SEARCH · Search bioRxiv

Results for “epidemiology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28Linked to original sources

bioRxiv2018

Genome-wide association analysis identifies 27 novel loci associated with uterine leiomyomata revealing common genetic origins with endometriosis

Uterine leiomyomata (UL), also known as uterine fibroids, are the most common neoplasms of the reproductive tract and the primary cause for hysterectomy, leading to considerable impact on womens lives as well as high economic burden1,2. Genetic epidemiologic studies indicate that heritable risk factors contribute to UL pathogenesis3. Previous genome-wide association studies (GWAS) identified five loci associated with UL at genome-wide significance (P < 5 x 10-8)4-6. We conducted GWAS meta-analysis in 20,406 cases and 223,918 female controls of white European ancestry, identifying 24 genome-wide significant independent loci; 17 replicated in an unrelated cohort of 15,068 additional cases and 43,587 female controls. Aggregation of discovery and replication studies (35,474 cases and 267,505 female controls) revealed six additional significant loci. Interestingly, four of the 17 loci identified and replicated in these analyses have also been associated with risk for endometriosis - another common gynecologic disorder. These findings increase our understanding of the biological mechanisms underlying UL development, and suggest overlapping genetic origins with endometriosis.

genomics↗

bioRxiv2018

TreeSwift: a massively scalable Python package for trees

Phylogenetic trees are essential to evolutionary biology, and numerous methods exist that attempt to extract phylogenetic information applicable to a wide range of disciplines, such as epidemiology and metagenomics. Currently, the three main Python packages for trees are Bio.Phylo, DendroPy, and the ETE Toolkit, but as dataset sizes grow, parsing and manipulating ultra-large trees becomes impractical for these tools. To address this issue, we present TreeSwift, a user-friendly and massively scalable Python package for traversing and manipulating trees that is ideal for algorithms performed on ultra-large trees.

bioinformatics↗

bioRxiv2018

Attenuation of influenza A virus disease severity by viral co-infection in a mouse model

Influenza viruses and rhinoviruses are responsible for a large number of acute respiratory viral infections in human populations and are detected as co-pathogens within hosts. Clinical and epidemiological studies suggest that co-infection by rhinovirus and influenza virus may reduce disease severity and that they may also interfere with each others spread within a host population. To determine how co-infection by these two unrelated respiratory viruses affects pathogenesis, we established a mouse model using a minor serogroup rhinovirus (RV1B) and mouse-adapted influenza A virus (PR8). Infection of mice with RV1B two days before PR8 reduced pathogenesis of mild to moderate, but not severe PR8 infections. Disease attenuation was associated with an early inflammatory response in the lungs and enhanced clearance of PR8. However, co-infection by RV1B did not reduce PR8 viral loads early in infection or inhibit replication of PR8 within respiratory epithelia or in vitro. Inflammation in co-infected mice remained focal, in comparison to diffuse inflammation and damage in the lungs of mice infected by PR8. These findings suggest that RV1B stimulates an early immune response that clears PR8 while limiting excessive pulmonary inflammation. The timing of RV1B co-infection was a critical determinant of protection, suggesting that sufficient time is needed to induce this response. Finally, disease attenuation was not unique to RV1B: co-infection by a murine coronavirus two days before PR8 also reduced disease severity. This model will be critical for understanding the mechanisms responsible for attenuation of influenza disease during co-infection by unrelated respiratory viruses.

microbiology↗

bioRxiv2018

Methicillin-resistant Staphylococcus aureus CC22-MRSA-IV as an agent of dairy cow intramammary infections.

Methicillin-resistant S. aureus (MRSA) lineages have become major responsible of healthcare-and community-associated infections in human population. Bovine MRSA are sporadically detected in the dairy herd, but its presence might enhance the risk of zoonosis. Some lineages are able to lose the specific host tropism, being easily transmitted from animals to humans and vice-versa. The present study aims at clarifying the epidemiology of MRSA intramammary infections in a closed dairy herd, which was running a mastitis control program since years. Quarter milk samples were collected from all lactating cows once a week for 9 weeks and bacteriologically tested. At the end of the follow-up period, also a self taken nasal swab of the milker was analysed. Three cows (12.5%) were MRSA positive, a four showed a transient infection and an MRSA was isolated also from the milker. Somatic cell counts of the infected quarters fluctuated from 1,000 to 1,800,000 cells/mL. All isolates were genotyped using DNA microarrays and identified as the epidemic UK-EMRSA-15 grouping in CC22. All strains carried the genes for {beta}-lactam and macrolide resistance. The milker isolate differed from cow isolates mainly for the absence of the untruncated {beta}-haemolysin and the presence of the immune evasion cluster. The milker had been volunteering in a nursing home since months, thus playing the role of MRSA vector into the herd. Our results showed the adaptive capacity of such MRSA to the bovine host. Therefore, we suggest that CC22-MRSA should be regarded as a potential cause of humanosis in dairy cattle herds.

microbiology↗

bioRxiv2018

Evaluation of whole genome sequencing for the identification and typing of Vibrio cholerae

Epidemiological and microbiological data on Vibrio cholerae isolated between 2004 and 2017 (n=836) and held in the Public Health England culture archive were reviewed. The traditional biochemical species identification and serological typing results were compared with the genome derived species identification and serotype for a sub-set of isolates (n=152). Of the 836 isolates, 750 (89.7%) were from faecal specimens, 206 (24.6%) belonged to serogroup O1 and seven (0.8%) were serogroup O139, and 792 (94.7%) isolates from patients reporting recent travel abroad, most commonly to India (n=209) and Pakistan (n=104). Of the 152 isolates of V. cholerae speciated by kmer identification, 149 (98.1%) were concordant with the traditional biochemical approach. Traditional serotyping results were 100% concordant with the whole genome sequencing (WGS) analysis for identification of serogroups O1 and O139 and Classical and El Tor biotypes. ctxA was detected in all isolates of V. cholerae O1 El Tor and O139 belonging to sequence type (ST) 69, and in V. cholerae O1 Classical variants belonging to ST73. A phylogeny of isolates belonging to ST69 from UK travellers clustered geographically, with isolates from India and Pakistan located on separate branches. Moving forward, WGS data from UK travellers will contribute to global surveillance programs, and the monitoring of emerging threats to public health and the global dissemination of pathogenic lineages. At the national level, these WGS data will inform the timely reinforcement of direct public health messaging to travellers and mitigate the impact of imported infections and the associated risks to public health.

microbiology↗

bioRxiv2018

Patterns of tumor progression predict small and tissue-specific tumor-originating niches

Cancer development is a multistep process in which cells increase in malignancy through progressive alterations. The early phase of this process is hardly observable which aggravates an understanding of later tumor development. We shed light on this initial phase with a cell-based stochastic model calibrated with epidemiological data from the tissue scale. Our model allows to estimate the number of tumor cells needed for tumor formation in human tissues based on data on the diagnosed ratios of benign and malignant tumors. We find that the minimal number of cells needed for tumor formation is surprisingly small and largely depends on the tissue type. Our results point towards the existence of tumor-originating niches in which the fate of tumor development is early decided. Our estimate for the human colon agrees well with the size of the stem cell niche in colonic crypts. Our estimates might help to identify the tumor-originating cell type, e.g. our analysis suggests for glioblastoma that the tumors originate from a cell type competing in a range of 300 - 1900 cells.\n\nSummaryWe estimate the number of tumor cells needed for tumor formation in human tissues and propose the existence of small and tissue-specific tumor-originating niches which might help to find tumor-originating cell types, in particular in glioblastoma.

cancer biology↗

bioRxiv2018

Repurposing the quinoline antibiotic nitroxoline to treat infections caused by the brain-eating amoeba Balamuthia mandrillaris

Balamuthia mandrillaris is a pathogenic free-living amoeba that causes a rare but almost always fatal infection of the central nervous system called granulomatous amoebic encephalitis (GAE). Two distinct forms of B. mandrillaris - a proliferative trophozoite form and a non-proliferative cyst form, which is highly resistant to harsh physical and chemical conditions - have been isolated from environmental samples worldwide and are both observed in infected tissue. Patients suffering from GAE are typically treated with aggressive and prolonged multi-drug regimens often including the antimicrobial agents miltefosine and pentamidine isethionate. However, survival rates remain low and studies evaluating the susceptibility of B. mandrillaris to these compounds and other potential therapeutics are limited. To address the need for more effective treatments, we screened 2,177 clinically-approved compounds for in vitro activity against B. mandrillaris. The quinoline antibiotic nitroxoline, which has safely been used in humans to treat urinary tract infections, was identified as a lead compound. We show that nitroxoline inhibits both trophozoites and cysts at low micromolar concentrations, which are within a physiologically relevant range. We compare the in vitro efficacy of nitroxoline to drugs currently used in the standard of care for GAE and find that nitroxoline is the most potent and selective inhibitor of B. mandrillaris tested. Furthermore, we demonstrate that nitroxoline prevents B. mandrillaris-mediated destruction of host cells in cultured fibroblast and primary brain explant models also at physiologically relevant concentrations. Together, our findings indicate that nitroxoline is a promising candidate for repurposing as a novel treatment of B. mandrillaris infections.\n\nImportanceBalamuthia mandrillaris is responsible for hundreds of reported cases of amoebic encephalitis, the majority of which have been fatal. Despite being an exceptionally deadly pathogen, B. mandrillaris is understudied, leaving many open questions regarding epidemiology, diagnosis, and treatment. Due to the lack of effective drugs to fight B. mandrillaris infections, mortality rates remain high even for patients receiving intensive care. This study addresses the need for new anti-amoebic drugs using a high-throughput screening approach to identify novel B. mandrillaris inhibitors. The most promising candidate identified was the quinoline antibiotic nitroxoline, which has a long history of safe use in humans. We show that nitroxoline kills B. mandrillaris at physiologically relevant concentrations and exhibits greater potency and selectivity than drugs commonly used in the current standard of care. The findings we present demonstrate the potential of nitroxoline to be an important new tool in the treatment of life threatening B. mandrillaris infections.

pharmacology and toxicology↗

bioRxiv2018

Rapid Therapeutic Recommendations in the Context of a Global Public Health Crisis using Translational Bioinformatics Approaches: A proof-of-concept study using Nipah Virus Infection

We live in a world of emerging new diseases and old diseases resurging in more aggressive forms. Drug development by pharmaceutical companies is a market-driven and costly endeavor, and thus it is often a challenge when drugs are needed for diseases endemic only to certain regions or which affect only a few patients. However, biomedical open data is accessible and reusable for reanalysis and generation of a new hypotheses and discovery. In this study, we leverage biomedical data and tools to analyze available data on Nipah Virus (NiV) infection. NiV infection is an emerging zoonosis that is transmissible to humans and is associated with high mortality rates. In this study, explored the application of computational drug repositioning and chemogenomic enrichment analyses using host transcriptome data to match drugs that could reverse the virus-induced gene signature. We performed analyses using two gene signatures: i) A previously published gene signature (n=34), and ii) a gene signature generated using the characteristic direction method (n= 5,533). Our predictive framework suggests that several drugs including FDA approved therapies like beclometasone, trihexyphenidyl, S-propranolol etc. could modulate the NiV infection induced gene signatures in endothelial cells. A target specific analysis of CXCL10 also suggests the potential application of Eldelumab, an investigative therapy for Crohns disease and ulcerative colitis, as a putative candidate for drug repositioning. To conclude, we also discuss challenges and opportunities in clinical trials (n-of-1 and adaptive trials) for repositioned drugs. Further follow-up studies including biochemical assays and clinical trials are required to identify effective therapies for clinical use. Our proof-of-concept study highlights that translational bioinformatics methods including gene expression analyses and computational drug repositioning could augment epidemiological investigations in the context of an emerging disease with no effective treatment.

microbiology↗

bioRxiv2018

Ancient ancestry informative markers for identifying fine-scale ancient population structure in Eurasians

The rapid accumulation of ancient human genomes from various areas and time periods potentially allows the expansion of studies of biodiversity, biogeography, forensics, population history, and epidemiology into past populations. However, most ancient DNA (aDNA) data were generated through microarrays designed for modern-day populations known to misrepresent the population structure. Past studies addressed these problems using ancestry informative markers (AIMs). However, it is unclear whether AIMs derived from contemporary human genomes can capture ancient population structure and whether AIM finding methods are applicable to ancient DNA (aDNA) provided that the high missingness rates in ancient, oftentimes haploid, DNA can also distort the population structure. Here, we define ancient AIMs (aAIMs) and develop a framework to evaluate established and novel AIM-finding methods in identifying the most informative markers. We show that aAIMs identified by a novel principal component analysis (PCA)-based method outperforms all competing methods in classifying ancient individuals into populations and identifying admixed individuals. In some cases, predictions made using the aAIMs were more accurate than those made with a complete marker set. We discuss the features of the ancient Eurasian population structure and strategies to identify aAIMs. This work informs the design of population microarrays and the interpretation of aDNA results.

genetics↗

bioRxiv2018

Precise prediction of antibiotic resistance in Escherichia coli from full genome sequences

The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The diagnosis is typically made by measuring a few known determinants previously identified from whole genome sequencing, and thus is restricted to existing information on biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average F1 score of 0.88 on held-out data (range 0.66-0.96). While the best models most frequently employed all inputs, an average F1 score of 0.73 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and failed to improve prediction for ten out of 11 antibiotics. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data are informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic.\n\nSummaryOne of the major health threats of 21st century is emergence of antibiotic resistance. To manage its economic impact, efforts are made to develop novel diagnostic tools that rapidly detect resistant strains in clinical settings. In our study, we employed a range machine learning tools to predict antibiotic resistance from whole genome sequencing data for E. coli. We used the presence or absence of genes, population structure and isolation year of isolates as predictors, and could attain average precision of 0.93 and recall of 0.83, without prior knowledge about the causal mechanisms. These results demonstrate the potential application of machine learning methods as a diagnostic tool in healthcare settings.

bioinformatics↗

bioRxiv2018

Parallel sexual and parasexual population genomic structure in Trypanosoma cruzi

Genetic exchange and hybridization in parasitic organisms is fundamental to the exploitation of new hosts and host populations. Variable mating frequency often coincides with strong metapopulation structure, where patchy selection or demography may favor different reproductive modes. Evidence for genetic exchange in Trypanosoma cruzi over the last 30 years has been limited and inconclusive. The reproductive modes of other medically important trypanosomatids are better established, although little is known about their variability on a spatio-temporal scale. Targeting a contemporary focus of T. cruzi transmission in southern Ecuador, we present compelling evidence from 45 sequenced genomes that T. cruzi (discrete typing unit I) maintains sexual populations alongside others that represent clonal bursts of parasexual origin. Strains from one site exhibit genome-wide Hardy-Weinberg equilibrium and intra-chromosomal linkage decay consistent with meiotic reproduction. Strains collected from adjacent areas (>6 km) show excess heterozygosity, near-identical haplo-segments, common mitochondrial sequences and levels of aneuploidy incompatible with Mendelian sex. Certain individuals exhibit trisomy in as many as fifteen chromosomes. Others present fewer, yet shared, aneuploidies reminiscent of mitotic genome erosion and parasexual genetic exchange. Genomic and intra-genomic phylogenetics as well as haplotype co-ancestry analyses indicate a clear break in gene-flow between these distinct populations, despite the fact that they occasionally co-occur in vectors and hosts. We propose biological explanations for the fine-scale disconnectivity we observe and discuss the epidemiological consequences of flexible reproductive modes and their genomic architecture for this medically important parasite.

evolutionary biology↗

bioRxiv2018

A metapopulation model of the spread of the Devil Facial Tumour Disease predicts the long term collapse of its host but not its extinction

The Devil Facial Tumour Disease (DFTD), a unique case of a transmissible cancer, had a devastating effect on its host, the Tasmanian Devil. Current estimates of its density are at roughly 20% of the pre-disease state, and single-population epidemiological models have predicted the likely extinction of the host. Here we take advantage of extensive surveys across Tasmania providing data on the spatial and temporal spread of DFTD, and investigate the dynamics of this host-pathogen system using a spatial metapopulation model. We first confirm a most likely origin of DFTD in the north-east corner of the island, and then use the inferred dynamics to predict the fate of the species. We find that our medium-term predictions match additional data not used for fitting, and that on the longterm, Tasmanian Devils are predicted to coexist with the tumour. The key process allowing persistence is the repeated reinvasion of extinct patches from neighbouring areas where the disease has flared up and died out, resulting in a dynamic equilibrium with different levels of spatial heterogeneity. However, this dynamic equilibrium is predicted to keep this apex predator at about 9 % of its original density, with possible dramatic effects on the Tasmanian ecosystem.

ecology↗

bioRxiv2018

Influenza A(H1N1)pdm09 M and HA segments sequences from Rio Grande do Sul, Brazil

The influenza virus is one of the most critical viruses in epidemiology. The 2009 pandemic was caused by a reassortment of the human-avian-swine virus with eight RNA segments responsible for all virus proteins. Segment 7 codifies for matrix proteins M1 and M2. These proteins exhibited low mutation rate because the matrix is fundamental for virion encapsidation and ion channel formation. However, hemagglutinin (HA) segment 4 is one of the most important segments for virulence and hence, is more studied. Brazil had many influenza virus infection cases just before 2009 and from 2011 to 2015, particularly in the Rio Grande do Sul (RS) State. Two hundred samples obtained during the pandemic were used for amplification and sequencing of the viral genome; a total of 19 M and 17 HA amplified segments were sequenced. Sequencing of the M fragment showed that RS has a virus origin different from that in Eastern Asia, Western Europe, USA, and Central America (Mexico and Nicaragua). All the sequences showed amantadine resistance (S31N) and one was out of the phylogenetic tree (Brazil/RS-3335/2009) due to high mutation rate. RS-3335 was the only sample obtained from a patient who died. Many migratory birds that flock to RS are from Europe, Asia, and USA, which could explain this rate of mutation. Insertions and deletions were found in the M1 protein in these samples. The HA sequences showed worldwide spread and less diversity than the M sequences in this study. The most divergent sample was Brazil/RS-3093/2009 that showed mutations in the sialic acid ligation site.

microbiology↗

bioRxiv2018

Bayesian inference of ancestral dates on bacterial phylogenetic trees

The sequencing and comparative analysis of a collection of bacterial genomes from a single species or lineage of interest can lead to key insights into its evolution, ecology or epidemiology. The tool of choice for such a study is often to build a phylogenetic tree, and more specifically when possible a dated phylogeny, in which the dates of all common ancestors are estimated. Here we propose a new Bayesian methodology to construct dated phylogenies which is specifically designed for bacterial genomics. Unlike previous Bayesian methods aimed at building dated phylogenies, we consider that the phylogenetic relationships between the genomes have been previously evaluated using a standard phylogenetic method, which makes our methodology much faster and scalable. This two-steps approach also allows us to directly exploit existing phylogenetic methods that detect bacterial recombination, and therefore to account for the effect of recombination in the construction of a dated phylogeny. We analysed many simulated datasets in order to benchmark the performance of our approach in a wide range of situations. Furthermore, we present applications to three different real datasets from recent bacterial genomic studies. Our methodology is implemented in a R package called BactDating which is freely available for download at https://github.com/xavierdidelot/BactDating.

bioinformatics↗

bioRxiv2018

SIV/SHIV-Zika coinfection does not alter disease pathogenesis in adult non-pregnant Rhesus Macaques

Due to the large geographical overlap of populations exposed to Zika virus (ZIKV) and human immunodeficiency virus (HIV), understanding disease pathogenesis in such coinfections is urgently needed. We used chronically infected simian immunodeficiency virus and chimeric simian human immunodeficiency virus (SIV/SHIV) macaques and inoculated with ZIKV. Plasma viral loads of both SIV/SHIV and ZIKV showed no significant changes as compared to ZIKV alone-infected animals. Tissue clearance of ZIKV was observed similarly. Furthermore, minimal changes in cytokines/chemokines were observed. Collectively, these data suggest that coinfection may not alter disease pathogenesis and warrants large HIV-ZIKV epidemiological studies to validate these findings.\n\nAuthor SummaryThe co-infection incidence of human immunodeficiency virus (HIV) infection and neglected tropical infectious diseases is increasing due to the large geographical overlap of populations exposed to both of these viruses. Thus, researching on such coinfection is of particular importance. In this study, we investigated HIV-ZIKV coinfection dynamics in adult non-pregnant Rhesus Macaques model chronically infected with simian immunodeficiency virus (SIV) - or chimeric simian human immunodeficiency virus (SHIV). We found that post ZIKV inoculation, plasma viral loads were similar to ZIKV alone infected animals in addition to minimal changes of cytokines. Dynamics of SIV and SHIV also did not change. Tissue clearance of ZIKV was found 67 months later. Our findings provide insights into HIV-ZKIV coinfection to determine the alteration of their pathogenesis.

microbiology↗

bioRxiv2018

Coffee polyphenols prevent cognitive dysfunction and suppress amyloid β plaques in APP/PS2 transgenic mouse

Epidemiological studies have found that habitual coffee consumption may reduce the risk of Alzheimers disease. Coffee contains numerous phenolic compounds (coffee polyphenols) such as chlorogenic acids. However, evidence demonstrating the contribution of chlorogenic acids in preventing cognitive dysfunction induced by Alzheimers disease is limited. In this study, we investigated the effect of chlorogenic acids on prevention of cognitive dysfunction in APP/PS2 transgenic mouse model of Alzheimers disease. Five-week-old APP/PS2 mice were administered a diet supplemented with coffee polyphenols daily for 5 months. The memory and cognitive function of mice was determined using the novel object recognition test, the Morris water maze test, and the step-through passive avoidance test. We found that chronic treatment with coffee polyphenols prevented cognitive dysfunction and significantly reduced hippocampal A{beta} deposition. We then determined the effect of 5-caffeoylquinic acid, one of the primary components of coffee polyphenols, on A{beta} formation. 5-Caffeoylquinic acid did not inhibit A{beta} fibrillation, but degraded A{beta} fibrils in a dose-dependent manner. In conclusion, these results demonstrate that coffee polyphenols prevented cognitive deficits and alleviated A{beta} plaque deposition via disaggregation of A{beta} in APP/PS2 mouse.

animal behavior and cognition↗

bioRxiv2018

A stochastic model of metastatic bottleneck predicts patient outcome and therapy response

Metastases are responsible for 90% of cancer-related deaths. Initiation of metastases, where newly seeded tumor cells expand into colonies, presents a tremendous bottleneck to metastasis formation. Despite its clinical importance, our understanding of this process is very limited. Here, we propose a simple stochastic model assuming that the initiating metastatic cells proliferate faster when surrounded by more of their kind. The model quantifies the severity of metastatic bottleneck as the probability that the seeded colony survives. Based on this model, we derive how metastasis occurrence depends on primary tumor size and affects patient outcome. Our predictions agree with epidemiological data for thirteen cancer types. The model predicts that impact of treatment decisions depends both on the primary tumor size and on the severity of the metastatic bottleneck, and that medical interventions that tighten the bottleneck would be much more efficient than therapies that decrease overall tumor burden, such as chemotherapy.

cancer biology↗

bioRxiv2018

Oxford Nanopore MinION genome sequencer: performance characteristics, optimised analysis workflow, phylogenetic analysis and prediction of antimicrobial resistance in Neisseria gonorrhoeae

Antimicrobial resistant (AMR) Neisseria gonorrhoeae strains are common and compromise gonorrhoea treatment internationally. Rapid identification and characterisation of AMR gonococcal strains could ensure appropriate and even personalised treatment, and support identification and investigation of gonorrhoea outbreaks in nearly real-time. Whole-genome sequencing is ideal for investigation of the emergence and dissemination of AMR determinants that predict AMR in the gonococcal population and spread of AMR strains in the human population. The novel, rapid and revolutionary long-read sequencer MinION is a small hand-held device that can generate bacterial genomes within one day. However, the accuracy of MinION reads has been suboptimal for many objectives and the MinION has not been evaluated for gonococci. In this first MinION study for gonococci, we show that MinION-derived sequences analysed with existing open-access, web-based sequence analysis tools are not sufficiently accurate to identify key gonococcal AMR determinants. Nevertheless, using an in house-developed CLC Genomics Workbench, we show that ONT-derived sequences can be used for accurate prediction of decreased susceptibility or resistance to recommended therapeutic antimicrobials. We also show that the ONT-derived sequences can be useful for rapid phylogenomic-based molecular epidemiological investigations, and, in hybrid assemblies with Illumina sequences, for producing contiguous assemblies and finished reference genomes.

genomics↗