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bioRxiv2017

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

Genetic connectedness refers to a measure of genetic relatedness across management units (e.g., herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attention has been paid to using genomic information to measure connectedness. In this study, we assessed genome-based connectedness across management units by applying prediction error variance of difference (PEVD), coefficient of determination (CD), and prediction error correlation (r) to a combination of computer simulation and real data (mice and cattle). We found that genomic information (G) increased the estimate of connectedness among individuals from different management units compared to that based on pedigree (A). A disconnected design benefited the most. In both datasets, PEVD and CD statistics inferred increased connectedness across units when using G- rather than A-based relatedness suggesting stronger connectedness. With r once using allele frequencies equal to one-half or scaling G to values between 0 and 2, which is intrinsic to A, connectedness also increased with genomic information. However, PEVD occasionally increased, and r decreased when obtained using the alternative form of G, instead suggesting less connectedness. Such inconsistencies were not found with CD. We contend that genomic relatedness strengthens measures of genetic connectedness across units and has the potential to aid genomic evaluation of livestock species.\n\nThe problem of connectedness or disconnectedness is particularly important in genetic evaluation of managed populations such as domesticated livestock. When selecting among animals from different management units (e.g., herds and flocks), caution is needed; choosing one animal over others across management units may be associated with greater uncertainty than selection within management units. Such uncertainty is reduced if individuals from different management units are genetically linked or connected. In such a case, best linear unbiased prediction (BLUP) offers meaningful comparison of the breeding values across management units for genetic evaluation (e.g., Kuehn et al., 2007).\n\nStructures of breeding programs have a direct influence on levels of connectedness. Wide use of artificial insemination (AI) programs generally increases genetic connectedness across management units. For example, dairy cattle populations are considered highly connected due to dissemination of genetic material from a small number of highly selected sires. The situation may be different for species with less use of AI and more use of natural service mating such as for beef cattle or sheep populations. Under these scenarios, the magnitude of connectedness across management units is reduced and genetic links are largely confined within management units.\n\nPedigree-based genetic connectedness has been evaluated and applied in practice (e.g., Kuehn et al., 2009; Eikje and Lewis, 2015). However, there is a relative paucity of use of genomic information such as single nucletide polymorphisms (SNPs) to ascertain connectedness. It still remains elusive in what scenarios genomics can strengthen connectedness and how much gain can be expected relative to use of pedigree information alone. Connectedness statistics have been used to optimize selective genotyping and phenotyping in simulated livestock (Pszczola et al., 2012) and plant populations (Maenhout et al., 2010), and in real maize (Rincent et al., 2012; Isidro et al., 2015), and real rice data (Isidro et al., 2015). These studies concluded that the greater the connectedness between the reference and validation populations, the greater the predictive performance. However, 1) connectedness among different management units and 2) differences in connectedness measures between pedigree and genomic relatedness were not explored in those studies. For better understanding of genome-based connectedness, it is critical to examine how the presence of management units comes into play. For instance, genomic relatedness provides relationships between distant individuals that appear disconnected according to the pedigree information. In addition, it captures Mendelian sampling that is not present in pedigree relationships (Hill and Weir, 2011). Thus, genomic information is expected to strengthen measures of connectedness, which in turn refines comparisons of genetic values across different management units. The objective of this study was to assess measures of genetic connectedness across management units with use of genomic information. We leveraged the combination of real data and computer simulation to compare gains in measures of connectedness when moving from pedigree to genomic relationships. First, we studied a heterogenous mice dataset stratified by cage. Then we investigated approaches to measure connectedness using real cattle data coupled with simulated management units to have greater control over the degree of confounding between fixed management groups and genetic relationships.

genetics↗
bioRxiv2017

Human demographic history has amplified the effects background selection across the genome

Natural populations often grow, shrink, and migrate over time. Demographic processes such as these can impact genome-wide levels of genetic diversity. In addition, genetic variation in functional regions of the genome can be altered by natural selection, which drives adaptive mutations to higher frequencies or purges deleterious ones. Such selective processes impact not only the sites directly under selection but also nearby neutral variation through genetic linkage through processes referred to as genetic hitch-hiking in the context of positive selection and background selection (BGS) in the context of purifying selection. While there is extensive literature examining the impact of selection at linked sites at demographic equilibrium, less is known about how non-equilibrium demographic processes impact the effects of hitchhiking and BGS. Utilizing a global sample of human whole-genome sequences from the Thousand Genomes Project and extensive simulations, we investigate how non-equilibrium demographic processes magnify and dampen the consequences of selection at linked sites across the human genome. When binning the genome by inferred strength of BGS, we observe that, compared to Africans, non-African populations have experienced larger proportional decreases in neutral genetic diversity in such regions. We replicate these findings in admixed populations by showing that non-African ancestral components of the genome have also been impacted more severely in these regions. We attribute these differences to the strong, sustained/recurrent population bottlenecks that non-Africans experienced as they migrated out of Africa and throughout the globe. Furthermore, we observe a strong correlation between FST and inferred strength of BGS, suggesting a stronger rate of genetic drift. Forward simulations of human demographic history with a model of BGS support these observations. Our results show that non-equilibrium demography significantly alters the consequences selection at linked sites and support the need for more work investigating the dynamic process of multiple evolutionary forces operating in concert.\n\nAuthor summaryPatterns of genetic diversity within a species are affected at broad and fine scales by population size changes (\"demography\") and natural selection. From both population genetics theory and observation of genomic sequence data, it is known that demography can alter genome-wide average neutral genetic diversity. Additionally, natural selection can affect neutral genetic diversity regionally across the genome via selection at linked sites. During this process, natural selection acting on adaptive or deleterious variants in the genome will also impact diversity at nearby neutral sites due to genetic linkage. However, less is well known about the dynamic changes to diversity that occur in regions impacted by selection at linked sites when a population undergoes a size change. We characterize these dynamic changes using thousands of human genomes and find that the population size changes experienced by humans have shaped the consequences of linked selection across the genome. In particular, population contractions, such as those experienced by non-Africans, have disproportionately decreased neutral diversity in regions of the genome inferred to be under strong background selection (i.e., selection at linked sites that is caused by natural selection acting on deleterious variants), resulting in large differences between African and non-African populations.

evolutionary biology↗
bioRxiv2020

Nucleotide-resolution bacterial pan-genomics with reference graphs

BackgroundBacterial genomes follow a U-shaped frequency distribution whereby most genomic loci are either rare (accessory) or common (core); the union of these is the pan-genome. The alignable fraction of two genomes from a single species can be low (e.g. 50-70%), such that no single reference genome can access all single nucleotide polymorphisms (SNPs). The pragmatic solution is to choose a close reference, and analyse SNPs only in the core genome. Given much bacterial adaptability hinges on the accessory genome, this is an unsatisfactory limitation. ResultsWe present a novel pan-genome graph structure and algorithms implemented in the software pandora, which approximates a sequenced genome as a recombinant of reference genomes, detects novel variation and then pan-genotypes multiple samples. The method takes fastq as input and outputs a multi-sample VCF with respect to an inferred data-dependent reference genome, and is available at https://github.com/rmcolq/pandora. Constructing a reference graph from 578 E. coli genomes, we analyse a diverse set of 20 E. coli isolates. We show pandora recovers at least 13k more rare SNPs than single-reference based tools, achieves equal or better error rates with Nanopore as with Illumina data, 6-24x lower Nanopore error rates than other tools, and provides a stable framework for analysing diverse samples without reference bias. We also show that our inferred recombinant VCF reference genome is significantly better than simply picking the closest RefSeq reference. ConclusionsThis is a step towards comprehensive cohort analysis of bacterial pan-genomic variation, with potential impacts on genotype/phenotype and epidemiological studies.

bioinformatics↗
bioRxiv2021

Measuring the invisible - The sequences causal of genome size differences in eyebrights (Euphrasia) revealed by k-mers

Genome size variation within plant (and other) taxa may be due to presence/absence variation in low-copy sequences or copy number variation in genomic repeats of various frequency classes. However, identifying the sequences underpinning genome size variation has been challenging because genome assemblies commonly contain collapsed representations of repetitive sequences and because genome skimming studies miss low-copy number sequences. Here, we take a novel approach based on k-mers, short sub-sequences of equal length k, generated from whole genome sequencing data of diploid eyebrights (Euphrasia), a group of plants which have considerable genome size variation within a ploidy level. We compare k-mer inventories within and between closely related species, and quantify the contribution of different copy number classes to genome size differences. We further assign high-copy number k-mers to specific repeat types as retrieved from the RepeatExplorer2 pipeline. We find complex patterns of k-mer differences between samples. While all copy number classes contributed to genome size variation, the largest contribution came from repeats with 1000-10,000 genomic copies including the 45S rDNA satellite DNA and, unexpectedly, a repeat associated with an Angela transposable element. We also find size differences in the low-copy number class, likely indicating differences in gene space between our samples. In this study, we demonstrate that it is possible to pinpoint the sequences causing genome size variation within species without use of a reference genome. Such sequences can serve as targets for future cytogenetic studies. We also show that studies of genome size variation should go beyond repeats and consider the whole genome. To allow future work with other taxonomic groups, we share our analysis pipeline, which is straightforward to run, relying largely on standard GNU command line tools.

evolutionary biology↗
bioRxiv2022

Experimental support for human papillomavirus genome amplification early after infectious delivery

Even though replication and transcription of human papillomavirus type 16 (HPV16) has been intensively studied, little is known about immediate early events of the viral life cycle due to the lack of an efficient infection model allowing genetic dissection of viral factors. We employed the recently developed infection model (Bienkowska-Haba et al. PLOS Pathogen 2018) to investigate genome amplification and transcription immediately after infectious delivery of viral genome to nuclei of primary keratinocytes. Using EdU pulse labeling and highly sensitive fluorescence in situ hybridization, we observed that the HPV16 genome is replicated and amplified in an E1 and E2 dependent manner. Knockout of E1 resulted in failure of the viral genome to replicate and amplify. In contrast, knockout of the E8^E2 repressor led to increased viral genome copy number confirming previous reports. Genome copy control by E8^E2 was confirmed for differentiation-induced genome amplification. Lack of functional E1 had no effect on transcription from the early promoter, suggesting that viral genome replication is not required for p97 promoter activity. However, infection with an HPV16 mutant virus defective for E2 transcriptional function revealed a requirement of E2 for efficient transcription from the early promoter. In absence of the E8^E2 protein, early transcript levels are unaltered and even decreased when normalized to genome copy number. Surprisingly, lack of functional E8^E2 repressor did not affect E8^E2 transcript levels when normalized to genome copy number. These data suggest that the main function of E8^E2 in the viral life cycle is to control genome copy number. IMPORTANCEIt is being assumed that HPV utilizes three different modes of replication during its lifecycle: initial amplification during the establishment phase, genome maintenance and differentiation-induced amplification. However, HPV16 initial amplification was never formally proven due to a lack of an infection model. Using our recently established infection model (Bienkowska-Haba et al. PLOS Pathogen 2018), we demonstrate herein that viral genome is indeed amplified in an E1 and E2 dependent manner. Furthermore, we find that the main function of the viral repressor E8^E2 is to control viral genome copy number. We did not find evidence that it regulates its own promoter in a negative feedback loop. Our data also suggest that the E2 transactivator function is required for stimulation of early promoter activity, which has been debated in the literature. Overall, this report confirms the usefulness of the infection model for studying early events of the HPV lifecycle using mutational approaches.

microbiology↗
bioRxiv2023

Novel diversity of polar Cyanobacteria revealed by genome-resolved metagenomics

Benthic microbial mats dominated by Cyanobacteria are important features of polar lakes. Although culture-independent studies have provided important insights into their diversity, only a handful of genomes of polar Cyanobacteria have been sequenced to date. Here, we applied a genome-resolved metagenomics approach to data obtained from Arctic, sub-Antarctic, and Antarctic microbial mats. We recovered 22 unique metagenome-assembled genomes (MAGs) of Cyanobacteria, most of which are only distantly related to genomes that have been sequenced so far. These include i) lineages that are common in polar microbial mats such as the filamentous taxa Pseudanabaena, Leptolyngbya, Microcoleus/Tychonema, and Phormidium; ii) the less common taxa Crinalium and Chamaesiphon; iii) an enigmatic Chroococcales lineage only distantly related to Microcystis; and iv) an early branching lineage in the order Gloeobacterales that is almost exclusively restricted to the cold biosphere, for which we propose the name Candidatus Sivonenia alaskensis. Our results show that genome-resolved metagenomics is a powerful tool for expanding our understanding of the diversity of Cyanobacteria, especially in understudied remote and extreme environments. Data summaryThe sequencing data generated in this study have been submitted to the European Nucleotide Archive (ENA) under the BioProject PRJEB59431. Individual accession numbers for raw reads and genomic bins are listed in Table S1 and Table S3, respectively. Genomic bins can also be downloaded from doi.org/10.6084/m9.figshare.22003967. The commands used throughout this study are available in github.com/igorspp/polar-cyanobacteria-MAGs. Impact statementCyanobacteria are photosynthetic microorganisms that play important roles in polar lacustrine ecosystems. Many Cyanobacteria are difficult to grow in the laboratory, particularly in isolation from other organisms, which makes it challenging to sequence their genomes. As such, considerably fewer genomes of Cyanobacteria have been sequenced so far compared to other bacteria. In this study, we used a metagenomics approach to recover novel genomes of Cyanobacteria from Arctic and Antarctic microbial mats without the need to isolate the organisms. The community DNA was extracted and sequenced, and the genomes of individual populations were separated using bioinformatics tools. We recovered 22 different genomes of Cyanobacteria, many of which have not been sequenced before. We describe in more detail an interesting lineage of ancestral Cyanobacteria in the order Gloeobacterales, for which we propose the name Candidatus Sivonenia alaskensis. Our study shows that genome-resolved metagenomics is a valuable approach for obtaining novel genomes of Cyanobacteria, which are needed to improve our understanding of life in the polar regions and the planet at large.

microbiology↗
bioRxiv2023

Deep Whole-Genome Resequencing of Marek's Disease Virus Strains Reveals Intrastrain Variation

Current strategies to understand the molecular basis of Mareks disease virus (MDV) virulence primarily consist of cataloguing divergent nucleotides between strains with different phenotypes. However, each MDV strain is typically represented by a single consensus genome despite the confirmed existence of mixed viral populations. To assess the reliability of single-consensus interstrain genomic comparisons, we obtained two additional consensus genomes of vaccine strain CVI988 (Rispens) and two additional consensus genomes of the very virulent strain Md5 by sequencing viral stocks and cultured field isolates. In conjunction with the published genomes of CVI988 and Md5, this allowed us to perform 3-way comparisons between consensus genomes of the same strain. We found that consensus genomes of CVI988 can vary in as many as 236 positions involving 13 open reading frames (ORFs). In contrast, we found that Md5 genomes varied only in 11 positions involving a single ORF. Phylogenomic analyses showed all three Md5 consensus genomes clustered closely together, while also showing that CVI988GenBank.BAC diverged from CVI988Pirbright.lab and CVI988USDA.PA.field. Comparison of CVI988 consensus genomes revealed 19 SNPs in the unique regions of CVI988GenBank.BAC that were not present in either CVI988Pirbright.lab or CVI988USDA.PA.field. Finally, we evaluated the genomic heterogeneity of CVI988 and Md5 populations by identifying positions with >2% read support for alternative alleles in two ultra-deeply sequenced samples. We were able to confirm that both populations of CVI988 and Md5 were mixed, exhibiting a total of 29 and 27 high-confidence minor variant positions, respectively. We did not find any evidence of minor variants in the positions corresponding to the 19 SNPs in the unique regions of CVI988GenBank.BAC. Taken together, our findings confirm that consensus genomes of the same strain of MDV can vary and suggest that multiple consensus genomes per strain are needed in order to maximize the accuracy of interstrain genomic comparisons.

microbiology↗
bioRxiv2025

Preliminary genomic data on five Orientia tsutsugamushi strains isolated in Vellore, India

IntroductionOrientia tsutsugamushi, the causative agent of scrub typhus, can be isolated in Vero or L929 cells and has a small genome (2-2.5 Mb). However, genome assembly is challenging due to the presence of host DNA contamination and a high proportion of repeat regions (up to 51%). Current global data includes 11 fully annotated genomes, with none from India. Here, we present the first whole-genome sequences of O. tsutsugamushi from India. MethodsFive O. tsutsugamushi strains were cultured in Vero cells and confirmed by 47kDa real-time PCR. Genomic DNA was extracted after removal of host DNA and sequencing libraries were prepared. Whole-genome sequencing was performed using the PacBio Sequel II system in CCS/HiFi mode. The raw reads were assembled using Flye, and genome completeness was assessed with QUAST and BUSCO. Annotation was performed using the NCBI PGAP pipeline and comparative genome analysis by Roary. Phylogenetic analysis was based on the full-length 56kDa gene, which contains four variable domains. ResultsWe report five complete genomes of O. tsutsugamushi, four of which are circular and one linear. Genome sizes range from 2.1 to 2.4 Mb. The total number of predicted genes falls between 2,379 and 2,715, with an average of 1,824 coding genes and 613 pseudogenes. Repeat regions constitute 53-59% of the genome, a higher proportion than previously reported. All five genomes have been submitted to NCBI GenBank (Accession Numbers: CP166954-58). Phylogenetic analysis based on the full-length 56kDa gene revealed that two strains belong to the Karp genogroup, two to Kato, and one to TA763. ConclusionThis study presents the first whole-genome sequencing data of O. tsutsugamushi from India. Notably, the repetitive regions in these genomes are more extensive than previously reported. Further analyses with additional isolates are necessary to validate this observation. Comprehensive phylogenomic studies, particularly to elucidate evolutionary dynamics and potential recombination events will provide further information.

microbiology↗
bioRxiv2025

Leveraging FracMinHash Containment for Genomic dN/dS

Increasing availability of genomic data demands algorithmic approaches that can efficiently and accurately conduct downstream genomic analyses. These analyses, such as evaluating selection pressures within and across genomes, can reveal developmental and environmental pressures. One such commonly used metric to measure evolutionary pressures is based on the ratio of non-synonymous and synonomous substitution rates, dN/dS. Conventionally, the dN/dS ratio is used to infer selection pressures employing alignments to estimate total non-synonymous and synonymous substitution rates along protein-coding genes. However, this process can be time consuming and not scalable for larger datasets. Recently, a fast, approximate similarity measure, FracMinHash containment, was introduced and related to average nucleotide identity. In this work, we show how FracMinHash containment can be used to quickly estimate dN/dS enabling alignment-free estimations at a genomic level. Through simulated and real world experiments, our results indicate that employing FracMinHash containment to estimate dN/dS is scalable, enabling pairwise dN/dS estimations for 85,205 genomes within 5 hours. Furthermore, our approach is comparable to traditional dN/dS methods, representing sequences subject to positive and negative selection across various mutation rates. Moreover, we used this model to evaluate signatures of selection between Archaeal and Bacterial genomes, identifying a previously unreported metabolic island between Methanobrevibacter sp. RGIG2411 and Candidatus Saccharibacteria bacterium RGIG2249. We present, FracMinHash dN/dS, a novel alignment-free approach for estimating dN/dS at a genome level that is accurate and scalable beyond gene-level estimations while demonstrating comparability to conventional alignment-based dN/dS methods. Leveraging the alignment-free similarity estimation, FracMinHash containment, pairwise dN/dS estimations are facilitated within milliseconds, making it suitable for large-scale evolutionary analyses across diverse taxa. It supports comparative genomics, evolutionary inference, and functional interpretation across both synthetic, and complex biological datasets. Availability and implementationA version of the implementation is available at https://github.com/KoslickiLab/dnds-using-fmh.git. The reproduction of figures, data, and analysis can be found at https://github.com/KoslickiLab/dnds-using-fmh_reproducibles.git. Contactdmk333@psu.edu Supplementary informationSupplementary data are available at PLOS Computational Biology online. Author summaryUnderstanding how evolution shapes genomes helps us learn about the pressures organisms face in their environments. Scientists traditionally measure this by comparing genetic changes that alter proteins versus those that dont, a ratio that reveals whether natural selection is preserving or changing genes. However, this conventional approach requires computationally intensive sequence alignments, making it impractical for analyzing the massive genomic datasets now available. We developed a faster, alignment-free method to estimate evolutionary pressure across entire genomes. Our approach uses a computational technique called FracMinHash that compresses genomic information while preserving meaningful patterns. We tested our method on both simulated and real-world data, including over 85,000 microbial genomes, completing the analysis in just five hours whereas traditional methods would take days or weeks for the same analysis. The results were comparable to traditional methods and correctly identified genes under different types of selection. Using this approach, we discovered a previously unreported shared genetic region between an archaeal and bacterial species from the goat gut microbiome, suggesting ancient gene transfer between these distant branches of life. Our method makes large-scale evolutionary analysis practical for diverse applications, from tracking microbial strains to understanding adaptation in complex microbial communities, potentially accelerating discoveries in comparative genomics and evolutionary biology.

bioinformatics↗
bioRxiv2025

COATswga: A Coverage Optimizing and Accurate Toolkit for fast primer design in selective whole genome amplification

BackgroundDespite the transformative nature of next-generation sequencing in genomics, efficiently capturing underrepresented microbial DNA from complex biological mixtures, such as pathogens from host samples, remains a persistent challenge. Unwanted genomes make whole-genome sequencing (WGS) inefficient and expensive without enrichment of the targeted genome. Selective whole genome amplification (sWGA) uses primers that selectively bind to the target genome to preferentially amplify and enrich an entire microbial genome. However, existing sWGA primer design is often complicated and time-consuming, with primers usually having biased amplification producing uneven genome coverage. Developing faster primer design methods that ensure uniform, reliable microbial genome recovery are critical to improving sWGA. MethodsWe developed COATswga, a Coverage Optimizing and Accurate Toolkit for designing sWGA primer sets. The pipeline consists of three key steps: (1) primer discovery, using k-mer counting to identify all candidate primers in the target genomes; (2) filtering, where primers are screened for amplification potential, thermodynamic stability, and specificity; and (3) set formation, which uses a novel interval-based tiling algorithm. COATswga is parallelized for efficiency and supports pre-existing primer set refinement and gap filling. The pipeline was evaluated by designing primer sets for Plasmodium falciparum (strain 3D7) and benchmarking their performance against sets generated using similar pipelines. ResultsCOATswga-designed primers demonstrated superior enrichment based on both qPCR and long-read sequencing. In P. falciparum-spiked human DNA samples at parasitemia levels of 400, 100, and 10 parasites/L, COATswga primers achieved higher amplification efficiency and specificity compared to alternatives. At 100 parasitemia, COATswga yielded [~]99% of reads mapping to the P. falciparum genome with 82.5% of the genome having 5x coverage. Even at 10 parasites/uL, COATswga primers enabled effective amplification of key drug resistance genes (pfcrt and pfmdr1). Additionally, integration with molecular inversion probe (MIP) genotyping showed that COATswga greatly improved sequencing depth and target recovery in low-density infections compared to established sWGA primers. ConclusionCOATswga provides a robust and flexible solution for designing sWGA primers to reliably recover targeted genomes from complex samples. The new P. falciparum primer set performs well in low-parasitemia samples allowing more extensive application of sWGA in falciparum malaria genomics.

bioinformatics↗
bioRxiv2018

A planarian nidovirus expands the limits of RNA genome size

RNA viruses are the only known RNA-protein (RNP) entities capable of autonomous replication (albeit within a permissive environment). A 33.5-kb nidovirus has been considered close to the upper size limit for such entities; conversely, the minimal cellular DNA genome is ~200 kb. This large difference presents a daunting gap for the transition from primordial RNP to contemporary DNA-RNP-based life. Whether or not RNA viruses represent transitional steps on the road to DNA-based life, studies of larger RNA viruses advance our understanding of size constraints on RNP entities. For example, emergence of the largest previously known RNA genomes (20-34 kb in positive-stranded nidoviruses, including coronaviruses) is associated with a proofreading exoribonuclease encoded in the nidoviral open reading frame 1b (ORF1b). However, apparent constraints on the size of ORF1b, which encodes this and other key replicative enzymes, have been hypothesized to limit further expansion of viral RNA genomes. Here, we characterize a novel nidovirus (planarian secretory cell nidovirus; PSCNV) whose disproportionately large ORF1b-like region, and overall 41.1 kb genome, substantially extend the presumed limits on RNA genome size. This genome encodes a predicted 13,556-aa polyprotein in an unconventional single ORF, yet retains canonical nidoviral genome organization and expression, and key replicative domains. Our evolutionary analysis suggests that PSCNV diverged early from multi-ORF nidoviruses, and subsequently acquired additional genes, including those typical of large DNA viruses or hosts. PSCNVs greatly expanded genome, proteomic complexity, and unique features - impressive in themselves - attest to the likelihood of still-larger RNA genomes awaiting discovery.\n\nSignificance StatementRNA viruses are the only known RNA-protein (RNP) entities capable of autonomous replication. The upper genome size for such entities was assumed to be <35 kb; conversely, the minimal cellular DNA genome is ~200 kb. This large difference presents a daunting gap for the proposed evolution of contemporary DNA-RNP-based life from primordial RNP entities. Here, we describe a nidovirus from planarians, whose 41.1 kb genome is 23% larger than the largest known of RNA virus. The planarian secretory cell nidovirus has broken apparent constraints on the size of the genomic subregion that encodes core replication machinery, and has acquired genes not previously observed in RNA viruses. This virus challenges and advances our understanding of the limits to RNA genome size.

microbiology↗
bioRxiv2018

The tempo of linked selection: emergence of a heterogeneous genomic landscape during a recent radiation of monkeyflowers

Speciation genomic studies aim to interpret patterns of genome-wide variation in light of the processes that give rise to new species. However, interpreting the genomic landscape of speciation is difficult, because many evolutionary processes can impact levels of variation. Facilitated by the first chromosome-level assembly for the group, we use whole-genome sequencing and simulations to shed light on the processes that have shaped the genomic landscape during a recent radiation of monkeyflowers. After inferring the phylogenetic relationships among the nine taxa in this radiation, we show that highly similar diversity ({pi}) and differentiation (FST) landscapes have emerged across the group. Variation in these landscapes was strongly predicted by the local density of functional elements and the recombination rate, suggesting that the landscapes have been shaped by widespread natural selection. Using the varying divergence times between pairs of taxa, we show that the correlations between FST and genome features arose almost immediately after a population split and have become stronger over time. Simulations of genomic landscape evolution suggest that background selection (i.e., selection against deleterious mutations) alone is too subtle to generate the observed patterns, but scenarios that involve positive selection and genetic incompatibilities are plausible alternative explanations. Finally, tests for introgression among these taxa reveal widespread evidence of heterogeneous selection against gene flow during this radiation. Thus, combined with existing evidence for adaptation in this system, we conclude that the correlation in FST among these taxa informs us about the genomic basis of adaptation and speciation in this system.\n\nAuthor summaryWhat can patterns of genome-wide variation tell us about the speciation process? The answer to this question depends upon our ability to infer the evolutionary processes underlying these patterns. This, however, is difficult, because many processes can leave similar footprints, but some have nothing to do with speciation per se. For example, many studies have found highly heterogeneous levels of genetic differentiation when comparing the genomes of emerging species. These patterns are often referred to as differentiation landscapes because they appear as a rugged topography of peaks and valleys as one scans across the genome. It has often been argued that selection against deleterious mutations, a process referred to as background selection, is primarily responsible for shaping differentiation landscapes early in speciation. If this hypothesis is correct, then it is unlikely that patterns of differentiation will reveal much about the genomic basis of speciation. However, using genome sequences from nine emerging species of monkeyflower coupled with simulations of genomic divergence, we show that it is unlikely that background selection is the primary architect of these landscapes. Rather, differentiation landscapes have probably been shaped by adaptation and gene flow, which are processes that are central to our understanding of speciation. Therefore, our work has important implications for our understanding of what patterns of differentiation can tell us about the genetic basis of adaptation and speciation.

evolutionary biology↗
bioRxiv2019

Accurate and Complete Genomes from Metagenomes

Genomes are an integral component of the biological information about an organism and, logically, the more complete the genome, the more informative it is. Historically, bacterial and archaeal genomes were reconstructed from pure (monoclonal) cultures and the first reported sequences were manually curated to completion. However, the bottleneck imposed by the requirement for isolates precluded genomic insights for the vast majority of microbial life. Shotgun sequencing of microbial communities, referred to initially as community genomics and subsequently as genome-resolved metagenomics, can circumvent this limitation by obtaining metagenome-assembled genomes (MAGs), but gaps, local assembly errors, chimeras and contamination by fragments from other genomes limit the value of these genomes. Here, we discuss genome curation to improve and in some cases achieve complete (circularized, no gaps) MAGs (CMAGs). To date, few CMAGs have been generated, although notably some are from very complex systems such as soil and sediment. Through analysis of ~7000 published complete bacterial isolate genomes, we verify the value of cumulative GC skew in combination with other metrics to establish bacterial genome sequence accuracy. Interestingly, analysis of cumulative GC skew identified potential mis-assemblies in some reference genomes of isolated bacteria and the repeat sequences that likely gave rise to them. We discuss methods that could be implemented in bioinformatic approaches for curation to ensure that metabolic and evolutionary analyses can be based on very high-quality genomes.

microbiology↗
bioRxiv2026

Fundamental limitations of genomic language models for realistic sequence generation

Large language models (LLMs) have shown remarkable success in natural language processing, prompting interest in their application to genomic sequence analysis. Genomic Language Models (gLMs) based on similar architectures offer a promising avenue for synthetic genome generation and characterization. However, their effectiveness for biological sequence modeling remains poorly characterized. We present a comprehensive evaluation of genomic language models that explicitly aim to generate entire synthetic genomes. We tested Evo 2 on diverse prokaryotic, eukaryotic and viral genomes, and megaDNA on bacteriophage genomes, and assessed performance across key biological features and organizational patterns. Our results reveal systematic failures in gLM-based genomic reconstruction. While the synthetic sequences captured local sequence statistics, they consistently failed to preserve long-range genomic organization, repeat and k-mer composition, transcription factor binding site architecture, and evolutionary constraints. Generated sequences exhibited violations of natural genomic patterns and models showed particular difficulty with repetitive elements. To assess the quality of genome generation, we trained a convolutional neural network that reliably distinguished synthetic from natural sequences, achieving AUROC values up to 0.97 in eukaryotes and 0.82 in prokaryotes, with classification accuracy increasing monotonically with genomic distance from the seed. These findings suggest fundamental limitations in current gLM architectures for capturing the long-range, hierarchical nature of genomic sequences. Our work highlights the need for specialized architectures that explicitly model evolutionary constraints rather than relying solely on statistical patterns, with important implications for computational biology applications requiring realistic sequence generation and for biosafety assessments that depend on the distinguishability of synthetic and natural genomic sequences.

synthetic biology↗
bioRxiv2026

Verticall: A fast and robust tool for recombination detection in large-scale bacterial genomic datasets

The inference and removal of horizontally acquired genomic regions is a crucial step in phylogenomics analyses for evolutionary studies. Existing tools perform well on clonal lineage-focused datasets on the scale of hundreds of genomes, but are limited in their ability to analyse larger or more diverse datasets. Here we present Verticall, a tool to identify recombinant regions in bacterial assemblies and generate recombination-free phylogenies, which scales to thousands of genomes from clonal to genus-level diversity. Verticall uses a non-parametric approach to assign genomic regions as horizontally or vertically related based on the distribution of pairwise genetic distances between genomes. Recombination-free phylogenetic trees may be inferred by either calculating a pairwise genetic distance matrix from vertical-only regions (distance-tree approach) or by pairwise comparisons of all genomes to a reference and then masking horizontally acquired regions in a pseudo-alignment to the reference (alignment-tree approach). We demonstrate Verticalls performance using four publicly available whole-genome sequence datasets of varying sample sizes (range: 154 - 4,857 genomes) and evolutionary scales (ranging from within-lineage to genus-wide diversity). Across all four datasets, Verticall showed comparable or superior performance to the established tools Gubbins and ClonalFrameML in terms of computational efficiency, plausibility of inferred phylogenetic trees, and recovery of temporal signal for molecular dating. Our results show that Verticall is a useful tool to more efficiently and accurately detect recombination, particularly applied to datasets for which existing tools are limited, including large datasets with hundreds to thousands of genomes and those that span entire species or genera. Verticall is available free and open source at https://github.com/rrwick/Verticall. Impact StatementMany bacterial species can acquire genetic material from external sources and stably incorporate them into their own genomes through homologous recombination. During phylogenomic analyses to investigate outbreaks or for evolutionary studies, a core objective is often to reconstruct the evolutionary history of the studied organisms independent of these horizontally acquired genomic regions. This is particularly desirable when the aim is to construct dated phylogenies, as horizontally acquired variation can interfere with the molecular clock signal on which dating relies. Existing recombination detection programs perform well in certain contexts, but their algorithms are not suitable for datasets with very high diversity or thousands of genomes. We addressed this gap by developing the software package Verticall. We show this approach produces comparable results to existing software for smaller more clonal datasets, but also performs well on datasets that the existing packages cannot handle. Data SummaryVerticall is available free and open source at https://github.com/rrwick/Verticall. We used published whole-genome sequence data deposited in public databases (Pathogenwatch [https://pathogen.watch/]; European Nucleotide Archive [https://www.ebi.ac.uk/ena/], Sequence Read Archive [https://www.ncbi.nlm.nih.gov/sra/]). Accession numbers for the raw whole-genome sequences are presented in Tables S2-S6. All data, code, and analysis commands used to generate the results and figures presented in this paper are available on figshare (DOI: 10.6084/m9.figshare.31930821) and GitHub (https://github.com/erkison/verticall_paper).

bioinformatics↗
bioRxiv2026

Application of Computer Vision Tools to Maize Genomic Data for Trait Prediction and Gene Discovery

Artificial intelligence and machine learning for computer vision (CV) and image recognition is a rapidly evolving field with multiple potential applications in plant genomics. While CV has been widely adopted by the research community for plant phenotyping and disease surveillance, applications of CV tools to plant genome analysis are underrepresented. CV tools may complement traditional statistical classification tools used in plant genomics, since CV perceives problems holistically rather than granularly (in terms of pattern recognition), which is particularly applicable to analysis of large, complex eukaryotic genomes. In this study, we report on a new strategy to apply existing CV tools to classify plant genotypes and predict genotype-phenotype relationships. A technique was developed for converting maize genome resequencing data into a set of images reminiscent of a quick response (QR) code. Several hundred maize genomes were processed and it was demonstrated that CV models can successfully categorize genome images into heterotic groups (accuracy and recall > 0.8). Models for classifying genome images into phenotypic trait groups (such as short, medium, and high plant height) performed with moderate success for the most heritable trait analyzed (ear height; accuracy and recall > 0.5). Querying model results permitted identification of genome regions that were important for model classification predictions. The CV model results revealed enriched metabolic pathways consistent with traits under consideration. Overall, our initial application of CV tools to plant genome analysis highlights its applicability to genomic data. Design of new CV architectures optimized for genome-derived images may further improve upon our initial results generated using only off-the-shelf CV tools optimized for unrelated image analysis tasks. Core ideasO_LIAI/ML computer vision (CV) tools were applied to encoded maize genomes C_LIO_LICV image classification tools were able to successfully classify encoded genomes into heterotic groups C_LIO_LITrait values of maize strain ear height could be predicted with moderate success C_LIO_LIGenome regions encoding plausible metabolic pathways used by the classifier were identified C_LIO_LIRecommendations for improved success of CV for genotype-to-phenotype are discussed C_LI

bioinformatics↗
bioRxiv2026

Generating E. coli 0.5 controlled by a half-sized genome

Genome synthesis is a major limitation in generative biology. Here, the half-sized genome of Escherichia coli was constructed by fleshing out an imperfect minimal genome through genome-scale debugging process. Our platform consists of integrated development environment (IDE) and runtime environment (RTE). The genome IDE supported the cell-free assembly of 200-300 kb plasmids and their in vivo fusion into a single 1.7 Mb plasmid. This imperfect genome was stably maintained in E. coli as a guest genome. The RTE relies on the restriction enzyme-mediated self-digestion of the host genome in the presence and absence of the RecA recombinase. The guest genome was tested, debugged, and partially replaced by the host genome to establish E. coli controlled by a 2.3-Mb genome. This is less than half in size of the wildtype and the smallest ever reported. Enfleshing a guest genome will facilitate genome printing that transforms AI-designed genomes into physical ones.

synthetic biology↗
bioRxiv2016

Global Genome Nucleotide Excision Repair is Organised into Domains Promoting Efficient DNA Repair in Chromatin

The rates at which lesions are removed by DNA repair can vary widely throughout the genome with important implications for genomic stability. To study this, we measured the distribution of nucleotide excision repair (NER) rates for UV-induced lesions throughout the budding yeast genome. By plotting these repair rates in relation to genes and their associated flanking sequences, we reveal that in normal cells, genomic repair rates display a distinctive pattern, suggesting that DNA repair is highly organised within the genome. Furthermore, by comparing genome-wide DNA repair rates in wild-type cells, and cells defective in the global genome-NER (GG-NER) sub-pathway, we establish how this alters the distribution of NER rates throughout the genome. We also examined the genomic locations of GG-NER factor binding to chromatin before and after UV irradiation revealing that GG-NER is organised and initiated from specific genomic locations. At these sites, chromatin occupancy of the histone acetyl transferase Gcn5 is controlled by the GG-NER complex, which regulates histone H3 acetylation and chromatin structure, thereby promoting efficient DNA repair of UV-induced lesions. Chromatin remodeling during the GG-NER process is therefore organized into these genomic domains. Importantly, loss of Gcn5, significantly alters the genomic distribution of NER rates, a finding that has important implications for the effects of chromatin modifiers on the distribution of mutations that arise throughout the genome.

Genomics↗