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Objective:  To develop web-based tools and resources to gather and share genetic data.

Pathogens do not recognise geographical boundaries, so it is essential that researchers around the world can efficiently build, share and analyse large and complex datasets to generate an overview of global diversity in pathogen populations. This is difficult for any disease but is particularly challenging for diseases that are endemic in low- and middle-income countries with limited health systems and research infrastructure.

Systems to integrate data across labs and field study sites working on the same disease are often lacking. Cost-effective sampling tools are needed and they must be suitable to a variety of field conditions. Moreover, clinical and epidemiological researchers may lack the tools and training needed to make use of large-scale genome variation data, while genetics and genomic scientists can lack the tools and data structures needed to make use of large-scale clinical and epidemiological data.

One way that we’re addressing these challenges is by building informatics systems and web-based tools that enable clinicians and scientists around the world to analyse and share data generated through large-scale collaborations. This work has led to the development of a number of tools including LookSeq, ExplorerCat and MapSeq, an early progenitor of the current MalariaGEN P. falciparum web application. We've also worked with the WorldWide Antimalarial Resistance Network (WWARN) to develop the first release of their online data submission system.

Drawing on this rich experience, we’re developing a new software framework, Panoptes, which can be rapidly deployed to create interactive web applications for exploring genetic and genomic data, while also supporting a high-level of customisation to suit the unique nature of particular datasets and projects. While work on Panoptes continues, we’ve released two early implementations of this framework—web applications designed to share data from the Anopheles gambiae 1000 Genomes Project (Ag1000G) and Pf3k projects.

Going forward, we intend to extend the scope of this work, integrating these efforts into a number of ongoing projects at field sites in Southeast Asia, The Gambia and Kenya in order to enlarge our understanding of how it will in future be possible to survey evolutionary changes in pathogen and vector populations at high-spatial resolution and in near real-time.

An important component of this work will be developing mobile tools and technologies designed to gather data within these local-contexts and to put actionable information back into the hands of public health officials and health care providers.

Future work will also focus on developing methods for collecting data from multiple sources to construct integrated maps of the many different factors that determine disease transmission and the emergence and spread of resistance, such as epidemiological and ecological data.