Abstract
A significant approach for the discovery of biological regulatory rules of genes, protein and their inheritance relationships is the extraction of meaningful patterns from biological sequence data. The existing algorithms of sequence pattern discovery, like MSPM and FBSB, suffice their low efficiency and accuracy. In order to deal with this issue, this paper presents a new algorithm for biological sequence pattern mining abbreviated MpBsmi based on the data index structure. The MpBsmi algorithm employs a sequence position table abbreviated ST and a sequence database index structure named DB-Index for data storing, mining and pattern expansion. The ST and DB-Index of single items are firstly obtained through scanning sequence database once. Then a new algorithm for fast support counting is developed to mine the table ST to identify the frequent single items. Based on a connection strategy, the frequent patterns are expanded and the expanded table ST is updated by scanning the DB-Index. The fast support counting algorithm is used for obtaining the frequent expansion patterns. Finally, a new pruning technique is developed for extended pattern to avoid the generation of unnecessarily large number of candidate patterns. The experiments results on multiple classical protein sequences from the Pfam database validate the performance of the proposed algorithm including the accuracy, stability and scalability. It is showed that the proposed algorithm has achieved the better space efficiency, stability and scalability comparing with MSPM, FBSB which are the two main algorithms for biological sequence mining.
Highlights
Biological sequence is an important component of bioinformatics data, generally including three categories: DNA sequence, RNA sequence and protein sequence [1]
In order to deal with this issue, this paper presents a new algorithm for biological sequence pattern mining abbreviated MpBsmi based on the data index structure
It is showed that the proposed algorithm has achieved the better space efficiency, stability and scalability comparing with MSPM, FBSB which are the two main algorithms for biological sequence mining
Summary
Biological sequence is an important component of bioinformatics data, generally including three categories: DNA sequence, RNA sequence and protein sequence [1]. (1) scan the sequence database once to construct the position table 1-ST and database index 1-DB-Index of single items; the fast support counting algorithm is used to get frequent sequence 1-BSP. The biological sequence pattern mining algorithm Mpbsmi contains building position table. In the case of a fixed support threshold of 40% with the same as Experiment 2, the result of the biological sequence patterns obtained are shown in Table 7: the first column shows the support threshold, the (k+1)th column and the kth column are the data set size and the corresponding number of pattern is mined, wherein 1
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