Public access and documentation is freely available through the database webpage (http://pbil.univ-lyon1.fr/ databases/phever/) and through all dedicated ACNUC retrieval systems. More information on dedicated ACNUC retrieval systems, such as standalone query software (16) , the seqinR package for R (30) or the C and Python APIs, can be obtained in the Supplementary Data or on the PhEVER webpage. The PhEVER flat files for local installation are available through our ftp server (ftp://pbil.univ lyon1.fr/pub/phever) and instructions are available on the database webpage. The PhEVER database is updated every 6 months. This update frequency allows to follow the fast pace of viral and prokaryotic genome sequencing as well as to obtain updated genomic annotations for large eukaryotic genomes. Previous versions of the database remain available upon request. Reporting errors in infectious disease outbreaks, with an application to Pandemic Influenza A/H1N1 BACKGROUND: Effectively responding to infectious disease outbreaks requires a well-informed response. Quantitative methods for analyzing outbreak data and estimating key parameters to characterize the spread of the outbreak, including the reproductive number and the serial interval, often assume that the data collected is complete. In reality reporting delays, undetected cases or lack of sensitive and specific tests to diagnose disease lead to reporting errors in the case counts. Here we provide insight on the impact that such reporting errors might have on the estimation of these key parameters. RESULTS: We show that when the proportion of cases reported is changing through the study period, the estimates of key epidemiological parameters are biased. Using data from the Influenza A/H1N1 outbreak in La Gloria, Mexico, we provide estimates of these parameters, accounting for possible reporting errors, and show that they can be biased by as much as 33%, if reporting issues are not accounted for. CONCLUSIONS: Failure to account for missing data can lead to misleading and inaccurate estimates of epidemic parameters. The recent outbreak of pandemic strain Influenza A H1N1 (pH1N1), as well as other infectious disease outbreaks that have taken place recently illustrate the need for a rapid public health response and the ability to collect and analyze data efficiently. Unnecessary panic and disruption to society is more likely to be avoided and appropriately measured public health responses are more likely to take place when we have accurate information on the virulence and pathogenicity of an emerging disease. For these reasons, quantitative methods have been developed and continue to be developed to facilitate the assimilation of emerging data. Important quantities to estimate include the basic reproductive number, R 0 , defined as the average number of secondary infections created by a single infected individual in an entirely susceptible population. Numerous methods have been proposed for the estimation of this quantity, including deterministic and stochastic compartmental models [1] , branching processes [2] , networks [3, 4] , and, more recently, a likelihood based method [5, 6] .