Chaos game representation(CGR)-walk model for DNA sequences  

作　　者：高洁 徐振源 

机构地区：[1]School of Science,Jiangnan University [2]School of Information Technology,Jiangnan University

出　　处：《Chinese Physics B》2009年第1期370-376,共7页中国物理B（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (Grant No 60575038);the Natural Science Foundation of Jiangnan University,China (Grant No 20070365)

摘　　要：Chaos game representation （CGR） is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to determine the coordinates of their positions in a continuous space. This distribution of positions has two features： one is unique, and the other is source sequence that can be recovered from the coordinates so that the distance between positions may serve as a measure of similarity between the corresponding sequences. A CGR-walk model is proposed based on CGR coordinates for the DNA sequences. The CGR coordinates are converted into a time series, and a long-memory ARFIMA （p, d, q） model, where ARFIMA stands for autoregressive fractionally integrated moving average, is introduced into the DNA sequence analysis. This model is applied to simulating real CGR-walk sequence data of ten genomic sequences. Remarkably long-range correlations are uncovered in the data, and the results from these models are reasonably fitted with those from the ARFIMA （p, d, q） model.Chaos game representation （CGR） is an iterative mapping technique that processes sequences of units, such as nucleotides in a DNA sequence or amino acids in a protein, in order to determine the coordinates of their positions in a continuous space. This distribution of positions has two features： one is unique, and the other is source sequence that can be recovered from the coordinates so that the distance between positions may serve as a measure of similarity between the corresponding sequences. A CGR-walk model is proposed based on CGR coordinates for the DNA sequences. The CGR coordinates are converted into a time series, and a long-memory ARFIMA （p, d, q） model, where ARFIMA stands for autoregressive fractionally integrated moving average, is introduced into the DNA sequence analysis. This model is applied to simulating real CGR-walk sequence data of ten genomic sequences. Remarkably long-range correlations are uncovered in the data, and the results from these models are reasonably fitted with those from the ARFIMA （p, d, q） model.

关 键 词：CGR-walk model DNA sequence LONG-MEMORY ARFIMA（p d q） model 

分 类 号：Q523[生物学—生物化学] O415.5[理学—理论物理]