由於傳統的序列比對方式都是利用動態程式設計的方法。在進行序列比對時，時間及空間複雜度都不可避免的隨著序列長度呈指數成長。雖然後來有提出改進的方法，節省了空間，解決記憶體的問題，但換來的是需要更多的時間。如此一來，當面對超長序列比對時，例如百萬長度，一般的電腦根本無法負擔這麼龐大的計算量及硬體需求。所以我們依據1999年，L. Delcher所提出whole genomes排比方式 [1] 加以改變。利用演算法中的LCS ( longest Common subsequence ) 的概念來取代原來suffix tree [5] 所提供的子字串比對功能，且能藉由生物學者的專家經驗，允許一定誤差範圍內的相似關係。進一步提出合併及改進LCS的方法及性質。使得實際上，在一般個人電腦上進行百萬長度序列比對時，所花費的時間尚在可接受的範圍之內。而依據我們實驗所得的結果，在進行一百二十多萬對一百萬長度的序列排比時，取兩千的範圍，滑動視窗取16，相似度15以上的必取，14的還可接受的條件之下，在一般的個人電腦上，取範圍2000，需25分鐘即可得到結果。所以說，雖然採用LCS設計的序列排比方式，在時間複雜度上比利用suffix tree來做的方式高，但還在可接受的範圍內。且獲得更多的彈性。故在實際應用上，我們所提出的方法不但能兼顧彈性，且還不失為一個有效率的方法。

We design a new sequence alignment system that base on L. Delcher’s [1] idea to aligning very long sequences. Because the traditional sequence alignment methods [12] spend too much time and memory space when align very long sequences. We use an idea, longest common subsequence (LCS), instead of suffix tree [5], and then we can solve sequence alignment problems more flexible. But it spends more time than using suffix tree. We improve LCS by using hash table, and it is a kind of trade off method between time and space. We also find some properties of LCS. So we can combine two shorter LCS’s results to get a longer LCS’s approximate result. In our experiment, we show our method is an effective method in practice.

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