Construction and Analysis of Secondary Database of Human Cancer Gene p53 Based on Quasi Alignment Cellular Neural Network

Abstract:

Using the biological primary databases at the National Center for Biotechnology Information（NCBI），We construct a secondary database of human cancer-related nucleotides p53. The database design mainly includes four aspects：cancer information，p53 sequence information，sample information and reference information. We store the data from NCBI in XML file，then by parsing the files to secondary database and initially realize the data of searching，linking ，statistical analysis and other functions. p53 cancer gene sequences are compared by quasi alignment one dimensional cellular neural network method，and the sensitivity and reliability of the global alignment of the two sequences are enhanced by improving the similarity evaluation formula. We applied the improved sequence alignment algorithm in non small cell lung cancer and breast cancer p53 gene sequence alignment , the result shows that there are great differences in mutant p53 Exon 5 of two cancer sequences which can be used to discriminate these two cancers. In addition, by transforming the primary database into the secondary database in the form of XML, the dynamic exchange of network data and local data is redized, which provides an excellent platform for the study of cancer and p53 gene.