Similarity analysis of DNA barcodes sequences based on compressed feature vectors
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Motivation: Biological sequence was regarded as an important study by many biologists, because the sequence contains a large number of biological information, what is helpful for scientists' studies on biological cells, DNA and proteins. Currently, many researchers used the method based on protein sequences in function classification, sub-cellular location, structure and functional site prediction, including some machine-learning methods. The purpose of this article, is to find a new way of sequence analysis, but more simple and effective. Results: According to the nature of 64 genetic codes, we propose a simple and intuitive 2D graphical expression of protein sequences. And based on this expression we give a new Euclidean-distance method to compute the distance of different sequences for the analysis of sequence similarity. This approach contains more sequence information. A typical phylogenetic tree constructed based on this method proved the effectiveness of our approach. Finally, we use this sequence-similarity-analysis method to predict protein sub-cellular localization, in the two datasets commonly used. The results show that the method is reasonable. Contact: dragonbw@163.com