The broken Alzheimer's disease genome.

The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.

SNP-SVant: A Computational Workflow to Predict and Annotate Genomic Variants in Organisms Lacking Benchmarked Variants.

Whole-genome sequencing is widely used to investigate population genomic variation in organisms of interest. Assorted tools have been independently developed to call variants from short-read sequencing data aligned to a reference genome, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). We developed SNP-SVant, an integrated, flexible, and computationally efficient bioinformatic workflow that predicts high-confidence SNPs and SVs in organisms without benchmarked variants, which are traditionally used for distinguishing sequencing errors from real variants. In the absence of these benchmarked datasets, we leverage multiple rounds of statistical recalibration to increase the precision of variant prediction. The SNP-SVant workflow is flexible, with user options to tradeoff accuracy for sensitivity. The workflow predicts SNPs and small insertions and deletions using the Genome Analysis ToolKit (GATK) and predicts SVs using the Genome Rearrangement IDentification Software Suite (GRIDSS), and it culminates in variant annotation using custom scripts. A key utility of SNP-SVant is its scalability. Variant calling is a computationally expensive procedure, and thus, SNP-SVant uses a workflow management system with intermediary checkpoint steps to ensure efficient use of resources by minimizing redundant computations and omitting steps where dependent files are available. SNP-SVant also provides metrics to assess the quality of called variants and converts between VCF and aligned FASTA format outputs to ensure compatibility with downstream tools to calculate selection statistics, which are commonplace in population genomics studies. By accounting for both small and large structural variants, users of this workflow can obtain a wide-ranging view of genomic alterations in an organism of interest. Overall, this workflow advances our capabilities in assessing the functional consequences of different types of genomic alterations, ultimately improving our ability to associate genotypes with phenotypes. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Predicting single nucleotide polymorphisms and structural variations Support Protocol 1: Downloading publicly available sequencing data Support Protocol 2: Visualizing variant loci using Integrated Genome Viewer Support Protocol 3: Converting between VCF and aligned FASTA formats.

Disregarding multimappers leads to biases in the functional assessment of NGS data.

BACKGROUND: Standard ChIP-seq and RNA-seq processing pipelines typically disregard sequencing reads whose origin is ambiguous ("multimappers"). This usual practice has potentially important consequences for the functional interpretation of the data: genomic elements belonging to clusters composed of highly similar members are left unexplored. RESULTS: In particular, disregarding multimappers leads to the underrepresentation in epigenetic studies of recently active transposable elements, such as AluYa5, L1HS and SVAs. Furthermore, this common strategy also has implications for transcriptomic analysis: members of repetitive gene families, such the ones including major histocompatibility complex (MHC) class I and II genes, are under-quantified. CONCLUSION: Revealing inherent biases that permeate routine tasks such as functional enrichment analysis, our results underscore the urgency of broadly adopting multimapper-aware bioinformatic pipelines -currently restricted to specific contexts or communities- to ensure the reliability of genomic and transcriptomic studies.

Genomic analysis of a spontaneous unifoliate mutant reveals gene candidates associated with compound leaf development in Vigna unguiculata [L] Walp.

Molecular mechanisms which underpin compound leaf development in some legumes have been reported, but there is no previous study on the molecular genetic control of compound leaf formation in Vigna unguiculata (cowpea), an important dryland legume of African origin. In most studied species with compound leaves, class 1 KNOTTED-LIKE HOMEOBOX genes expressed in developing leaf primordia sustain morphogenetic activity, allowing leaf dissection and the development of leaflets. Other genes, such as, SINGLE LEAFLET1 in Medicago truncatula and Trifoliate in Solanum lycopersicum, are also implicated in regulating compound leaf patterning. To set the pace for an in-depth understanding of the genetics of compound leaf development in cowpea, we applied RNA-seq and whole genome shotgun sequence datasets of a spontaneous cowpea unifoliate mutant and its trifoliate wild-type cultivar to conduct comparative reference-based gene expression, de novo genome-wide isoform switch, and genome variant analyses between the two genotypes. Our results suggest that genomic variants upstream of LATE ELONGATED HYPOCOTYL and down-stream of REVEILLE4, BRASSINOSTERIOD INSENSITIVE1 and LATERAL ORGAN BOUNDARIES result in down-regulation of key components of cowpea circadian rhythm central oscillator and brassinosteroid signaling, resulting in unifoliate leaves and brassinosteroid-deficient-like phenotypes. We have stated hypotheses that will guide follow-up studies expected to provide more insights.

Genie: the first open-source ISO/IEC encoder for genomic data.

For the last two decades, the amount of genomic data produced by scientific and medical applications has been growing at a rapid pace. To enable software solutions that analyze, process, and transmit these data in an efficient and interoperable way, ISO and IEC released the first version of the compression standard MPEG-G in 2019. However, non-proprietary implementations of the standard are not openly available so far, limiting fair scientific assessment of the standard and, therefore, hindering its broad adoption. In this paper, we present Genie, to the best of our knowledge the first open-source encoder that compresses genomic data according to the MPEG-G standard. We demonstrate that Genie reaches state-of-the-art compression ratios while offering interoperability with any other standard-compliant decoder independent from its manufacturer. Finally, the ISO/IEC ecosystem ensures the long-term sustainability and decodability of the compressed data through the ISO/IEC-supported reference decoder.

OMD Curation Toolkit: a workflow for in-house curation of public omics datasets.

BACKGROUND: Major advances in sequencing technologies and the sharing of data and metadata in science have resulted in a wealth of publicly available datasets. However, working with and especially curating public omics datasets remains challenging despite these efforts. While a growing number of initiatives aim to re-use previous results, these present limitations that often lead to the need for further in-house curation and processing. RESULTS: Here, we present the Omics Dataset Curation Toolkit (OMD Curation Toolkit), a python3 package designed to accompany and guide the researcher during the curation process of metadata and fastq files of public omics datasets. This workflow provides a standardized framework with multiple capabilities (collection, control check, treatment and integration) to facilitate the arduous task of curating public sequencing data projects. While centered on the European Nucleotide Archive (ENA), the majority of the provided tools are generic and can be used to curate datasets from different sources. CONCLUSIONS: Thus, it offers valuable tools for the in-house curation previously needed to re-use public omics data. Due to its workflow structure and capabilities, it can be easily used and benefit investigators in developing novel omics meta-analyses based on sequencing data.

Pragmatic nationwide master observational trial based on genomic alterations in advanced solid tumors: KOrean Precision Medicine Networking Group Study of MOlecular profiling guided therapy based on genomic alterations in advanced Solid tumors (KOSMOS)-II study protocol KCSG AL-22-09.

BACKGROUND: Next-generation sequencing (NGS) has been introduced to many Korean institutions to support molecular diagnostics in cancer since 2017, when it became eligible for reimbursement by the National Health Insurance Service. However, the uptake of molecularly guided treatment (MGT) based on NGS results has been limited because of stringent regulations regarding prescriptions outside of approved indications, a lack of clinical trial opportunities, and limited access to molecular tumor boards (MTB) at most institutions. The KOSMOS-II study was designed to demonstrate the feasibility and effectiveness of MGT, informed by MTBs, using a nationwide precision medicine platform. METHODS: The KOSMOS-II trial is a large-scale nationwide master observational study. It involves a framework for screening patients with metastatic solid tumors for actionable genetic alterations based on local NGS testing. It recommends MGT through a remote and centralized MTB meeting held biweekly. MGT can include one of the following options: Tier 1, the therapeutic use of investigational drugs targeting genetic alterations such as ALK, EGFR, ERBB2, BRAF, FH, ROS1, and RET, or those with high tumor mutational burden; Tier 2, comprising drugs with approved indications or those permitted for treatment outside of the indications approved by the Health Insurance Review and Assessment Service of Korea; Tier 3, involving clinical trials matching the genetic alterations recommended by the MTB. Given the anticipated proportion of patients receiving MGT in the range of 50% ± 3.25%, this study aims to enroll 1,000 patients. Patients must have progressed to one or more lines of therapy and undergone NGS before enrollment. DISCUSSION: This pragmatic master protocol provides a mass-screening platform for rare genetic alterations and high-quality real-world data. Collateral clinical trials, translational studies, and clinico-genomic databases will contribute to generating evidence for drug repositioning and the development of new biomarkers. TRIAL REGISTRATION: NCT05525858.

Pancreatic ß-Cell Identity Change through the Lens of Single-Cell Omics Research.

The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional ß-cell mass. This decline is predominantly attributed to ß-cell death, although recent findings suggest that the loss of ß-cell identity may also contribute to ß-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with ß-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on ß-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogeneity among ß-cells and have facilitated the identification of distinct ß-cell subpopulations through the discovery of cell surface markers, transcriptional regulators, the upregulation of stress-related genes, and alterations in chromatin activity. Furthermore, specific subsets of ß-cells have been identified in diabetes, such as displaying an immature, dedifferentiated gene signature, expressing significantly lower insulin mRNA levels, and expressing increased ß-cell precursor markers. Additionally, single-cell omics has increased insight into the detrimental effects of diabetes-associated conditions, including endoplasmic reticulum stress, oxidative stress, and inflammation, on ß-cell identity. Lastly, this review outlines the factors that may influence the identification of ß-cell subpopulations when designing and performing a single-cell omics experiment.

Estimating genomic relationships of metafounders across and within breeds using maximum likelihood, pseudo-expectation-maximization maximum likelihood and increase of relationships.

BACKGROUND: The theory of "metafounders" proposes a unified framework for relationships across base populations within breeds (e.g. unknown parent groups), and base populations across breeds (crosses) together with a sensible compatibility with genomic relationships. Considering metafounders might be advantageous in pedigree best linear unbiased prediction (BLUP) or single-step genomic BLUP. Existing methods to estimate relationships across metafounders Γ are not well adapted to highly unbalanced data, genotyped individuals far from base populations, or many unknown parent groups (within breed per year of birth). METHODS: We derive likelihood methods to estimate Γ . For a single metafounder, summary statistics of pedigree and genomic relationships allow deriving a cubic equation with the real root being the maximum likelihood (ML) estimate of Γ . This equation is tested with Lacaune sheep data. For several metafounders, we split the first derivative of the complete likelihood in a term related to Γ , and a second term related to Mendelian sampling variances. Approximating the first derivative by its first term results in a pseudo-EM algorithm that iteratively updates the estimate of Γ by the corresponding block of the H-matrix. The method extends to complex situations with groups defined by year of birth, modelling the increase of Γ using estimates of the rate of increase of inbreeding ( Δ F ), resulting in an expanded Γ and in a pseudo-EM+ Δ F algorithm. We compare these methods with the generalized least squares (GLS) method using simulated data: complex crosses of two breeds in equal or unsymmetrical proportions; and in two breeds, with 10 groups per year of birth within breed. We simulate genotyping in all generations or in the last ones. RESULTS: For a single metafounder, the ML estimates of the Lacaune data corresponded to the maximum. For simulated data, when genotypes were spread across all generations, both GLS and pseudo-EM(+ Δ F ) methods were accurate. With genotypes only available in the most recent generations, the GLS method was biased, whereas the pseudo-EM(+ Δ F ) approach yielded more accurate and unbiased estimates. CONCLUSIONS: We derived ML, pseudo-EM and pseudo-EM+ Δ F methods to estimate Γ in many realistic settings. Estimates are accurate in real and simulated data and have a low computational cost.

Redefining and interpreting genomic relationships of metafounders.

Metafounders are a useful concept to characterize relationships within and across populations, and to help genetic evaluations because they help modelling the means and variances of unknown base population animals. Current definitions of metafounder relationships are sensitive to the choice of reference alleles and have not been compared to their counterparts in population genetics-namely, heterozygosities, FST coefficients, and genetic distances. We redefine the relationships across populations with an arbitrary base of a maximum heterozygosity population in Hardy-Weinberg equilibrium. Then, the relationship between or within populations is a cross-product of the form Γ b , b ' = 2 n 2 p b - 1 2 p b ' - 1 ' with p being vectors of allele frequencies at n markers in populations b and b ' . This is simply the genomic relationship of two pseudo-individuals whose genotypes are equal to twice the allele frequencies. We also show that this coding is invariant to the choice of reference alleles. In addition, standard population genetics metrics (inbreeding coefficients of various forms; FST differentiation coefficients; segregation variance; and Nei's genetic distance) can be obtained from elements of matrix Γ .

The genomic landscape of Vk*MYC myeloma highlights shared pathways of transformation between mice and humans.

Multiple myeloma (MM) is a heterogeneous disease characterized by frequent MYC translocations. Sporadic MYC activation in the germinal center of genetically engineered Vk*MYC mice is sufficient to induce plasma cell tumors in which a variety of secondary mutations are spontaneously acquired and selected over time. Analysis of 119 Vk*MYC myeloma reveals recurrent copy number alterations, structural variations, chromothripsis, driver mutations, apolipoprotein B mRNA-editing enzyme, catalytic polypeptide (APOBEC) mutational activity, and a progressive decrease in immunoglobulin transcription that inversely correlates with proliferation. Moreover, we identify frequent insertional mutagenesis by endogenous retro-elements as a murine specific mechanism to activate NF-kB and IL6 signaling pathways shared with human MM. Despite the increased genomic complexity associated with progression, advanced tumors remain dependent on MYC. In summary, here we credential the Vk*MYC mouse as a unique resource to explore MM genomic evolution and describe a fully annotated collection of diverse and immortalized murine MM tumors.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

Genomic analysis and phylogenetic characterization of Himalayan snow trout, Schizothorax esocinus based on mitochondrial protein-coding genes.

BACKGROUND: Mitochondrial DNA (mtDNA) has become a significant tool for exploring genetic diversity and delineating evolutionary links across diverse taxa. Within the group of cold-water fish species that are native to the Indian Himalayan region, Schizothorax esocinus holds particular importance due to its ecological significance and is potentially vulnerable to environmental changes. This research aims to clarify the phylogenetic relationships within the Schizothorax genus by utilizing mitochondrial protein-coding genes. METHODS: Standard protocols were followed for the isolation of DNA from S. esocinus. For the amplification of mtDNA, overlapping primers were used, and then subsequent sequencing was performed. The genetic features were investigated by the application of bioinformatic approaches. These approaches covered the evaluation of nucleotide composition, codon usage, selective pressure using nonsynonymous substitution /synonymous substitution (Ka/Ks) ratios, and phylogenetic analysis. RESULTS: The study specifically examined the 13 protein-coding genes of Schizothorax species which belongs to the Schizothoracinae subfamily. Nucleotide composition analysis showed a bias towards A + T content, consistent with other cyprinid fish species, suggesting evolutionary conservation. Relative Synonymous Codon Usage highlighted leucine as the most frequent (5.18%) and cysteine as the least frequent (0.78%) codon. The positive AT-skew and the predominantly negative GC-skew indicated the abundance of A and C. Comparative analysis revealed significant conservation of amino acids in multiple genes. The majority of amino acids were hydrophobic rather than polar. The purifying selection was revealed by the genetic distance and Ka/Ks ratios. Phylogenetic study revealed a significant genetic divergence between S. esocinus and other Schizothorax species with interspecific K2P distances ranging from 0.00 to 8.87%, with an average of 5.76%. CONCLUSION: The present study provides significant contributions to the understanding of mitochondrial genome diversity and genetic evolution mechanisms in Schizothoracinae, hence offering vital insights for the development of conservation initiatives aimed at protecting freshwater fish species.

Insights into the novel Enterococcus faecalis phage: A comprehensive genome analysis.

Enterococcus faecalis, a Gram-positive bacterium, poses a significant clinical challenge owing to its intrinsic resistance to a broad spectrum of antibiotics, warranting urgent exploration of innovative therapeutic strategies. This study investigated the viability of phage therapy as an alternative intervention for antibiotic-resistant E. faecalis, with a specific emphasis on the comprehensive genomic analysis of bacteriophage SAM-E.f 12. The investigation involved whole-genome sequencing of SAM-E.f 12 using Illumina technology, resulting in a robust dataset for detailed genomic characterization. Bioinformatics analyses were employed to predict genes and assign functional annotations. The bacteriophage SAM-E.f 12, which belongs to the Siphoviridae family, exhibited substantial potential, with a burst size of 5.7 PFU/infected cells and a latent period of 20 min. Host range determination experiments demonstrated its effectiveness against clinical E. faecalis strains, positioning SAM-E.f 12 as a precise therapeutic agent. Stability assays underscore resilience across diverse environmental conditions. This study provides a comprehensive understanding of SAM-E.f 12 genomic composition, lytic lifecycle parameters, and practical applications, particularly its efficacy in murine wound models. These results emphasize the promising role of phage therapy, specifically its targeted approach against antibiotic-resistant E. faecalis strains. The nuanced insights derived from this research will contribute to the ongoing pursuit of efficacious phage therapies and offer valuable implications for addressing the clinical challenges associated with E. faecalis infections.

Genomic analysis uncovers novel candidate genes related to adaptation to tropical climates and milk production traits in native goats.

BACKGROUND: Since domestication, both evolutionary forces and human selection have played crucial roles in producing adaptive and economic traits, resulting in animal breeds that have been selected for specific climates and different breeding goals. Pakistani goat breeds have acquired genomic adaptations to their native climate conditions, such as tropical and hot climates. In this study, using next-generation sequencing data, we aimed to assess the signatures of positive selection in three native Pakistani goats, known as milk production breeds, that have been well adapted to their local climate. RESULTS: To explore the genomic relationship between studied goat populations and their population structure, whole genome sequence data from native goat populations in Pakistan (n = 26) was merged with available worldwide goat genomic data (n = 184), resulting in a total dataset of 210 individuals. The results showed a high genetic correlation between Pakistani goats and samples from North-East Asia. Across all populations analyzed, a higher linkage disequilibrium (LD) level (- 0.59) was found in the Pakistani goat group at a genomic distance of 1 Kb. Our findings from admixture analysis (K = 5 and K = 6) showed no evidence of shared genomic ancestry between Pakistani goats and other goat populations from Asia. The results from genomic selection analysis revealed several candidate genes related to adaptation to tropical/hot climates (such as; KITLG, HSPB9, HSP70, HSPA12B, and HSPA12B) and milk production related-traits (such as IGFBP3, LPL, LEPR, TSHR, and ACACA) in Pakistani native goat breeds. CONCLUSIONS: The results from this study shed light on the structural variation in the DNA of the three native Pakistani goat breeds. Several candidate genes were discovered for adaptation to tropical/hot climates, immune responses, and milk production traits. The identified genes could be exploited in goat breeding programs to select efficient breeds for tropical/hot climate regions.

Measuring the perceived wellbeing of hemodialysis patients: A Mind Genomics cartography.

Chronic Kidney Disease patients under hemodialysis have high morbidity rate, which tends to considerably affect their health-related quality of life. Multiple studies that have made use of different questionnaries report the poor life quality of this patient group. The research in hand implemented the Mind Genomics Approach as a method to asses the health-related quality of life of hemodialysis patients, while relying on conjoint measurements to group individuals with similar patterns of responses to a certain mindset. The study is conducted in 3 clinics with 219 patients. It uncovers three clusters or mindsets: Mindset 1- Feels guardedly optimistic but worried about money, Mindset 2-Feels strongly positive because the state guarantees and the family supports, Mindset 3-Feels positive only about money. Based on the analysis of the collected data, the findings of this study suggest that the quality of life in hemodialysis patients is highly correlated to their financial status. The current study is one of the few first attempts to apply Mind Genomics in medical settings and the first, to our knowledge, in hemodialysis centers. This technology might enable healthcare proffesionals to provide personalized psychological treatment and additional social support to patients, which in turn could improve their clinical outcomes. The study is an example of using technology as a service.

Genomic properties of a Bartonella quintana strain from Japanese macaque (Macaca fuscata) revealed by genome comparison with human and rhesus macaque strains.

Bartonella quintana, the causative agent of trench fever, is an intracellular bacterium that infects human erythrocytes and vascular endothelial cells. For many years, humans were considered the only natural hosts for B. quintana; however, it was recently discovered that wild Japanese macaques (Macaca fuscata) also serve as hosts for B. quintana. To elucidate the genetic characteristics of the B. quintana strain MF1-1 isolated from a Japanese macaque, we determined the complete genome sequence of the strain and compared it with those of strain Toulouse from a human and strain RM-11 from a rhesus macaque. General genomic features and orthologous gene cluster profiles are similar among the three strains, and strain MF1-1 is genetically closer to strain RM-11 than strain Toulouse based on the average nucleotide identity values; however, a significant inversion of approximately 0.68 Mb was detected in the chromosome of strain MF1-1. Moreover, the Japanese macaque strains lacked the bepA gene, which is responsible for anti-apoptotic function, and the trwL2, trwL4, and trwL6 genes, which may be involved in adhesion to erythrocytes of rhesus macaque and human. These features likely represent the genomic traits acquired by Japanese macaque strains in their host-associated evolution.

Exploring optimal drug targets through subtractive proteomics analysis and pangenomic insights for tailored drug design in tuberculosis.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, ranks among the top causes of global human mortality, as reported by the World Health Organization's 2022 TB report. The prevalence of M. tuberculosis strains that are multiple and extensive-drug resistant represents a significant barrier to TB eradication. Fortunately, having many completely sequenced M. tuberculosis genomes available has made it possible to investigate the species pangenome, conduct a pan-phylogenetic investigation, and find potential new drug targets. The 442 complete genome dataset was used to estimate the pangenome of M. tuberculosis. This study involved phylogenomic classification and in-depth analyses. Sequential filters were applied to the conserved core genome containing 2754 proteins. These filters assessed non-human homology, virulence, essentiality, physiochemical properties, and pathway analysis. Through these intensive filtering approaches, promising broad-spectrum therapeutic targets were identified. These targets were docked with FDA-approved compounds readily available on the ZINC database. Selected highly ranked ligands with inhibitory potential include dihydroergotamine and abiraterone acetate. The effectiveness of the ligands has been supported by molecular dynamics simulation of the ligand-protein complexes, instilling optimism that the identified lead compounds may serve as a robust basis for the development of safe and efficient drugs for TB treatment, subject to further lead optimization and subsequent experimental validation.

Towards multi-omics synthetic data integration.

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

Hybracter: enabling scalable, automated, complete and accurate bacterial genome assemblies.

Improvements in the accuracy and availability of long-read sequencing mean that complete bacterial genomes are now routinely reconstructed using hybrid (i.e. short- and long-reads) assembly approaches. Complete genomes allow a deeper understanding of bacterial evolution and genomic variation beyond single nucleotide variants. They are also crucial for identifying plasmids, which often carry medically significant antimicrobial resistance genes. However, small plasmids are often missed or misassembled by long-read assembly algorithms. Here, we present Hybracter which allows for the fast, automatic and scalable recovery of near-perfect complete bacterial genomes using a long-read first assembly approach. Hybracter can be run either as a hybrid assembler or as a long-read only assembler. We compared Hybracter to existing automated hybrid and long-read only assembly tools using a diverse panel of samples of varying levels of long-read accuracy with manually curated ground truth reference genomes. We demonstrate that Hybracter as a hybrid assembler is more accurate and faster than the existing gold standard automated hybrid assembler Unicycler. We also show that Hybracter with long-reads only is the most accurate long-read only assembler and is comparable to hybrid methods in accurately recovering small plasmids.