Integrated Hardware Architecture for Efficient Computation of the $n$-Best Bio-Sequence Local Alignments in Embedded Platforms

A flexible hardware architecture that implements a set of new and efficient techniques to significantly reduce the computational requirements of the commonly used Smith-Waterman sequence alignment algorithm is presented. Such innovative techniques use information gathered by the hardware accelerator during the computation of the alignment scores to constrain the size of the subsequence that has to be post-processed in the traceback phase using a general purpose processor (GPP). Moreover, the proposed structure is also capable of computing the
n
-best local alignments according to the Waterman-Eggert algorithm, becoming the first hardware architecture that is able to simultaneously evaluate the
n
-best alignments of a given sequence pair, by incorporating a set of ordering units that work in parallel with the systolic array. A complete alignment system was developed and implemented in a Virtex-4 FPGA, by integrating the proposed accelerator architecture with a Leon3 GPP. The obtained experimental results demonstrate that the proposed system is flexible and allows the alignment of large sequences in memory constrained systems. As an example, a speedup of 17 was obtained with the conceived system when compared with a regular implementation of the LALIGN35 program running on an Intel Core2 Duo processor running at a 40 × higher frequency.