Phylogenetic tree (or phylogeny) is a meaningful tree representation for the evolutionary history of different organisms that it has been shown useful in drug discovery, virus identification and functional genomic study [1]. The objective of this project is to develop efficient FPGA implementations for phylogenetic tree reconstruction algorithms. By taking advantage of hardware high-performance, we explore the possibilities of parallelization and system optimization to provide high-speed acceleration for the phylogeny inference. The Maximum Likelihood approach for inferring the phylogeny from molecular data has received much attention [2]. Although the optimal ML phylogenetic tree search problem is classified as NP-hard and it is difficult to find the optimal solution, the GAML algorithm (based on Genetic Algorithm and Maximum Likelihood) has been shown to find a good near-optimal solution in reasonable time [3]. In [4], we have shown that using HW/SW (Hardware/ Software) codesign for GAML implementation can provide significant speed-up when compared with software-only implementation. Our HW/SW system has good potential for handling large scale problems in real applications. In [5], an enhanced version of FPGA design with parallel and pipelined implementation for the likelihood evaluation is proposed. It has been shown 100 times faster than the single-CPU solution for the ML tree evaluation. To reduce precision loss attributed to truncation error in the FPGA, we are developing a dynamic floating-point alike structure based on the fixed-point architecture. We have also studied the implementation of phylogenetic tree reconstruction algorithm in the embedded platform (i.e. VirtexII-Pro Platform FPGA). Significant improvement in data transmission rate between hardware and software and higher clock frequency of FPGA have been realized [6].
CITATION STYLE
Mak, T. S. T., & Lam, K. P. (2004). On computing maximum likelihood phylogeny using FPGA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3203, p. 1188). Springer Verlag. https://doi.org/10.1007/978-3-540-30117-2_174
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