variants in whole genome sequencing for rare disease diagnosis

The introduction of next generation sequencing technologies in the clinics has improved rare disease diagnosis. Nonetheless, for very heterogeneous or very rare diseases, more than half of cases still lack molecular diagnosis. Novel strategies are needed to prioritize variants within a single individual. The PSAP (Population Sampling Probability) method was developed to meet this aim but only for coding variants in exome data. To address the challenge of the analysis of non-coding variants in whole genome sequencing data, we propose an extension of the PSAP method to the non-coding genome called PSAP-genomic-regions. In this extension, instead of considering genes as testing units (PSAP-genes strategy), we use genomic regions defined over the whole genome that pinpoint potential functional constraints. We conceived an evaluation protocol for our method using artificially-generated disease exomes and genomes, by inserting coding and non-coding pathogenic ClinVar variants in large datasets of exomes and genomes from the general population. We found that PSAP-genomic-regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP-genomic-regions, more than fifty percent of non-coding ClinVar variants, especially those involved in splicing, were among the top 10 variants of the genome. In addition, our approach gave similar results compared to PSAP-genes regarding the scoring of coding variants. On real sequencing data from 6 patients with Cerebral Small Vessel Disease and 9 patients with male infertility, all causal variants were ranked in the top 100 variants with PSAP-genomic-regions. By revisiting the testing units used in the PSAP method to include non-coding variants, we have developed PSAP-genomic-regions, an efficient whole-genome prioritization tool which offers promising results for the diagnosis of unresolved rare diseases. PSAP-genomic-regions is implemented as a user-friendly Snakemake workflow, accessible to both researchers and clinicians which can easily integrate up-to-date annotation from large databases. Author summaryIn recent years, improvement in DNA sequencing technologies has allowed the identification of many genes involved in rare diseases. Nonetheless, the molecular diagnosis is still unknown for more than half of rare diseases cases. This is in part due to the large heterogeneity of molecular causes in rare diseases. This also highlights the need for the development of new methods to prioritize pathogenic variants from DNA sequencing data at the scale of the whole genome and not only coding regions. With PSAP-genomic-regions, we offer a strategy to prioritize coding and non-coding variants in whole-genome data from a single individual in need of a diagnosis. The PSAP-genomic-regions combines information on the predicted pathogenicity and frequency of variants in the context of functional regions of the genome. In this work, we compare the PSAP-genomic-regions strategy to other variant prioritization strategies on simulated and real data. We show the better performance of PSAP-genomic-regions over a classical approach based on variant pathogenicity scores alone. PSAP-genomic-regions provides a straightforward approach to prioritize causal pathogenic variants, especially non-coding ones, that are often missed with other strategies and could explain the cause of undiagnosed rare diseases.

genetics↗

bioRxiv2024

Genome-wide maps of highly-similar intrachromosomal repeats that mediate ectopic recombination in three human genome assemblies

Repeated sequences spread throughout the genome play important roles in shaping the structure of chromosomes and facilitating the generation of new genomic variation. Through a variety of mechanisms, repeats are involved in generating structural rearrangements such as deletions, duplications, inversions, and translocations, which can have the potential to impact human health. Despite their significance, repetitive regions including tandem repeats, transposable elements, segmental duplications, and low-copy repeats remain a challenge to characterize due to technological limitations inherent to many sequencing methodologies. We performed genome-wide analyses and comparisons of direct and inverted repeated sequences in the latest available human genome reference assemblies including GRCh37 and GRCh38 and the most recent telomere-to-telomere alternate assembly (T2T-CHM13). Overall, the composition and distribution of direct and inverted repeats identified remains similar among the three assemblies but we observed an increase in the number of repeated sequences detected in the T2T-CHM13 assembly versus the reference assemblies. As expected, there is an enrichment of repetitive regions in the short arms of acrocentric chromosomes, which had been previously unresolved in the human genome reference assemblies. We cross-referenced the identified repeats with protein-coding genes across the genome to identify those at risk for being involved in genomic disorders. We observed that certain gene categories, such as olfactory receptors and immune response genes, are enriched among those impacted by repeated sequences likely contributing to human diversity and adaptation. Through this analysis, we have produced a catalogue of direct and inversely oriented repeated sequences across the currently three most widely used human genome assemblies. Bioinformatic analyses of these repeats and their contribution to genome architecture can reveal regions that are most susceptible to genomic instability. Understanding how the architectural genomic features of repeat pairs such as their homology, size and distance can lead to complex genomic rearrangement formation can provide further insights into the molecular mechanisms leading to genomic disorders and genome evolution. Author summaryThis study focused on the characterization of intrachromosomal repeated sequences in the human genome that can play important roles in shaping chromosome structure and generating new genomic variation in three human genome assemblies. We observed an increase in the number of repeated sequence pairs detected in the most recent telomere-to-telomere alternate assembly (T2T-CHM13) compared to the reference assemblies (GRCh37 and GRCh38). We observed an enrichment of repeats in the T2T-CHM13 acrocentric chromosomes, which had been previously unresolved. Importantly, our study provides a catalogue of direct and inverted repeated sequences across three commonly used human genome assemblies, which can aid in the understanding of genomic architecture instability, evolution, and disorders. Our analyses provide insights into repetitive regions in the human genome that may contribute to complex genomic rearrangements

genomics↗

bioRxiv2024

Genome streamlining: effect of mutation rate and population size on genome size reduction

Genome streamlining, i.e. genome size reduction, is observed in bacteria with very different life traits, including endosymbiotic bacteria and several marine bacteria, raising the question of its evolutionary origin. None of the hypotheses proposed in the literature is firmly established, mainly due to the many confounding factors related to the diverse habitats of species with streamlined genomes. Computational models may help overcome these difficulties and rigorously test hypotheses. In this work, we used Aevol, a platform designed to study the evolution of genome architecture, to test two main hypotheses: that an increase in population size (N) or mutation rate ({micro}) could cause genome reduction. In our experiments, both conditions lead to streamlining, but with very different resulting genome structures. Under increased population sizes, genomes loose a significant fraction of non-coding sequences, but maintain their coding size, resulting in densely packed genomes (akin to streamlined marine bacteria genomes). By contrast, under increased mutation rate, genomes loose both coding and non-coding sequences (akin to endosymbiotic bacteria genomes). Hence, both factors lead to an overall reduction in genome size, but the coding density of the genome appears to be determined by N x {micro}. Thus, a broad range of genome size and density can be achieved by different combinations of N and {micro}. Our results suggest that genome size and coding density are determined by the interplay between selection for phenotypic adaptation and selection for robustness. Significance statementMany bacterial species show reduced genomes. However, the diversity of these species and of their life traits makes it difficult to identify the mechanisms that led to this reduction. Indeed, no unifying hypothesis accounts for the whole diversity of genome size reduction. Here, we used simulations to systematically explore the effect of population size and mutation rate on genome size. We show that the interaction between these two factors tightly determine the size, but also the density of genomes, making it possible to account for the whole diversity of reduced genomes by acting on these two parameters only. Our results suggest a theoretical model in which genome reduction is driven by a robustness/fitness trade off.

evolutionary biology↗

bioRxiv2026

Widespread Genomic Islands in Giant Viruses Shape Genome Plasticity and Mosaicism

Giant viruses in the phylum Nucleocytoviricota possess exceptionally large and mosaic genomes, yet the mechanisms underlying their remarkable genomic plasticity remain poorly understood. Genomic islands are large dynamic genomic regions that are major drivers of genome diversification and adaptation in bacteria. However, their contribution to genome evolution in giant viruses remains largely unexplored. Here, we systematically characterize the genomic island landscape of giant viruses using 369 high-quality genomes spanning cultured isolates and long-read metagenome-assembled genomes. We identify 307 genomic islands across >50% of the genomes, demonstrating that these regions are pervasive across Nucleocytoviricota diversity. Giant virus genomic islands are frequently associated with genomic hypervariability and enriched in genes involved in host interaction, particularly surface adhesion proteins, suggesting key roles in host adaptation during virus-host arms race. Comparative analyses further reveal these islands to be major hotspots of genome diversification, exhibiting frequent gain, loss, and rearrangement even among highly similar genomes, with evidence that entire island regions can be exchanged among closely related viral populations. Notably, 37% of genomic islands are enriched in bacterial homologs, and several exhibit striking synteny with genomic regions recovered from environmentally co-occurring bacterial genomes, supporting large-scale genetic exchange between bacteria and giant viruses. Together, these findings identify genomic islands as pervasive and dynamic drivers of giant virus genome evolution, providing a mechanistic framework for genome plasticity, mosaicism, and adaptive potential of giant viruses.

ecology↗

bioRxiv2024

Hybrid Genome Sequence of Cryptococcus neoformans of Indian origin and comparative genome analysis

ObjectivesThe Indian isolate of Cryptococcus neoformans underwent complete genome sequencing to elucidate its genomic architecture and functional characteristics. Furthermore, this study aimed to comprehensively characterize the virulence factors (virulome), antibiotic resistance genes (resistome), and the pan-genome of C. neoformans spp. through a comparative genomic analysis, providing insights into the genetic diversity and evolutionary relationships among strains. MethodsThe genomic data of a clinical strain of C. neoformans was assembled and annotated by MaSuRCA5 and Braker tool. Along with this, the assembled genomic data of the 11 strains were retrieved from NCBI datasets. The comparative virulome, resistome, phylogeny and of the 12 C. neoformans genomes were analyzed using DFVF, AFRbase, BLAST, CLUSTAL Omega, MEGAX, and Orthovenn3, respectively. ResultsThe sequenced isolate was identified as a member of the Cryptococcus neoformans var. grubii subspecies. Notably, virulence-related genes comprise approximately 4.8% of the total genome. A comparative genomic analysis of 12 study genomes revealed variations in virulence patterns, including differences in melanization, immune evasion, blood-brain barrier evasion, transcriptional regulation, and oxidative stress response. The phylogenetic study using MLST and orthologous clusters categorized the subspecies grubii and neoformans in different clades. Pan-genome analysis showed that 73.6% of orthologous gene clusters and 77.72% of orthologous proteins were conserved across all 12 study genomes, indicating a shared core genome. Furthermore, the evolutionary relatedness study of the pan-genome revealed gene expansion and contraction events among the study strains. ConclusionThis pioneering study presents the first comprehensive genomic and comparative genomic analysis of Cryptococcus sp., incorporating data on virulence genes, antibiotic resistance, and pan-genome dynamics. Key findings reveal that strains Cn, H99, and JEC21 harbor crucial virulence genes associated with infection severity. While all study strains possess genes promoting antifungal resistance (AFR), most lack specific single nucleotide polymorphisms defining AFR. Consistent with pan-genome analysis, our results show significant gene expansion and contraction events in these strains. This study underscores the importance of bioinformatic tools for efficient whole-genome analysis and large-scale comparative genomics research.

genomics↗

bioRxiv2021

Recovery of high-quality assembled genomes via single-cell genome-guided binning of metagenome assembly

BackgroundObtaining high-quality (HQ) reference genomes from microbial communities is crucial for understanding the phylogeny and function of uncultured microbes in complex microbial ecosystems. Despite the improved bioinformatic approaches to generate curated metagenome-assembled genomes (MAGs), existing metagenomic binners often fail to obtain reliable MAGs, and thus, they are nowhere comparable to genomes sequenced from isolates in terms of strain level resolution. Here, we present a single-cell genome-guided metagenome binning (MetaSAG) to reconstruct the strain-resolved genomes from microbial communities at once. ResultsMetaSAG employs single-cell amplified genomes (SAGs) generated with microfluidic technology as binning guides to recover improved draft genomes with the metagenomic data. To assess the performance of reconstructing genomes from various microbial communities, we compared MetaSAG with four conventional metagenomic binners using a cell mock community, human gut microbiota, and skin microbiota samples. MetaSAG showed precise contig binning and higher recovery rates (>97%) of rRNA and plasmids compared to conventional binners in genome reconstruction from the cell mock community. In human microbiota samples, MetaSAG recovered the largest number of genomes with a total of 103 gut microbial genomes (21 HQ and 65 showed >90% completeness) and 45 skin microbial genomes (10 HQ and 40 showed >90% completeness), respectively. Conventional binners recovered one Staphylococcus hominis genome, whereas MetaSAG recovered two S. hominis genomes from the identical skin microbiota sample. Single-cell sequencing indicated that these S. hominis genomes clearly derived from two distinct strains harboring specifically different plasmids. We found that all conventional S. hominis MAGs had substantial lack or excess of the genome sequences and contamination of other Staphylococcus bacteria (S. epidermidis). ConclusionsMetaSAG enabled us to obtain the strain-resolved genomes in the mock community and human microbiota samples by assigning metagenomic sequences correctly and covering both highly conserved genes such as rRNA genes and unique extrachromosomal elements, including plasmids. MetaSAG will provide HQ genomes that are difficult to obtain with metagenomic analyses alone and will facilitate the understanding of microbial ecosystems by elucidating detailed metabolic pathways and horizontal gene transfer networks through microbial genomes. MetaSAG is available at https://github.com/kojiari/metasag.

microbiology↗

bioRxiv2023

Giants among Cnidaria: large nuclear genomes and rearranged mitochondrial genomes in siphonophores

Siphonophores (Cnidaria:Hydrozoa) are abundant predators found throughout the ocean and are important components in worldwide zooplankton. They range in length from a few centimeters to tens of meters. They are gelatinous, fragile, and difficult to collect, so many aspects of the biology of these 190 species remain poorly understood. To survey siphonophore genome diversity, we performed Illumina sequencing of 32 species sampled broadly across the phylogeny. Sequencing depth was sufficient to estimate nuclear genome size from k-mer spectra in 8 specimens, ranging from 0.7-4.8Gb. In 6 specimens we got heterozygosity estimates between 0.7-5.3%. Rarefaction analyses indicate k-mer peaks can be absent with as much as 30x read coverage, suggesting minimum genome sizes range from 1.0-3.8Gb in the remaining 27 samples without k-mer peaks. This work confirms most siphonophore nuclear genomes are large, but also identifies several with reduced size that are tractable targets for future siphonophore nuclear genome assembly projects. We also assembled mitochondrial genomes for 32 specimens from these new data, indicating a conserved gene order among Hydrozoa, Cystonectae and some Physonectae, also revealing the ancestral gene organization of siphonophores. There then was extensive rearrangement of mitochondrial genomes within other physonects and in Calycophorae, including the repeated loss of atp8. Though siphonophores comprise a small fraction of cnidarian species, this survey greatly expands our understanding of cnidarian genome diversity. This study further illustrates both the importance of deep phylogenetic sampling and the utility of Illumina genome skimming in understanding genomic diversity of a clade. SignificanceDescriptions of basic genome features, such as nuclear genome size and mitochondrial genome sequences, remain sparse across many clades in the tree of life, leading to over generalizations from very small sample sizes and often limiting selection of optimal species for genome assembly efforts. Here we use Illumina genome skimming to assess a variety of genome features across 35 siphonophores (Cnidaria). This deep dive within a single clade identifies six species that are optimal candidates of future genomic work, and reveals greater range in nuclear genome size and diversity of mitochondrial genome orders within siphonophores than had been described across all Cnidaria.

evolutionary biology↗

bioRxiv2023

Genome assembly of the dyeing poison frog provides insights into the dynamics of transposable element and genome-size evolution

Genome size varies greatly across the tree of life and transposable elements are an important contributor to this variation. Among vertebrates, amphibians display the greatest variation in genome size, making them ideal models to explore the causes and consequences of genome size variation. However, high-quality genome assemblies for amphibians have, until recently, been rare. Here, we generate a high-quality genome assembly for the dyeing poison frog, Dendrobates tinctorius. We compare this assembly to publicly-available frog genomes and find evidence for both large-scale conserved synteny and widespread rearrangements between frog lineages. Comparing conserved orthologs annotated in these genomes revealed a strong correlation between genome size and gene size. To explore the cause of gene-size variation, we quantified the location of transposable elements relative to gene features and find that the accumulation of transposable elements in introns has played an important role in the evolution of gene size in D. tinctorius, while estimates of insertion times suggest that many insertion events are recent and species-specific. Finally, we show that the diversity and abundance of transposable elements in poison frog genomes can complicate genotyping efforts that rely on repetitive elements as sequence anchors. Our results show that transposable elements have clearly played an important role in the evolution of large genome size in D. tinctorius. Future studies are needed to fully understand the dynamics of transposable element evolution and to optimise primer or bait design for cost-effective population-level genotyping in species with large, repetitive genomes. SignificanceAmphibians display more variation in genome size than any other vertebrate lineage. Complexities associated with large genomes frequently hamper genome assembly and population genetic studies. Here we use long-read HiFi sequences to generate a high-quality 6.3 Gbp genome assembly of the poison frog Dendrobates tinctorius. We use this genome and leverage comparative genomics and de novo annotations to quantify aspects of genome evolution driven by repetitive transposable genetic elements. Our results provide support for the dynamic role that transposable elements play in driving the evolution of "genomic gigantism" in amphibians. We also show how transposable elements can be leveraged for cost-efficient population genetic studies using limited input material.

genetics↗

bioRxiv2025

Genome-wide Pervasiveness and Localized Variation of k-mer-based Genomic Signatures in Eukaryotes

Genomic signatures are taxon-specific patterns in nucleotide sequence composition observed across different regions of a genome, used in the taxonomic classification of organisms and in inferring their evolutionary relationships. However, the nature and extent of the pervasiveness of a genomic signature across the expanse of a Telomere-to-Telomere (T2T) assembly, especially across the functionally diverse sequence elements and highly repetitive regions, remain counterintuitive and underexplored. This study aims to bridge this knowledge gap by systematically investigating the pervasiveness and variation of the genomic signature across the human genome and the genome of each of three other eukaryotic species from different kingdoms. Using the alignment-free k-mer-based Frequency Chaos Game Representation (FCGR) of DNA sequences, this study qualitatively and quantitatively analyzes the variations of the genomic signature along an entire genome. Qualitative analysis is first performed through visual inspection of FCGR patterns across different chromosomes of a species. In parallel, a quantitative analysis evaluates the variation of the genomic signature within a genome by comparing eight distance measures to identify the optimal one for the datasets in this study. By taking an intragenomic perspective with detailed analysis of chromosome landscapes these analyses reveal that, while the genomic signature is preserved in most genomic regions, exceptions exist in localized regions, such as tandem repetitions of short and long repeat units. Upon determining this pervasiveness, we assemble novel pipelines aimed at selecting a short contiguous representative genomic segment that encapsulates the sequence composition patterns characteristic of the entire genome. These representative segments are then used to assess intragenomic variation of the genomic signature, demonstrating that only a small proportion of segments (namely those characterized by regional density of short and long tandem repeats) show high distance values from the representative. No-tably, in the human genome, 80% of the segments have a distance of less than 0.24 (on a [0,1] DSSIM scale) from the representative. Moreover, we demonstrate that using these representative segments improves down-stream tasks, e.g., increasing one-nearest-neighbor (1-NN) taxonomic classification accuracy by 7% compared to selecting a random genomic segment to serve as a proxy of the genome. Lastly, this study presents a special-purpose graphical user interface (GUI) software tool, CGR-Diff, designed to provide both visual and quantitative comparisons of FCGRs of sample or user-provided DNA sequences, thereby facilitating intragenomic variation analysis of genomic signature within and across species.

bioinformatics↗

bioRxiv2026

Genomic-island cassette architecture drives pathogenic Enterococcus cecorum lineages: Cassette2Vec-EC, a structural genomics and machine-learning framework

Mobile genetic elements and genomic islands (GIs) frequently encode antibiotic resistance and host-adaptation cargo, yet routine genome comparison pipelines often miss the higher-order organization of how genes co-occur as transferable, GI-anchored modules. We present Cassette2Vec-EC, a structural genomics framework that converts annotated genomes into cassette units (local gene neighborhoods with GI context), encodes each cassette as a fixed-length feature vector, and applies genome-grouped machine learning to predict pathogenic lineages while preventing within-genome leakage. Using a curated Enterococcus cecorum cohort from poultry production systems, we integrate pangenome context, GI calls, mobility markers, and AMR/virulence annotations into cassette-level features and evaluate models strictly under GroupKFold-by-genome. Cassette2Vec-EC achieves strong genome-level generalization (AUROC 0.975 {+/-} 0.030, average precision 0.938 {+/-} 0.077, Brier score 0.056 {+/-} 0.058). When evaluated at the cassette unit level under the same genome-grouped protocol, performance remains high (AUROC 0.974 {+/-} 0.029, AP 0.919 {+/-} 0.093, Brier 0.057 {+/-} 0.057), supporting that cassette representations capture transferable signal rather than genome identity. Baselines show that GI burden alone can partially rank genomes but yields poorer calibration and limited interpretability. By combining comparative genomics with cassette-aware features and providing locus-level explanations (SHAP) that map predictions to specific GI-associated modules, Cassette2Vec-EC provides a practical blueprint for genomic-island-aware pathogen surveillance, junction-based diagnostics, and targeted monitoring of high-risk lineages. ImportancePathogenic Enterococcus cecorum lineages threaten poultry health and production, yet many genomic surveillance workflows reduce genomes to gene presence-absence lists and overlook how mobile elements are organized into transferable modules. Cassette2Vec-EC addresses this gap by representing genomic islands as GI-anchored cassette neighborhoods and encoding each cassette as a fixed-length feature vector linking cassette architecture to pathogenic risk. Under a strict genome-grouped evaluation protocol, the framework predicts pathogenic lineages while preventing within-genome leakage and supports calibrated probability estimates in the full model. Cassette level explanations (SHAP) localize predictive signal to specific GI-anchored modules, yielding tractable targets for surveillance prioritization and junction-based diagnostics. Although developed for poultry-associated E. cecorum, this cassette and GI-aware representation is transferable to other bacterial pathogens to strengthen genomic surveillance and help partners identify high-risk isolates before clinical disease emerges.

bioinformatics↗

bioRxiv2026

Near-Gapless and Haplotype-Resolved Capsella Genomes Enable Investigation into Genomic Consequences of Mating System Shifts

The shift from outcrossing to self-fertilization is a common evolutionary transition in flowering plants. The genus Capsella, comprising the obligate outcrosser C. grandiflora and two self-fertile species, C. rubella and C. orientalis, provides a powerful system to explore genomic consequences of mating system shifts. Despite its utility, existing genomic resources in Capsella are fragmented, incomplete, and particularly deficient in repetitive genomic regions, hindering the study of transposable element (TE) dynamics and gene annotation. Here, we present high-quality, chromosome-scale, near-gapless genome assemblies for C. grandiflora, C. rubella, and C. orientalis. Leveraging these improved genomes, we created high-quality genomic resources for the Capsella genus by performing comprehensive, de novo annotations of protein-coding genes and TEs. Comparative genomic analysis among these species reveals differences in TE abundance, position, and production of small RNAs. These resources provide an unprecedented opportunity to explore how mating system transitions influence genome architecture, TE behavior, and gene evolution. This research also developed a static online platform for Capsella genomic resources, Capsella Database (CapBase, www.capsella.uk), to support community use of these resources. Our findings advance understanding of the genomic impacts of selfing and establish a robust foundation for future research into genomics, epigenomics, and evolutionary biology within Capsella and related plant systems. Significance statementThe Capsella species are model plants for investigating the genomic impacts of the shift from outcrossing to self-fertilization. However, genomic resources for these species remain poor. In this study, we created high-quality genome assemblies and annotations for three Capsella species, performed comparative genomic analysis among these genomes, and constructed the first Capsella online genomic database, CapBase (www.capsella.uk). These outputs provide a valuable foundation for studying plant genome dynamics following the evolution of self-fertilization.

plant biology↗

bioRxiv2021

iLoci: Robust evaluation of genome content and organization for provisional and mature genome assemblies

BackgroundThe rate at which new draft genome assemblies and corresponding annotation versions are being produced has long outpaced the scientific communitys capacity to refine these drafts into "finished," reference-quality data resources to a standard typically expected from dedicated efforts of model organism research communities. Nonetheless, scientists must be able to evaluate newly sequenced genomes in the context of previously published data, requiring summaries of genome content and organization that can be quickly computed, updated, and meaningfully compared. As annotation quality will necessarily vary within and across data sets, the ability to select subsets of only those data that are well supported is critical for distinguishing technical artifacts from biological effects in genome-wide analyses. ResultsWe introduce a new framework for genome analyses based on parsing an annotated genome assembly into distinct interval loci (iLoci), available as open source software as part of the AEGeAn Toolkit (https://github.com/BrendelGroup/AEGeAn). We demonstrate that iLoci provide an alternative coordinate system that is robust to changes in assembly and annotation versions and facilitates granular quality control of genome data. We discuss how statistics computed on iLoci reflect various characteristics of genome content and organization and illustrate how these statistics can be used to establish a baseline for assessment of the completeness and accuracy of the data. We also introduce a well-defined measure of relative genome compactness and compute other iLocus statistics that reveal genome-wide characteristics of gene arrangements in the whole genome context. ConclusionsWe present a coherent computational framework that calculates informative statistics from genome assembly/annotation data input. Given the fast pace of assembly/annotation updates, our AEGeAn Toolkit fills a niche in computational genomics based on deriving persistent and species-specific genome statistics. Gene structure model centric iLoci provide a precisely defined coordinate system that can be used to store assembly/annotation updates that reflect either stable or changed assessments. Large-scale application of the approach revealed species and clade specific genome organization in precisely defined computational terms, promising intriguing forays into the forces of shaping genome structure as more and more genome assemblies are being deposited.

genomics↗

bioRxiv2021

Characterising genome architectures using Genome Decomposition Analysis

Genome architecture describes how genes and other features are arranged in genomes. These arrangements reflect the evolutionary pressures on genomes and underlie biological processes such as chromosomal segregation and the regulation of gene expression. We present a new tool called Genome Decomposition Analysis (GDA) that characterises genome architectures and acts as an accessible approach for discovering hidden features of a genome assembly. With the imminent deluge of high quality genome assemblies from projects such as the Darwin Tree of Life and the Earth BioGenome Project, GDA has been designed to facilitate their exploration and the discovery of novel genome biology. We highlight the effectiveness of our approach in characterising the genome architectures of single-celled eukaryotic parasites from the phylum Apicomplexa and show that it scales well to large genomes. SignificanceGenome sequencing has revealed that there are functionally important arrangements of genes, repetitive elements and regulatory sequences within chromosomes. Identifying these arrangements requires extensive computation and analysis. Furthermore, improvements in genome sequencing technology and the establishment of consortia aiming to sequence all species of eukaryotes mean that there is a need for high throughput methods for discovering new genome biology. Here we present a software pipeline, named GDA, which determines the patterns of genomic features across chromosomes and uses these to characterise genome architecture. We show that it recapitulates the known genome architecture of several Apicomplexan parasites and use it to identify features in a recently sequenced, less well-characterised genome. GDA scales well to large genomes and is freely available.

genomics↗

bioRxiv2025

From macro to micro: De novo genomes of Aedes mosquitoes enable comparative genomics among close and distant relatives

The yellow fever mosquito (Aedes aegypti) is an organism of high medical importance because it is the primary vector for diseases such as yellow fever, Zika, dengue, and chikungunya. Its medical importance has made it a subject of numerous efforts to understand their biology. One such effort, was the development of a high-quality reference genome (AaegL5). However, this reference genome was sourced from a highly inbred laboratory strain with unknown geographic origin. Thus, the reference is not representative of a wild mosquito, let alone one from its native range in sub-Saharan Africa. To better understand the genetic architecture of Ae. aegypti and their sister species, we developed two de novo chromosome-scale genomes with sequences sourced from single individuals: one of Ae. aegypti formosus (Aaf) from Burkina Faso and one of Ae. mascarensis (Am) from Mauritius. Both genomes exhibit high contiguity and gene completeness, comparable to AaegL5. While Aaf exhibits high degree of synteny to AaegL5, it also exhibits several large inversions. We further conducted comparative genomic analyses using our genomes and other publicly available culicid reference genomes to find extensive chromosomal rearrangements between major lineages. Overrepresentation analysis of expanded genes in Aaf, AaegL5, and Am revealed that while the overarching category of genes that have expanded are similar, the specific genes that have expanded differ. Our findings elucidate novel insights into chromosome evolution at both microevolutionary and macroevolutionary scales. The genomic resources we present are additions to the arsenal of biologists in understanding mosquito biology and genome evolution. SignificanceAedes aegypti is a major arboviral disease vector found throughout the tropics and sub-tropics. Its subspecies differ ecologically, as native sub-Saharan African form feeds on mammals generally and inhabit both sylvatic and domestic areas and the global invasive form preferentially feeds on humans and lives primarily domestic areas. Their medical importance has prompted the development of a high-quality reference genome, but it was sourced from an inbred laboratory strain of unknown origin. Here, we leveraged PacBio HiFi sequencing and HiC sequencing to develop the first de novo genome of Ae. aegypti sampled its native range in Burkina Faso. We also present a de novo genome of Ae. mascarensis, its sister species. Our genomes are comparably contiguous and complete to the reference genome. Comparative genomic analysis using our genomes and other culicid reference genomes reveal extensive chromosomal rearrangements.

genomics↗

bioRxiv2019

Benchmarking bacterial genome-wide association study (GWAS) methods using simulated genomes and phenotypes

Genome Wide Association Studies (GWASs) have the potential to reveal the genetics of microbial phenotypes such as antibiotic resistance and virulence. Capitalizing on the growing wealth of bacterial sequence data, microbial GWAS methods aim to identify causal genetic variants while ignoring spurious associations. Bacteria reproduce clonally, leading to strong population structure and genome-wide linkage, making it challenging to separate true \"hits\" (i.e. mutations that cause a phenotype) from non-causal linked mutations. GWAS methods attempt to correct for population structure in different ways, but their performance has not yet been systematically evaluated. Here we developed a bacterial GWAS simulator (BacGWASim) to generate bacterial genomes with varying rates of mutation, recombination, and other evolutionary parameters, along with a subset of causal mutations underlying a phenotype of interest. We assessed the performance (recall and precision) of three widely-used univariate GWAS approaches (cluster-based, dimensionality-reduction, and linear mixed models, implemented in PLINK, pySEER, and GEMMA) and one relatively new whole-genome elastic net model implemented in pySEER, across a range of simulated sample sizes, recombination rates, and causal mutation effect sizes. As expected, all methods performed better with larger sample sizes and effect sizes. The performance of clustering and dimensionality reduction approaches to correct for population structure were considerably variable according to the choice of parameters. Notably, the elastic net whole-genome model was consistently amongst the highest-performing methods and had the highest power in detecting causal variants with both low and high effect sizes. Most methods reached good performance (Recall > 0.75) to identify causal mutations of strong effect size (log Odds Ratio >= 2) with a sample size of 2000 genomes. However, only elastic nets reached reasonable performance (Recall = 0.35) for detecting markers with weaker effects (log OR [~]1) in smaller samples. Elastic nets also showed superior precision and recall in controlling for genome-wide linkage, relative to univariate models. However, all methods performed relatively poorly on highly clonal (low-recombining) genomes, suggesting room for improvement in method development. These findings show the potential for whole-genome models to improve bacterial GWAS performance. BacGWASim code and simulated data are publicly available to enable further comparisons and benchmarking of new methods.\n\nAuthor summaryMicrobial populations contain measurable phenotypic differences with important clinical and environmental consequences, such as antibiotic resistance, virulence, host preference and transmissibility. A major challenge is to discover the genes and mutations in bacterial genomes that control these phenotypes. Bacterial Genome-Wide Association Studies (GWASs) are family of methods to statistically associate phenotypes with genotypes, such as point mutations and other variants across the genome. However, compared to sexual organisms such as humans, bacteria reproduce clonally meaning that causal mutations tend to be strongly linked to other mutations on the same chromosome. This genome-wide linkage makes it challenging to statistically separate causal mutations from non-causal false-positive associations. Several GWAS methods are currently available, but it is not clear which is the most powerful and accurate for bacteria. To systematically evaluate these methods, we developed BacGWASim, a computational pipeline to simulate the evolution of bacterial genomes and phenotypes. Using simulated genomes, we found that GWAS methods varied widely in their performance. In general, causal mutations of strong effect (e.g. those under strong selection for antibiotic resistance) could be easily identified with relatively small samples sizes of around 1000 genomes, but more complex phenotypes controlled by mutations of weaker effect required 3000 genomes or more. We found that a recently-developed GWAS method called elastic net was particularly good at identifying causal mutations in highly clonal populations, with strong linkage between mutations - but there is still room for improvement. The BacGWASim computer code is publicly available to enable further comparisons and benchmarking of new methods.

genomics↗

bioRxiv2020

Genomic rearrangements uncovered by genome-wide co-evolution analysis of a major nosocomial pathogen Enterococcus faecium

Enterococcus faecium is a gut commensal of the gastro-digestive tract, but also known as nosocomial pathogen among hospitalized patients. Population genetics based on whole-genome sequencing has revealed that E. faecium strains from hospitalized patients form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here we further explored the adaptive evolution of E. faecium using a genome-wide co-evolution study (GWES) to identify co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are not proximal to each other based on the completely assembled chromosome of clade A1 reference hospital isolate AUS0004. Close examination of these regions revealed that they are located at the borders of four different types of large-scale genomic rearrangements, insertion sites of two different genomic islands and an IS30-like transposon. In non-clade A1 isolates, these regions are adjacent to each other and they lack the insertions of the genomic islands and IS30-like transposon. Additionally, among the clade A1 isolates there is one group of pet isolates lacking the genomic rearrangement and insertion of the genomic islands, suggesting a distinct evolutionary trajectory. In silico analysis of the biological functions of the genes encoded in three regions revealed a common link to a stress response. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. In conclusion, to our knowledge, this is the first study using GWES to identify genomic rearrangements, suggesting that there is considerable untapped potential to unravel hidden evolutionary signals from population genomic data. Impact statementEnterococcus faecium has emerged as an important nosocomial pathogen around the world. Population genetics revealed that clinical E. faecium strains form a distinct clade, designated as clade A1 and that plasmids are major contributors to the emergence of nosocomial E. faecium. Here, the adaptive evolution of E. faecium was further explored using an unsupervised machine learning method (SuperDCA) to identify genome-wide co-evolving SNPs. We identified three genomic regions harboring large numbers of SNPs in tight linkage which are separated by a large chromosomal distance in a clinical clade A1 reference isolate, but appeared adjacent to each other in non-clade A1 isolates. We identified four different types of large-scale genomic rearrangements and in all cases, we found insertion of two different genomic islands and an insertion element at the border. In contrast, no genomic rearrangement and insertions were identified among a group of clade A1 pet isolates, suggesting a distinct evolutionary trajectory. Based on the in silico predicted biological functions, we found a common link to a stress response for the genes encoded in three regions. This suggests that these rearrangements may reflect adaptation to the stringent conditions in the hospital environment, such as antibiotics and detergents, to which bacteria are exposed. Data summaryRaw core-genome alignment (1.1 MB, Harvest suite v1.1.2), including the 1,644 Clade A isolates and the complete E. faecium AUS0004 (accession number CP003351) as a reference is available under the following gitlab repository https://gitlab.com/sirarredondo/efm_gwes.

genomics↗