Nonetheless, at least 3 genotypes have been identified for the human partetravirus [52, 53] , which showed some degree of geographic segregation in their circulation. In contrast, the porcine partetraviruses identified in wild boars in Germany were highly similar to the porcine partetraviruses initially discovered in Hong Kong, though it remains possible to distinguish them as two close clusters of viruses on a phylogenetic tree constructed from discontinuous genome sequences [20] . The distinct cluster formation by these related viruses on phylogenetic trees argue for the consideration of the viruses to be classified in a new genus, which has already been suggested in a current ICTV taxonomic proposal. The present discovery of the ovine partetravirus and a new genotype of bovine partetravirus adds to the diversity of known partetraviruses, and should aid future efforts to characterize the evolution and transmission of these related viruses. Figure S1 Bootscan analysis on the genome sequences of ovine partetravirus and related viruses (porcine partetravirus HK7 (P), human partetravirus HK1 (H), and bovine partetraviruses HK4 and HK5 (B)) using Simplot version 3.5.1. Consensus threshold of 50% was employed for analysing the bovine partetravirus sequences. Parameters for the analysis are shown in the figure. (PDF) Reconstructing disease outbreaks from genetic data: a graph approach Epidemiology and public health planning will increasingly rely on the analysis of genetic sequence data. In particular, genetic data coupled with dates and locations of sampled isolates can be used to reconstruct the spatiotemporal dynamics of pathogens during outbreaks. Thus far, phylogenetic methods have been used to tackle this issue. Although these approaches have proved useful for informing on the spread of pathogens, they do not aim at directly reconstructing the underlying transmission tree. Instead, phylogenetic models infer most recent common ancestors between pairs of isolates, which can be inadequate for densely sampled recent outbreaks, where the sample includes ancestral and descendent isolates. In this paper, we introduce a novel method based on a graph approach to reconstruct transmission trees directly from genetic data. Using simulated data, we show that our approach can efficiently reconstruct genealogies of isolates in situations where classical phylogenetic approaches fail to do so. We then illustrate our method by analyzing data from the early stages of the swine-origin A/H1N1 influenza pandemic. Using 433 isolates sequenced at both the hemagglutinin and neuraminidase genes, we reconstruct the likely history of the worldwide spread of this new influenza strain. The presented methodology opens new perspectives for the analysis of genetic data in the context of disease outbreaks. The most effective strategy to avert infectious disease epidemics is to identify outbreaks at an early stage and identify their transmission routes. For example, wider spread of nosocomial bacteria could be best avoided by rapid identification of colonized medical personnel spreading the infection (Albrich and Harbarth, 2008) . Early identification of preferential transmission routes could also prove critical for containing wider epidemics, such as SARS or avian influenza (Cooper et al., 2006; Ferguson et al., 2006; Germann et al., 2006) . To be most effective, such prophylactic interventions must be implemented early on and will be based on only very preliminary scientific evidence. Thus, it is crucial that all sources of useful information be considered. Genetic sequence data can now be generated essentially in real time and the analysis of sequence data has already been added to the toolbox of infectious disease epidemiology (Rambaut et al., 2008; Russell et al., 2008; Cottam et al., 2008b; Garten et al., 2009; Sloan et al., 2009) . However, the statistical methodologies to harness the full potential of genetic sequence information are currently lacking. In particular there is a need for methods devoted to the reconstruction of the transmission tree of a set of isolates collected during the early stages of an outbreak.