Influenza viruses are rapidly evolving pathogens causing annual epidemics and occasional pandemics. The accumulation of amino acid substitutions allows the virus to adapt to changing environments like novel host species or to escape the acquired immunity of the host population. Especially substitutions in the epitope regions of the surface protein HA lead to antigenic change, facilitating the evasion of the host´s immune response by the virus and making frequent updates of the vaccine composition necessary. Through the global monitoring of circulating influenza viruses, large amounts of sequence data are generated. Computational biology offers a quick and easy way to analyze these to characterize the genetic and antigenic evolution of influenza viruses. Using sequence data together with antigenic information provided by hemagglutination inhibition (HI) assays and structural information, bioinformatics methods can elucidate evolutionary relationships between isolates, infer amino acid sites or regions of the protein under positive selection, and identify amino acid changes relevant for the antigenic evolution. We here describe a selection of programs used to generate hypotheses about functionally or antigenically important amino acid changes, protein regions, or individual sites that can subsequently be tested in wet-lab experiments or have value for predicting the future evolution of seasonal influenza A viruses.