A generic and versatile architecture for inference of evolutionary trees under maximum likelihood

Likelihood-based reconstruction of phylogenetic (evolutionary) trees from molecular sequence data exhibits extreme resource requirements because of the high computational cost of the phylogenetic likelihood function. We propose a dedicated computer architecture for the inference of phylogenies under the maximum likelihood criterion. Our design is sufficiently generic to support any possible input data type, that is, DNA, RNA secondary structure, or protein data. Furthermore, the architecture is able to calculate log-likelihood scores and perform numerical scaling to maintain numerical stability on large datasets. It can also optimize the branch lengths of tree topologies and calculate transition probability matrices. We used FPGA technology to verify the correctness of our architecture.