| Summary: | 碩士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 107 === Sequence classification is a preliminary step in many researches of computational biology. There are a variety of methods proposed to compute this problem. However, with the development of high-throughput sequencing technologies, the datasets of sequencing data are getting much larger. As a result, many existing methods cannot accomplish this task with limited computational resource and acceptable time. The k-mer based algorithms are some of these methods. Most of them could finish the classification fast and accurately, but they need large computational space, which is not available in common personal computers.
In this thesis, we propose a k-mer based algorithm. The time complexity of our algorithm is comparable to those of the existing methods, while we make an improvement in space usage by avoiding the redundancy of storing the k-mers. To further reduce the memory usage, we propose a partitioning strategy. In addition to the reduction in memory usage, the algorithm under this partitioning structure can be highly parallelized to improve performance.
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