Microreact: visualizing and sharing data for genomic epidemiology and phylogeography

Visualization is frequently used to aid our interpretation of complex datasets. Within microbial genomics, visualizing the relationships between multiple genomes as a tree provides a framework onto which associated data (geographical, temporal, phenotypic and epidemiological) are added to generate hypotheses and to explore the dynamics of the system under investigation. Selected static images are then used within publications to highlight the key findings to a wider audience. However, these images are a very inadequate way of exploring and interpreting the richness of the data. There is, therefore, a need for flexible, interactive software that presents the population genomic outputs and associated data in a user-friendly manner for a wide range of end users, from trained bioinformaticians to front-line epidemiologists and health workers. Here, we present Microreact, a web application for the easy visualization of datasets consisting of any combination of trees, geographical, temporal and associated metadata. Data files can be uploaded to Microreact directly via the web browser or by linking to their location (e.g. from Google Drive/Dropbox or via API), and an integrated visualization via trees, maps, timelines and tables provides interactive querying of the data. The visualization can be shared as a permanent web link among collaborators, or embedded within publications to enable readers to explore and download the data. Microreact can act as an end point for any tool or bioinformatic pipeline that ultimately generates a tree, and provides a simple, yet powerful, visualization method that will aid research and discovery and the open sharing of datasets

A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch.

As whole-genome sequencing capacity becomes increasingly decentralized, there is a growing opportunity for collaboration and the sharing of surveillance data within and between countries to inform typhoid control policies. This vision requires free, community-driven tools that facilitate access to genomic data for public health on a global scale. Here we present the Pathogenwatch scheme for Salmonella enterica serovar Typhi (S. Typhi), a web application enabling the rapid identification of genomic markers of antimicrobial resistance (AMR) and contextualization with public genomic data. We show that the clustering of S. Typhi genomes in Pathogenwatch is comparable to established bioinformatics methods, and that genomic predictions of AMR are highly concordant with phenotypic susceptibility data. We demonstrate the public health utility of Pathogenwatch with examples selected from >4,300 public genomes available in the application. Pathogenwatch provides an intuitive entry point to monitor of the emergence and spread of S. Typhi high risk clones

Visualizing variation within global pneumococcal sequence clusters (GPSCS) and country population snapshots to contextualize pneumococcal isolates

Knowledge of pneumococcal lineages, their geographic distribution and antibiotic resistance patterns, can give insights into global pneumococcal disease. We provide interactive bioinformatic outputs to explore such topics, aiming to increase dissemi-nation of genomic insights to the wider community, without the need for specialist training. We prepared 12 country-specific phylogenetic snapshots, and international phylogenetic snapshots of 73 common Global Pneumococcal Sequence Clusters (GPSCs) previously defined using PopPUNK, and present them in Microreact. Gene presence and absence defined using Roary, and recombination profiles derived from Gubbins are presented in Phandango for each GPSC. Temporal phylogenetic signal was assessed for each GPSC using BactDating. We provide examples of how such resources can be used. In our example use of a country-specific phylogenetic snapshot we determined that serotype 14 was observed in nine unrelated genetic backgrounds in South Africa. The international phylogenetic snapshot of GPSC9, in which most serotype 14 isolates from South Africa were observed, highlights that there were three independent sub-clusters represented by South African serotype 14 isolates. We estimated from the GPSC9-dated tree that the sub-clusters were each established in South Africa during the 1980s. We show how recombination plots allowed the identification of a 20 kb recombination spanning the capsular polysaccharide locus within GPSC97. This was consistent with a switch from serotype 6A to 19A estimated to have occured in the 1990s from the GPSC97-dated tree. Plots of gene presence/absence of resistance genes (tet, erm, cat) across the GPSC23 phylogeny were consistent with acquisition of a composite transposon. We estimated from the GPSC23-dated tree that the acquisition occurred between 1953 and 1975. Finally, we demonstrate the assignment of GPSC31 to 17 externally generated pneumococcal serotype 1 assemblies from Utah via Pathogenwatch. Most of the Utah isolates clustered within GPSC31 in a USA-specific clade with the most recent common ancestor estimated between 1958 and 1981. The resources we have provided can be used to explore to data, test hypothesis and generate new hypotheses. The accessible assignment of GPSCs allows others to contextualize their own collections beyond the data presented here.Fil: Gladstone, Rebecca A.. Wellcome Sanger Institute; Reino UnidoFil: Lo, Stephanie W.. Wellcome Sanger Institute; Reino UnidoFil: Goater, Richard. Wellcome Sanger Institute; Reino Unido. University of Oxford; Reino UnidoFil: Yeats, Corin. Wellcome Sanger Institute; Reino Unido. University of Oxford; Reino UnidoFil: Taylor, Ben. Wellcome Sanger Institute; Reino Unido. University of Oxford; Reino UnidoFil: Hadfield, James. Fred Hutchinson Cancer Research Center; Estados UnidosFil: Lees, John A.. Imperial College London; Reino UnidoFil: Croucher, Nicholas J.. Imperial College London; Reino UnidoFil: van Tonder, Andries. Wellcome Sanger Institute; Reino Unido. University of Cambridge; Estados UnidosFil: Bentley, Leon J.. Wellcome Sanger Institute; Reino UnidoFil: Quah, Fu Xiang. Wellcome Sanger Institute; Reino UnidoFil: Blaschke, Anne J.. University of Utah; Estados UnidosFil: Pershing, Nicole L.. University of Utah; Estados UnidosFil: Byington, Carrie L.. University of California; Estados UnidosFil: Balaji, Veeraraghavan. Christian Medical College; IndiaFil: Hryniewicz, Waleria. National Medicines Institute; PoloniaFil: Sigauque, Betuel. Instituto Nacional de Saude Maputo; MozambiqueFil: Ravikumar, K. L.. Kempegowda Institute Of Medical Sciences; IndiaFil: Grassi Almeida, Samanta Cristine. Adolfo Lutz Institute; BrasilFil: Ochoa, Theresa J.. Universidad Peruana Cayetano Heredia; PerúFil: Ho, Pak Leung. The University Of Hong Kong; Hong KongFil: du Plessis, Mignon. National Institute for Communicable Diseases; SudáfricaFil: Ndlangisa, Kedibone M.. National Institute for Communicable Diseases; SudáfricaFil: Cornick, Jennifer. Malawi liverpool wellcome Trust Clinical Research Programme; MalauiFil: Kwambana Adams, Brenda. Colegio Universitario de Londres; Reino Unido. Medical Research Council Unit The Gambia at The London School of Hygiene & Tropical Medicine; GambiaFil: Benisty, Rachel. Ben Gurion University of the Negev; IsraelFil: Nzenze, Susan A.. University of the Witwatersrand; SudáfricaFil: Madhi, Shabir A.. University of the Witwatersrand; SudáfricaFil: Hawkins, Paulina A.. Emory University; Estados UnidosFil: Faccone, Diego Francisco. Dirección Nacional de Institutos de Investigación. Administración Nacional de Laboratorios e Institutos de Salud. Instituto Nacional de Enfermedades Infecciosas. Área de Antimicrobianos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch.

As whole-genome sequencing capacity becomes increasingly decentralized, there is a growing opportunity for collaboration and the sharing of surveillance data within and between countries to inform typhoid control policies. This vision requires free, community-driven tools that facilitate access to genomic data for public health on a global scale. Here we present the Pathogenwatch scheme for Salmonella enterica serovar Typhi (S. Typhi), a web application enabling the rapid identification of genomic markers of antimicrobial resistance (AMR) and contextualization with public genomic data. We show that the clustering of S. Typhi genomes in Pathogenwatch is comparable to established bioinformatics methods, and that genomic predictions of AMR are highly concordant with phenotypic susceptibility data. We demonstrate the public health utility of Pathogenwatch with examples selected from >4,300 public genomes available in the application. Pathogenwatch provides an intuitive entry point to monitor of the emergence and spread of S. Typhi high risk clones

Public health surveillance of multidrug-resistant clones of Neisseria gonorrhoeae in Europe: a genomic survey.

BACKGROUND: Traditional methods for molecular epidemiology of Neisseria gonorrhoeae are suboptimal. Whole-genome sequencing (WGS) offers ideal resolution to describe population dynamics and to predict and infer transmission of antimicrobial resistance, and can enhance infection control through linkage with epidemiological data. We used WGS, in conjunction with linked epidemiological and phenotypic data, to describe the gonococcal population in 20 European countries. We aimed to detail changes in phenotypic antimicrobial resistance levels (and the reasons for these changes) and strain distribution (with a focus on antimicrobial resistance strains in risk groups), and to predict antimicrobial resistance from WGS data. METHODS: We carried out an observational study, in which we sequenced isolates taken from patients with gonorrhoea from the European Gonococcal Antimicrobial Surveillance Programme in 20 countries from September to November, 2013. We also developed a web platform that we used for automated antimicrobial resistance prediction, molecular typing (N gonorrhoeae multi-antigen sequence typing [NG-MAST] and multilocus sequence typing), and phylogenetic clustering in conjunction with epidemiological and phenotypic data. FINDINGS: The multidrug-resistant NG-MAST genogroup G1407 was predominant and accounted for the most cephalosporin resistance, but the prevalence of this genogroup decreased from 248 (23%) of 1066 isolates in a previous study from 2009-10 to 174 (17%) of 1054 isolates in this survey in 2013. This genogroup previously showed an association with men who have sex with men, but changed to an association with heterosexual people (odds ratio=4·29). WGS provided substantially improved resolution and accuracy over NG-MAST and multilocus sequence typing, predicted antimicrobial resistance relatively well, and identified discrepant isolates, mixed infections or contaminants, and multidrug-resistant clades linked to risk groups. INTERPRETATION: To our knowledge, we provide the first use of joint analysis of WGS and epidemiological data in an international programme for regional surveillance of sexually transmitted infections. WGS provided enhanced understanding of the distribution of antimicrobial resistance clones, including replacement with clones that were more susceptible to antimicrobials, in several risk groups nationally and regionally. We provide a framework for genomic surveillance of gonococci through standardised sampling, use of WGS, and a shared information architecture for interpretation and dissemination by use of open access software. FUNDING: The European Centre for Disease Prevention and Control, The Centre for Genomic Pathogen Surveillance, Örebro University Hospital, and Wellcome

Globetrotting strangles: the unbridled national and international transmission of Streptococcus equi between horses.

The equine disease strangles, which is characterized by the formation of abscesses in the lymph nodes of the head and neck, is one of the most frequently diagnosed infectious diseases of horses around the world. The causal agent, Streptococcus equi subspecies equi, establishes a persistent infection in approximately 10 % of animals that recover from the acute disease. Such 'carrier' animals appear healthy and are rarely identified during routine veterinary examinations pre-purchase or transit, but can transmit S. equi to naïve animals initiating new episodes of disease. Here, we report the analysis and visualization of phylogenomic and epidemiological data for 670 isolates of S. equi recovered from 19 different countries using a new core-genome multilocus sequence typing (cgMLST) web bioresource. Genetic relationships among all 670 S. equi isolates were determined at high resolution, revealing national and international transmission events that drive this endemic disease in horse populations throughout the world. Our data argue for the recognition of the international importance of strangles by the Office International des Épizooties to highlight the health, welfare and economic cost of this disease. The Pathogenwatch cgMLST web bioresource described herein is available for tailored genomic analysis of populations of S. equi and its close relative S. equi subspecies zooepidemicus that are recovered from horses and other animals, including humans, throughout the world. This article contains data hosted by Microreact

Rapid Genomic Characterization and Global Surveillance of Klebsiella Using Pathogenwatch.

BACKGROUND: Klebsiella species, including the notable pathogen K. pneumoniae, are increasingly associated with antimicrobial resistance (AMR). Genome-based surveillance can inform interventions aimed at controlling AMR. However, its widespread implementation requires tools to streamline bioinformatic analyses and public health reporting. METHODS: We developed the web application Pathogenwatch, which implements analytics tailored to Klebsiella species for integration and visualization of genomic and epidemiological data. We populated Pathogenwatch with 16 537 public Klebsiella genomes to enable contextualization of user genomes. We demonstrated its features with 1636 genomes from 4 low- and middle-income countries (LMICs) participating in the NIHR Global Health Research Unit (GHRU) on AMR. RESULTS: Using Pathogenwatch, we found that GHRU genomes were dominated by a small number of epidemic drug-resistant clones of K. pneumoniae. However, differences in their distribution were observed (eg, ST258/512 dominated in Colombia, ST231 in India, ST307 in Nigeria, ST147 in the Philippines). Phylogenetic analyses including public genomes for contextualization enabled retrospective monitoring of their spread. In particular, we identified hospital outbreaks, detected introductions from abroad, and uncovered clonal expansions associated with resistance and virulence genes. Assessment of loci encoding O-antigens and capsule in K. pneumoniae, which represent possible vaccine candidates, showed that 3 O-types (O1-O3) represented 88.9% of all genomes, whereas capsule types were much more diverse. CONCLUSIONS: Pathogenwatch provides a free, accessible platform for real-time analysis of Klebsiella genomes to aid surveillance at local, national, and global levels. We have improved representation of genomes from GHRU participant countries, further facilitating ongoing surveillance

Visualizing variation within Global Pneumococcal Sequence Clusters (GPSCs) and country population snapshots to contextualize pneumococcal isolates.

Knowledge of pneumococcal lineages, their geographic distribution and antibiotic resistance patterns, can give insights into global pneumococcal disease. We provide interactive bioinformatic outputs to explore such topics, aiming to increase dissemination of genomic insights to the wider community, without the need for specialist training. We prepared 12 country-specific phylogenetic snapshots, and international phylogenetic snapshots of 73 common Global Pneumococcal Sequence Clusters (GPSCs) previously defined using PopPUNK, and present them in Microreact. Gene presence and absence defined using Roary, and recombination profiles derived from Gubbins are presented in Phandango for each GPSC. Temporal phylogenetic signal was assessed for each GPSC using BactDating. We provide examples of how such resources can be used. In our example use of a country-specific phylogenetic snapshot we determined that serotype 14 was observed in nine unrelated genetic backgrounds in South Africa. The international phylogenetic snapshot of GPSC9, in which most serotype 14 isolates from South Africa were observed, highlights that there were three independent sub-clusters represented by South African serotype 14 isolates. We estimated from the GPSC9-dated tree that the sub-clusters were each established in South Africa during the 1980s. We show how recombination plots allowed the identification of a 20 kb recombination spanning the capsular polysaccharide locus within GPSC97. This was consistent with a switch from serotype 6A to 19A estimated to have occured in the 1990s from the GPSC97-dated tree. Plots of gene presence/absence of resistance genes (tet, erm, cat) across the GPSC23 phylogeny were consistent with acquisition of a composite transposon. We estimated from the GPSC23-dated tree that the acquisition occurred between 1953 and 1975. Finally, we demonstrate the assignment of GPSC31 to 17 externally generated pneumococcal serotype 1 assemblies from Utah via Pathogenwatch. Most of the Utah isolates clustered within GPSC31 in a USA-specific clade with the most recent common ancestor estimated between 1958 and 1981. The resources we have provided can be used to explore to data, test hypothesis and generate new hypotheses. The accessible assignment of GPSCs allows others to contextualize their own collections beyond the data presented here