Application Of Linear Space Algorithm For Pairwise Sequence Alignment Based On Parallel-Computing

Sequence alignment is a basic information disposal method in Bioinformatics. It is useful for discovering functional, structural, and evolutionary information in DNA and protein sequences. Because sequence data increase rapidly in biology sequence database, it is very exigent to develop algorithms that have high efficiency. Pairwise sequence alignment algorithms of Bioinformatics are studied in this paper. The main contents and production can be briefly summarized as follows:1. On the research actuality of pairwise sequence alignment, classical pairwise sequence alignment: Dynamic Programming Algorithm and Linear Space Algorithm were analysed, and maths models of them are these algorithms were programmedwith C++ programme. Offered experiment condition for the Linear Space Algorithm for Pairwise Sequence Alignment Based on Parallel Computing presented in this paper.2. The definition and classification of parallel computing were introduced and analysed. Cluster of Workstations was studied especially.3. The design of parallel computing and technologies of algorithm decompounding which were in common use were analysed. Appropriate communication mechanism was selected. On the research of former algorithm, linear space algorithm based on parallel computing was presented.4. By the alignment of different sequencing results for RNA of SARS, linear space algorithm based on parallel computing presented in this thesis was compared with classical pairwise sequence alignment. It testified that linear space algorithm based on parallel computing is superior to classical linear space algorithm in time complexity. Given the discussion of the foreground for technology of parallel computing in this research field.The research contents are innovated algorithms of pairwise sequence alignment in Bioinformatics. These algorithms are advanced more evidently than the two traditional algorithms in biology computing efficiency. It can offer sustainment for Bioinformatics research and practice.