Three immunoregulatory signatures define non-productive HIV infection in stem cell memory CD4(+) T cells.

In the two decades since the first genome was sequenced, the malaria research community has developed a wide array of methods for generating insights into malaria epidemiology and transmission dynamics from parasite genetic data. Laboratories can now sequence whole genomes or obtain deep coverage of many genomic targets from small volumes of blood. Advanced bioinformatic pipelines can detect minority alleles present in polyclonal infections while removing errors. An array of computational tools has been developed to distill critical insights from the data, and a variety of visualization methods have been created to communicate results to stakeholders. Collectively, these advancements provide the foundation for establishing a virtuous cycle linking public health personnel with laboratory and data scientists (). This collaboration is essential for designing meaningful studies, generating and analyzing sequence data, and translating results into actionable information. An integrated approach will be critical for responding to new threats, including the emergence and spread of drug and diagnostic resistance, increased transmission due to insecticide resistance and invasive mosquito species, and importation due to human migration. However, several significant barriers currently hinder the integration of these efforts into a cohesive data generation and analysis ecosystem. Model for an ideal genomic epidemiology framework involving public health stakeholders and scientists. This framework outlines the iterative cycle of malaria genomic epidemiology, integrating public health priority questions, data collection and generation, genetic data analysis, and synthesis and interpretation to inform malaria control and elimination policy. The process begins with defining public health questions relevant to national malaria control and elimination programs, such as whether cases are imported or locally acquired, whether histidine-rich protein 2 (HRP2)-based rapid diagnostic tests should continue to be used, or whether frontline antimalarial drugs remain effective. Next, data collection involves appropriate study design and sample types, as well as contextual data such as demographic information or travel history. Subsequently, data generation will involve sequencing and bioinformatic processing, as well as epidemiological and clinical data, including light microscopy and polymerase chain reaction testing for malaria diagnosis. Robust data standards would enable the harmonization of downstream analysis of genetic data. This stage includes deriving key metrics from genetic data, such as allele frequencies and the complexity of infection, which requires accessible analysis software. The results are then synthesized and interpreted by integrating genetic, clinical, epidemiological, and surveillance data to extract relevant insights and determine appropriate courses of action. Findings are then communicated to public health decision-makers and program officers through a variety of formats, including reports, dashboards, and policy briefs, among others. This will then guide intervention strategies and inform further research questions, thereby continuing the cycle. Substantial variability in sequencing, bioinformatic methods, and nonstandard data formats creates challenges in harmonizing downstream analysis. This variability, however, should not impede progress; rather, adopting a principled approach to analysis should enable users to select from various data generation options while ensuring the comparability of the final results. In addition, although sequencing approaches are increasingly common, other molecular methods, such as electrophoresis-based genotyping of length-polymorphic markers, remain widely used in malaria-endemic settings, further highlighting the need to accommodate diverse data types. Many analysis tools have been created over the past 15 years to address the challenges specific to analyzing genetic data, including ploidy, polyclonality, and recombination, which have motivated -specific analytical tool development. However, these tools have not been systematically assessed, and vary widely in their adherence to best practices in software development, for example, Findability, Accessibility, Interoperability, and Reuse (often abbreviated as FAIR) standards or those proposed by the Public Health Alliance for Genomic Epidemiology. Moreover, there are few, if any, standards for data exchange between them, limiting interoperability. A unified ecosystem that prioritizes interoperability and accessibility with clear data-sharing guidelines and modular workflows, such as that proposed for public health genomic surveillance more broadly, is essential to maximize the impact of malaria genomic data. Rather than investing resources in self-contained analysis pipelines, a more sustainable and effective solution would be to create a collaborative, transparent, and open platform for curated, interoperable software that