Promover a utilização em grande escala da genómica para compreender a dinâmica e a evolução dos agentes patogénicos bacterianos

The use of genomics information for pathogen surveillance is gaining increased momentum, but the currently available solutions are either server based, commercial or being discontinued, as is the case of the widely adopted Bionumerics software. Moreover, proprietary software is closed source, not allowing a transparent view of the algorithms used, preventing peer review, and is closed to the contribution of relevant stakeholders, making customization and inclusion into analysis pipelines difficult. Free open-source software (OSS) is a common-good resource promoting equitable access to advanced tools and a truly global adoption of standard approaches[5]. We will develop the next generation of OSS for the genomic study of bacterial pathogens. The software will allow the easy creation of frameworks for novel emerging pathogens, incorporate genomic variability and structure information, be able to effectively process the large amounts of data currently available that are only expected to grow, promote real-time data acquisition and analysis of changes in potentially important properties for host adaptation, and require limited computational resources and skills to yield actionable information. To achieve these goals, we will build on previous work by the team on chewBBACA (a wg/cgMLST platform) and PHYLOViZ (a visualization tool). In task 1, we will optimize chewBBACA to reduce the computational requirements and time for large-scale analysis and improve the identification of divergent alleles. Coupled with richer output data, this will facilitate more detailed downstream analyses. In task 2 we will create a suite of software tools to facilitate the process of creating novel schemas or of expanding existing schemas leveraging new genomic information, thereby lowering the barrier to develop suitable approaches to new pathogens or improve existing schemas while maintaining backward compatibility. In task 3 we will provide human readable interactive graphical reports of analyses of the generated data. These will make accessible to users information allowing an evaluation of the schema and on the analysis of a particular set of isolates. Since these reports are self-contained, they can be distributed, allowing collaboration and removing the technical burden from users of performing relevant subsequent analyses. In task 4 we create tools to leverage synteny information and expand the resolution of wg/cgMLST and population studies. In task 5 we will improve on the existing PHYLOViZ software. Despite its success, PHYLOViZ is struggling to deal with the large amounts of data currently available. Updating the software will allow it to overcome this limitation, while also expanding its capabilities in the algorithms for tree construction and identification of related strains, as well as in its capacity for visual analytics. The project will maintain software that has been widely used by the community and by international public health agencies, ensuring that ongoing users and future studies remain supported. By expanding the software’s capabilities and facilitating its adoption in studying novel pathogens, the project will contribute to global surveillance by a distributed network of stakeholders. By creating more specific outputs relevant to understanding pathogen evolution we will facilitate leveraging pathogen surveillance data for this important goal that can be translated into new diagnostic and preventive approaches.