This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The 2009 outbreak of a new Influenza A H1N1 virus has increased global concerns regarding pandemic influenza. Mutation processes such as drift and shift present challenges for researchers since understanding of these processes is still incomplete and unpredictable. Factors such as morbidity and mortality rates are also difficult to predict at the outset of an outbreak causing difficulties for policy makers and public health officials who need to take appropriate action quickly. Although exploration of a new flu variant can take many months of research fast sequencing of new variants has become practical. During the 2009 outbreak many new sequences were made publicly available by the National Center for Biotechnology Information (NCBI) early on during the outbreak and each day since. This has created a new opportunity for researchers to make use the new sequence information quickly and productively. Proteins with similar sequences have a greater chance of having similar function. We propose to use methods for quickly mapping protein sequence information, such as BLAST and Large Graph Layout (LGL). The NCBI Influenza set will be used as the source of sequence data. The sequence similarity perspective will be supplemented with a view of the state of relevant research using methods for mapping research literature such as CiteSpace. Interactive visualizations will integrate these multi-method approaches. Integrating variables may include temporal and geospatial characteristics of the data allowing researchers to detect trends or themes that would otherwise remain hidden. By contextualizing new viral variant sequences within existing published research on similar sequences researchers will have a map by which to navigate the dynamic information space created by the emergence of new variants.
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