We present a massively-parallel implementation of the computation of (co)evolutionary signals from biomolecular sequence alignments based on mutual information (MI) and a normalization procedure to neutral evolution. The MI is computed for two-and three-point correlations within any multiple-sequence alignment. The high computational demand in the normalization procedure is efficiently met by an implementation on Graphics Processing Units (GPUs) using NVIDIA's CUDA framework. GPU computation

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... ves as an enabling technology here insofar as MI normalization is also possible using traditional computational methods [11] but only GPU computation makes MI normalization for sequence analysis feasible in a statistically sufficient sample and in acceptable time. In particular, the normalization of the MI for three-point 'cliques' of amino acids or nucleotides requires large sampling numbers in the normalization, that can only be achieved using GPUs. We illustrate a) the computational efficiency and b) the biological usefulness of two-and three-point MI by an application to the well-known protein calmodulin. Here, we find striking coevolutionary patterns and distinct information on the molecular evolution of this molecule.