James Hadfield

Introduction: Influenza epidemics and pandemics cause significant morbidity and mortality. An effective response to a potential pandemic requires the infrastructure to rapidly detect, characterize, and potentially contain new and emerging influenza strains at a population level. The objective of this study is to use data gathered simultaneously from community and hospital sites to develop a model of how influenza enters and spreads in a population. Methods and Analysis: Starting in the 2018-19 season, we have been enrolling individuals with acute respiratory illness from community sites throughout the Seattle metropolitan area, including clinics, childcare facilities, Seattle-Tacoma International Airport, workplaces, college campuses, and homeless shelters. At these sites, we collect clinical data and mid-nasal swabs from individuals with at least two acute respiratory symptoms. Additionally, we collect residual nasal swabs and data from individuals who seek care for respiratory symptoms at four regional hospitals. Samples are tested using a multiplex molecular assay, and influenza whole genome sequencing is performed for samples with influenza detected. Geospatial mapping and computational modeling platforms are in development to characterize the regional spread of influenza and other respiratory pathogens. Ethics and Dissemination: The study was approved by the University of Washington’s Institutional Review Board. Results will be disseminated through talks at conferences, peer-reviewed publications, and on the study website ( www.seattleflu.org ). Article Summary: Strengths and limitations of this study: - Large-scale multiple-arm study of respiratory illness characterization with collection of samples from individuals in the community as well as in ambulatory care and hospital settings - Integration of sociodemographic, clinical, and geospatial data on a regional level - Multiplex molecular testing for multiple viral and bacterial pathogens and whole genome sequencing of influenza for detailed molecular epidemiologic characterization and transmission mapping - Geographically and socioeconomically diverse sampling of community-based acute respiratory illnesses

Chlamydia trachomatis is an obligate intracellular pathogen of humans, causing both the sexually transmitted infection, chlamydia, and the most common cause of infectious blindness, trachoma. The majority of sequenced C. trachomatis clinical isolates carry a 7.5-Kb plasmid, and it is becoming increasingly evident that this is a key determinant of pathogenicity. The discovery of the Swedish New Variant and the more recent Finnish variant highlight the importance of understanding the natural extent of variation in the plasmid. In this study we analysed 524 plasmid sequences from publicly available whole-genome sequence data. Single nucleotide polymorphisms (SNP) in each of the eight coding sequences (CDS) were identified and analysed. There were 224 base positions out of a total 7550 bp that carried a SNP, which equates to a SNP rate of 2.97%, nearly three times what was previously calculated. After normalising for CDS size, CDS8 had the highest SNP rate at 3.97% (i.e., number of SNPs per total number of nucleotides), whilst CDS6 had the lowest at 1.94%. CDS5 had the highest total number of SNPs across the 524 sequences analysed (2267 SNPs), whereas CDS6 had the least SNPs with only 85 SNPs. Calculation of the genetic distances identified CDS6 as the least variable gene at the nucleotide level (d = 0.001), and CDS5 as the most variable (d = 0.007); however, at the amino acid level CDS2 was the least variable (d = 0.001), whilst CDS5 remained the most variable (d = 0.013). This study describes the largest in-depth analysis of the C. trachomatis plasmid to date, through the analysis of plasmid sequence data mined from whole genome sequences spanning 50 years and from a worldwide distribution, providing insights into the nature and extent of existing variation within the plasmid as well as guidance for the design of future diagnostic assays. This is crucial at a time when single-target diagnostic assays are failing to detect natural mutants, putting those infected at risk of a serious long-term and life-changing illness.

Summary: Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualization platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, open to health professionals, epidemiologists, virologists and the public alike. Availability and implementation: All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

Summary: Understanding the spread and evolution of pathogens is important for effective public health measures and surveillance. Nextstrain consists of a database of viral genomes, a bioinformatics pipeline for phylodynamics analysis, and an interactive visualisation platform. Together these present a real-time view into the evolution and spread of a range of viral pathogens of high public health importance. The visualization integrates sequence data with other data types such as geographic information, serology, or host species. Nextstrain compiles our current understanding into a single accessible location, publicly available for use by health professionals, epidemiologists, virologists and the public alike. Availability and implementation: All code (predominantly JavaScript and Python) is freely available from github.com/nextstrain and the web-application is available at nextstrain.org.

Summary: Fully exploiting the wealth of data in current bacterial population genomics datasets requires synthesising and integrating different types of analysis across millions of base pairs in hundreds or thousands of isolates. Current approaches often use static representations of phylogenetic, epidemiological, statistical and evolutionary analysis results that are difficult to relate to one another. Phandango is an interactive application running in a web browser allowing fast exploration of large-scale population genomics datasets combining the output from multiple genomic analysis methods in an intuitive and interactive manner. Availability: Phandango is a web application freely available for use at https://jameshadfield.github.io/phandango and includes a diverse collection of datasets as examples. Source code together with a detailed wiki page is available on GitHub at https://github.com/jameshadfield/phandango Contact: jh22@sanger.ac.uk , sh16@sanger.ac.uk