Dataset S2 EST clones with decreased transcription under enhanced c-secretase compared to inhibited c-secretase activity. By applying a cut-off with a p value of 0.005 based on the false discovery rate (FDR, i.e. the probability to wrongly accept a difference between the two conditions), we found 2658 EST clones to be differentially expressed, with 1417 EST clones of decreased transcription with enhanced c-secretase activity compared to inhibited c-secretase activity. FC = Fold change; adj,P,Val = adjusted P-value Dataset S4 EST clones of transcriptional relevance differentially transcribed under enhanced c-secretase compared to inhibited csecretase activity. By applying a cut-off with a p value of 0.005 based on the false discovery rate (FDR, i.e. the probability to wrongly accept a difference between the two conditions), we found 2658 EST clones to be differentially expressed with enhanced c-secretase activity compared to inhibited c-secretase activity. Among them 56 imply transcriptional relevance. FC = Fold change; adj,P,Val = adjusted P-value. Found at: doi:10.1371/journal.pone.0006952.s004 (0.03 MB XLS)  Bayesian Phylogeography Finds Its Roots As a key factor in endemic and epidemic dynamics, the geographical distribution of viruses has been frequently interpreted in the light of their genetic histories. Unfortunately, inference of historical dispersal or migration patterns of viruses has mainly been restricted to model-free heuristic approaches that provide little insight into the temporal setting of the spatial dynamics. The introduction of probabilistic models of evolution, however, offers unique opportunities to engage in this statistical endeavor. Here we introduce a Bayesian framework for inference, visualization and hypothesis testing of phylogeographic history. By implementing character mapping in a Bayesian software that samples time-scaled phylogenies, we enable the reconstruction of timed viral dispersal patterns while accommodating phylogenetic uncertainty. Standard Markov model inference is extended with a stochastic search variable selection procedure that identifies the parsimonious descriptions of the diffusion process. In addition, we propose priors that can incorporate geographical sampling distributions or characterize alternative hypotheses about the spatial dynamics. To visualize the spatial and temporal information, we summarize inferences using virtual globe software. We describe how Bayesian phylogeography compares with previous parsimony analysis in the investigation of the influenza A H5N1 origin and H5N1 epidemiological linkage among sampling localities. Analysis of rabies in West African dog populations reveals how virus diffusion may enable endemic maintenance through continuous epidemic cycles. From these analyses, we conclude that our phylogeographic framework will make an important asset in molecular epidemiology that can be easily generalized to infer biogeogeography from genetic data for many organisms. Phylogenetic inference from molecular sequences is becoming an increasingly popular tool to trace the patterns of pathogen dispersal. The time-scale of epidemic spread usually provides ample time for rapidly evolving viruses to accumulate informative mutations in their genomes [1] . As a consequence, spatial diffusion-among other processes-can leave a measurable footprint in sampled gene sequences from these viruses [1] . Reconstructing both the evolutionary history and spatial process from these sequences provides fundamental understanding of the evolutionary dynamics underlying epidemics, e.g. [2, 3] . It is also hoped that these insights can be translated to effective intervention and prevention strategies [4] and elucidating the key factors in viral transmission and gene flow over larger distances is central in formulating such strategies, e.g. [5] .