LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS

Jin Chen; Lin-xi Zhang; De-lu Zhao

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LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS

Papers | Updated：2021-06-21

- LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS
- LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS
- 高分子科学（英文版） 2005年23卷第1期页码：11-16
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- 纸质出版：2005-01-20
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Jin Chen, Lin-xi Zhang, De-lu Zhao. LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS[J]. 高分子科学（英文版）, 2005,23(1):11-16.

Jin Chen, Lin-xi Zhang, De-lu Zhao. LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS[J]. 2005, 23(1): 11-16.
Jin Chen, Lin-xi Zhang, De-lu Zhao. LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS[J]. 高分子科学（英文版）, 2005,23(1):11-16. DOI：

Jin Chen, Lin-xi Zhang, De-lu Zhao. LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS[J]. 2005, 23(1): 11-16. DOI：

摘要

The characterization of long-range correlations and fractal properties of DNA sequences has proved to be a difficult though rewarding task mainly due to the mosaic character of DNA consisting of many patches of various lengths with different nucleotide constitutions. In this paper we investigate statistical correlations among different positions in DNA sequences using the two-dimensional DNA walk. The root-mean-square fluctuation F(l) is described by a power law. The autocorrelation function C(l)

which is used to measure the linear dependence and periodicity

exists a power law of C(l)  l. We also calculate the mean-square distance along the DNA chain

and it may be expressed as l r with 2 > > 1. Our investigations can provide some insights into long-range correlations in DNA sequences.

Abstract

which is used to measure the linear dependence and periodicity

exists a power law of C(l)  l. We also calculate the mean-square distance along the DNA chain

and it may be expressed as l r with 2 > > 1. Our investigations can provide some insights into long-range correlations in DNA sequences.

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