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. 2025 May 9;17(1):51.
doi: 10.1186/s13073-025-01470-4.

The TyphiNET data visualisation dashboard: unlocking Salmonella Typhi genomics data to support public health

Zoe A Dyson #  1   2   3 , Louise Cerdeira #  4 , Vandana Sharma  4 , Megan E Carey  4 , Kathryn E Holt  5   6 ; Global Typhoid Genomics Consortium
Collaborators, Affiliations

The TyphiNET data visualisation dashboard: unlocking Salmonella Typhi genomics data to support public health

Zoe A Dyson et al. Genome Med. .

Abstract

Background: Salmonella enterica subspecies enterica serovar Typhi (abbreviated as 'Typhi') is the bacterial agent of typhoid fever. Effective antimicrobial therapy reduces complications and mortality; however, antimicrobial resistance (AMR) is a major problem in many endemic countries. Prevention through vaccination is possible through recently-licensed typhoid conjugate vaccines (TCVs). National immunisation programs are currently being considered or deployed in several countries where AMR prevalence is known to be high, and the Gavi vaccine alliance has provided financial support for their introduction. Pathogen whole genome sequence data are a rich source of information on Typhi variants (genotypes or lineages), AMR prevalence, and mechanisms. However, this information is currently not readily accessible to non-genomics experts, including those driving vaccine implementation or empirical therapy guidance.

Results: We developed TyphiNET ( https://www.typhi.net ), an interactive online dashboard for exploring Typhi genotype and AMR distributions derived from publicly available pathogen genome sequences. TyphiNET allows users to explore country-level summaries such as the frequency of pathogen lineages, temporal trends in resistance to clinically relevant antimicrobials, and the specific variants and mechanisms underlying emergent AMR trends. User-driven plots and session reports can be downloaded for ease of sharing. Importantly, TyphiNET is populated by high-quality genome data curated by the Global Typhoid Pathogen Genomics Consortium, analysed using the Pathogenwatch platform, and identified as coming from non-targeted sampling frames that are suitable for estimating AMR prevalence amongst Typhi infections (no personal data is included in the platform). As of February 2024, data from a total of n = 11,836 genomes from 101 countries are available in TyphiNET. We outline case studies illustrating how the dashboard can be used to explore these data and gain insights of relevance to both researchers and public health policy-makers.

Conclusions: The TyphiNET dashboard provides an interactive platform for accessing genome-derived data on pathogen variant frequencies to inform typhoid control and intervention strategies. The platform is extensible in terms of both data and features, and provides a model for making complex bacterial genome-derived data accessible to a wide audience.

Keywords: Salmonella Typhi; Antimicrobial Resistance; Dashboard; Genetic epidemiology; Genomics; Surveillance; Typhoid fever; Web application; Whole Genome Sequencing.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable as the project concerns only data which are already in the public domain (please see Availability of data and materials), and no personal or clinical information relating to the bacterial isolates is included. Consent for publication: Not applicable. Competing interests: KLC has received consultancy payments from Pfizer, travel support from BD, and is a member of the Society for Infectious Disease (Singapore). Robert SH was a chair for a Phase I/II/III studies to determine efficacy, safety and immunogenicity of the candidate Coronavirus Disease (COVID- 19) vaccine ChAdOx1 nCoV- 19, and a chair for a Phase 1 Clinical Study to Determine the Safety and Immunogenicity of a Novel GMMA Vaccine Against Invasive Non-Typhoid Salmonella. Robert SH is also an executive board member of the International Society of Infectious Diseases, a member of the Infection & Immunity Board for the MRC/UKRI, and a member of the NIHR Global Health Research Professorships Committee. MML was a co-developer of a Trivalent Salmonella (Enteritidis/Typhimurium/Typhi Vi) conjugate vaccine with Bharat Biotech International and the Wellcome Trust. MML has also received payments from Pfizer for consultancy work. MML holds US patents for “Compositions and Methods for Producing Bacterial Conjugate Vaccines”. MML was a member of a NIH DSMB that oversaw US government-funded efficacy trials of COVID- 19 vaccines. DSMB was disbanded after several vaccines were given Emergency Use Authorization. MML was a member of the Vaccines and Related Biological Products Advisory Committee of the FDA. CAM holds a patent for Salmonella conjugate vaccines and was an employee of Novartis Vaccines Institute for Global Health. INO has received payments from the Wellcome Trust for consultancy work (review panel member). INO receives royalties for books or book chapters published via Springer, Cornell University Press, and Oxford University Press. INO has received travel support from BMGF, ESCMID, and the American ASM. INFO has held leadership or advisory roles for Wellcome SEDRIC, the BMGF surveillance advisory group, the Thomas Bassir Biomedical Foundation, and International Centre for Antimicrobial Resistance Solutions (ICARS) Technical Advisory Forum. INO has also been the surveillance lead for the AMR Technical Work Group, Nigeria Center for Disease Control, the commissioner for The Lancet Commission for Nigeria, a scientific advisor for The Lancet Infectious Diseases, a senior editor for Microbial Genomics, and the editor in chief of the African Journal of Laboratory Medicine. AJP has been involved an Oxford University partnership with AZ for development of COVI19 vaccines. AJP has received payments for consultancy work from Shionogi. AJP is chair of DHSC’s Joint Committee on Vaccination and Immunisation, and was a member of WHOs SAGE. MAC received support for travel from UKHSA, HEE and NIHR. JAC received support from BMGF, US NIH, and the WHO. NAF holds an NIHR Global Health Professorship. ARG received support from BMGF. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1
TyphiNET data curation and dashboard architecture. A The Global Typhoid Genomics Consortium (GTGC) aggregates and curates Typhi genome data and metadata, using Pathogenwatch as both an analysis platform (calling genotypes and AMR determinants from genome assemblies) and publicly accessible data store. Metadata that are not typically available in NCBI/ENA but are collected and curated by the GTGC include purpose of sampling (to tag datasets that are suitable for estimating AMR/genotype prevalence) and information on country-of-travel for travel-associated isolates (to identify country of origin). B A web-scraper is used to pull the latest versions of genotypes, AMR determinants, and metadata files from GTGC-curated Typhi collections in Pathogenwatch, which are used to populate the TyphiNET database. C The TyphiNET dashboard is implemented as a MERN (MongoDB, Express, React, Node) stack JavaScript application as illustrated. Genome data are filtered to exclude low-quality genome sequences, and data sets whose sampling frames make them unsuitable for AMR surveillance (such as those targeted towards sequencing of resistant strains only, or outbreak investigations), before calculating national/annual prevalences of AMR and genotypes to display in interactive plots. ReactJS is used to provide user interface layouts suitable for viewing the interactive plots on a range of devices (computer, tablet, phone). Users can also download static images of current plot displays (PNG), static reports with all current plots (PDF format), or a copy of the TyphiNET database (CSV format)
Fig. 2
Fig. 2
Global views of data gaps and AMR prevalence. A Total sample counts per country. Top panels indicate the number of sequences and genotypes present in the TyphiNET dashboard as of February 2024. Left panel indicates controls for filtering the data visualised by data source (all data, locally collected cases, or travel-associated cases) and time period (by providing start and end years for the period). Countries on the map are coloured by the total number of samples as per the inset legend (top right of map). B National frequencies of XDR. Countries on the map are coloured by XDR frequency as per the inset legend (top right of map). Data are shown where there are ≥ 20 sequences available for the country of interest. Tool tip indicates summary statistics for Pakistan upon mouse over
Fig. 3
Fig. 3
Exploring the emergence of XDR Typhi in Pakistan with the TyphiNET dashboard. A ‘Resistance frequencies within genotypes’ plot shows frequencies of resistance to different drug classes, within common genotypes circulating in Pakistan. Bars are coloured according to the inset legend. B ‘Annual genotype distribution’ plot shows the frequencies of pathogen genotypes circulating in Pakistan per year. Genotypes are coloured as per the inset legend
Fig. 4
Fig. 4
Azithromycin resistance emergence in Bangladesh is associated with different mutations in AcrB, arising in at least six different genotype backgrounds. A Resistance trends plot showing that ciprofloxacin non-susceptibility has remained near-universal since 2005, while MDR declined. Azithromycin emerged circa 2014, reaching 4–5% in 2017–2019. B Resistance determinants plot shows that two different types of azithromycin resistance mutations were detected (AcrB-R717L and AcrB-R717Q), in six different genotypes
Fig. 5
Fig. 5
Genotypes associated with Ciprofloxacin non-susceptibility in Malawi in different periods. A In 2010–2012, CipNS was detected in n = 4 isolates of genotype 4.3.1.1 and n = 2 isolates of 4.3.1.2, which were susceptible to other drugs. B In 2018–2019, CipNS emerged in the MDR genotype 4.3.1.1.EA1 (n = 7 isolates)
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