False discovery rate (FDR) methodologies are essential in the study of high-dimensional genomic and proteomic data. The R package fdrtool facilitates such analyses by offering a comprehensive set of procedures for FDR estimation. Its distinctive features include: (i) many different types of test statistics are allowed as input data, such as P-values, z-scores, correlations and t-scores, (ii) simultaneously, both local FDR and tail area-based FDR values are estimated for all test statistics and (iii) empirical null models are fit where possible, thereby taking account of potential over- or underdispersion of the theoretical null. In addition, fdrtool provides readily interpretable graphical output, and can be applied to very large scale (in the order of millions of hypotheses) multiple testing problems. Consequently, fdrtool implements a flexible FDR estimation scheme that is unified across different test statistics and variants of FDR.