Abstract
Predicting the location of the translation initiation sites (TIS) is an important problem of molecular biology. In this field, the computational cost for balancing non-TIS sequences is substantial and demands high-performance computing. In this article, we present an optimized version of the K-modes algorithm to cluster TIS sequences and a comparison with the standard K-means clustering. The adapted algorithm uses simple instructions and fewer computational resources to deliver a significant speedup without compromising the sequence clustering results. We also implemented two optimized parallel versions of the algorithm, one for graphics processing units (GPUs) and the other one for general-purpose multicore processors. In our experiments, the GPU K-modes's performance was up to 203 times faster than the respective sequential version for processing Arabidopsis thaliana sequence.
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