Microbial genomic data analysis for infectious diseases

Infectious diseases are caused by pathogenic micro-organisms which can be bacteria, viruses, parasites or fungi. The diseases can be spread through many different routes, either directly or indirectly. Military personnel are at high risk of contracting infections, in particular vector-borne and zoonotic infections, during overseas deployments, where they may be exposed to endemic or emerging infections to which they do not have immunity. Additionally, overcrowded settings with poor sanitation are high risks for disease. Genomics is having a transformational impact on medicine. It is enabling advances in accurate diagnosis of infectious disease, development of effective and targeted treatment strategies and opportunities to assess pathogenicity. Further, it supports the detection, surveillance of infectious diseases, the development and assessment of vaccines, as well as the assessment and prediction of anti-microbial resistance. These capabilities are all key military needs to protect personnel in this inter-connected world. The advances in sequencing technologies have resulted in an explosion of genomic data. However, making sense of genomic data requires advances in computational analysis technologies together with crossdisciplinary scientific approaches, skill sets and people. There are extensive reference databases of genomic data. One such open access database is PubMLST.org: it contains well curated genomes for more than 100 microbial species and genera integrated with provenance and phenotype information. All levels of sequence data, from single gene sequences up to and including complete, finished genomes can be accessed on this platform. This data is, however, both large and complex and intractable to analyse and understand using traditional analysis tools. This paper will discuss the challenges of analysing such genomic data for bacterial infections and consider the application of bioinformatics tools and techniques to analyse and communicate microbial genomic data in healthcare