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.

Key management is a fundamental security service in distributed sensor networks. In this paper, the focus is to design a lightweight group key management scheme to safeguard the data packet passing on the sensor networks under different types of attacks. We propose an energy efficient level-based hierarchical system and build the secure route from the source node to sink node. We compromise between the energy consumption and the shortest path by utilizing the number of neighbors (NBR) of a sensor when renewing a cluster head or choosing the next hop in the hierarchical clusters. In addition, our protocol contains group communication policies, group membership requirements and an algorithm for generating a distributed group key for secure communication