Genome informatics of influenza A: from data sharing to shared analytical capabilities

Abstract

Emerging infectious diseases are critical issues of public health and the economic and social stability of nations. As demonstrated by the international response to the severe acute respiratory syndrome (SARS) and influenza A, rapid genomic sequencing is a crucial tool to understand diseases that occur at the interface of human and animal populations. However, our ability to make sense of sequence data lags behind our ability to acquire the data. The potential of sequence data on pathogens is not fully realized until raw data are translated into public health intelligence. Sequencing technologies have become highly mechanized. If the political will for data sharing remains strong, the frontier for progress in emerging infectious diseases will be in analysis of sequence data and translation of results into better public health science and policy. For example, applying analytical tools such as Supramap (http://supramap.osu.edu) to genomic data for pathogens, public health scientists can track specific mutations in pathogens that confer the ability to infect humans or resist drugs. The results produced by the Supramap application are compelling visualizations of pathogen lineages and features mapped into geographic information systems that can be used to test hypotheses and to follow the spread of diseases across geography and hosts and communicate the results to a wide audience.

Phylogeography of swine influenza H3N2 in the United States: Translational public health for zoonotic disease surveillance

“…To our knowledge, there is currently no translational public health informatics system at a health department or agriculture department. One application with potential is the SUPRAmap project by Janies et al (Janies et al, 2010) which is a web application that allows the user to combine genomic, evolutionary, geospatial and temporal data for biogeography. The application can take raw sequence data, aligned sequences, complete trees, geographical data, and data files that describe the variables (leaf nodes) (Janies et al, 2010).…”

Phylogenetic visualization of the spread of H7 influenza A viruses

“…Sampling bias is always a concern in global influenza studies as the level of effort in sampling and sharing of data is highly variable around the world, although this has improved (Janies et al., ; Gostin et al., ). As this area of visualization is nascent it is important to move forward incrementally with the data at hand before trying to normalize or model the data.…”

Molecular distribution of amino acid substitutions on neuraminidase from the 2009 (H1N1) human influenza pandemic virus

“…NA AASs associated with oseltamivir and zanamivir resistance reported in different subtypes of seasonal influenza and pandemic AH1N1 (2009) influenza were also included [19, 29–31]. In addition, based on the scientific literature AASs related with resistance were classified in confirmed [15–18, 25] and potential (confirmed in different subtypes). The positions of these AASs were named as RRAP (reported resistance-associated position) and PRAP (potentially resistance-associated position), respectively.…”

“…Nevertheless, recent studies of NA sequencing and protein structure analysis have mostly been applied to drug development [23]. To date, the rapid large-scale sequencing, data sharing in influenza and the use of analytical and visualization tools, such as GENGIS [24] and SUPRAMAP [25], have let the integration of phylogenetic data and geographic information. Global geographic maps of NA AASs, phylogenetic relationships and geographical location are frequently reported.…”