Evolution trends of the 2009 pandemic influenza A (H1N1) viruses in different continents from March 2009 to April 2012

Novel Protein Sequence Comparison Method Based on Transition Probability Graph and Information Entropy

AIM AND OBJECTIVE

Sequence analysis is one of the foundations in bioinformatics. It is widely used to find out the feature metric hidden in the sequence. Otherwise, the graphical representation of biologic sequence is an important tool for sequencing analysis. This study is undertaken to find out a new graphical representation of biosequences.

MATERIALS AND METHODS

The transition probability is used to describe amino acid combinations of protein sequences. The combinations are composed of amino acids directly adjacent to each other or separated by multiple amino acids. The transition probability graph is built up by the transition probabilities of amino acid combinations. Next, a map is defined as a representation from transition probability graph to transition probability vector by k-order transition probability graph. Transition entropy vectors are developed by the transition probability vector and information entropy. Finally, the proposed method is applied to two separate applications, 499 HA genes of H1N1, and 95 coronaviruses.

RESULTS

By constructing a phylogenetic tree, we find that the results of each application are consistent with other studies.

CONCLUSION

the graphical representation proposed in this article is a practical and correct method.

Novel Method of 3-Dimensional Graphical Representation for Proteins and Its Application

In this article, we propose a 3-dimensional graphical representation of protein sequences based on 10 physicochemical properties of 20 amino acids and the BLOSUM62 matrix. It contains evolutionary information and provides intuitive visualization. To further analyze the similarity of proteins, we extract a specific vector from the graphical representation curve. The vector is used to calculate the similarity distance between 2 protein sequences. To prove the effectiveness of our approach, we apply it to 3 real data sets. The results are consistent with the known evolution fact and show that our method is effective in phylogenetic analysis.

A protein mapping method based on physicochemical properties and dimension reduction

BACKGROUND

The graphical mapping of a protein sequence is more difficult than the graphical mapping of a DNA sequence because of the twenty amino acids and their complicated physicochemical properties. However, the graphical mapping for protein sequences attracts many researchers to develop different mapping methods. Currently, researchers have proposed their mapping methods based on several physicochemical properties. In this article, a new mapping method for protein sequences is developed by considering additional physicochemical properties, which is a simple and effective approach.

METHODS

Based on the 12 major physicochemical properties of amino acids and the PCA method, we propose a simple and intuitive 2D graphical mapping method for protein sequences. Next, we extract a 20D vector from the graphical mapping which is used to characterize a protein sequence.

RESULTS

The proposed graphical mapping consists of three important properties, one-to-one, no circuit, and good visualization. This mapping contains more physicochemical information. Next, this proposed method is applied to two separate applications. The results illustrate the utility of the proposed method.

DISCUSSION

To validate the proposed method, we first give a comparison of protein sequences, which consists of nine ND6 proteins. The similarity/dissimilarity matrix for the ssnine ND6 proteins correctly reveals their evolutionary relationship. Next, we give another application for the cluster analysis of HA genes of influenza A (H1N1) isolates. The results are consistent with the known evolution fact of the H1N1 virus. The separate applications further illustrate the utility of the proposed method.