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It has been proposed that supertree approaches should be applied to large multilocus datasets to achieve computational tractability. Large datasets such as those derived from phylogenomics studies can be broken into many locus-specific tree searches and the resulting trees can be stitched together via a supertree method. Using simulated data, workers have reported that they can rapidly construct a supertree that is comparable to the results of heuristic tree search on the entire dataset. To test this assertion with organismal data, we compare tree length under the parsimony criterion and computational time for 20 multilocus datasets using supertree (SuperFine and SuperTriplets) and supermatrix (heuristic search in TNT) approaches. Tree length and computational times were compared among methods using the Wilcoxon matched-pairs signed rank test. Supermatrix searches produced significantly shorter trees than either supertree approach (SuperFine or SuperTriplets; P < 0.0002 in both cases). Moreover, the processing time of supermatrix search was significantly lower than SuperFine+locus-specific search ( P < 0.01) but roughly equivalent to that of SuperTriplets+locus-specific search ( P > 0.4, not significant). In conclusion, we show by using real rather than simulated data that there is no basis, either in time tractability or in tree length, for use of supertrees over heuristic tree search using a supermatrix for phylogenomics. [ABSTRACT FROM AUTHOR]

Background: In Spring 2009, a novel reassortant strain of H1N1 influenza A emerged as a lineage distinct from seasonal H1N1. On June 11, the World Heath Organization declared a pandemic - the first since 1968. There are currently two main branches of H1N1 circulating in humans, a seasonal branch and a pandemic branch. The primary treatment method for pandemic and seasonal H1N1 is the antiviral drug Tamiflu® (oseltamivir). Although many seasonal H1N1 strains around the world are resistant to oseltamivir, initially, pandemic H1N1 strains have been susceptible to oseltamivir. As of February 3, 2010, there have been reports of resistance to oseltamivir in 225 cases of H1N1 pandemic influenza. The evolution of resistance to oseltamivir in pandemic H1N1 could be due to point mutations in the neuraminidase or a reassortment event between seasonal H1N1 and pandemic H1N1 viruses that provide a neuraminidase carrying an oseltamivir-resistant genotype to pandemic H1N1. Results: Using phylogenetic analysis of neuraminidase sequences, we show that both seasonal and pandemic lineages of H1N1 are evolving to direct selective pressure for resistance to oseltamivir. Moreover, seasonal lineages of H1N1 that are resistant to oseltamivir co-circulate with pandemic H1N1 throughout the globe. By combining phylogenetic and geographic data we have thus far identified 53 areas of co-circulation where reassortment can occur. At our website POINTMAP, http://pointmap.osu.edu we make available a visualization and an application for updating these results as more data are released. Conclusions: As oseltamivir is a keystone of preparedness and treatment for pandemic H1N1, the potential for resistance to oseltamivir is an ongoing concern. Reassortment and, more likely, point mutation have the potential to create a strain of pandemic H1N1 against which we have a reduced number of treatment options. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]