Characterizing Long-Range Correlations in DNA Sequences from Wavelet Analysis

Arnéodo, A.; Bacry, Emmanuel; Graves, Pierre‐Vincent; Muzy, Jean–François

doi:10.1103/physrevlett.74.3293

Cited by 347 publications

(232 citation statements)

References 14 publications

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“…By plotting log 10 (wσ H (w)) − 0.6 log 10 w versus log 10 w, we select H = 0.6 as the Hurst exponent value of reference for an horizontal linear scaling behavior. This particular value actually corresponds to the LRC observed for the set of sequenced human introns of length L 800 bp [102,115]. In Figure 3(b) are also reported for comparison the results obtained when investigating the genome of Escherichia coli which are quite typical of what we have observed with other eubacterial genomes.…”

Section: Sequencessupporting

confidence: 63%

“…The existence of a large-scale regime ( 200 bp) of strong LRC [1,2] will be detailed in a forthcoming publication. An important feature is that we actually need to use oscillating boxes, i.e., wavelets, to perform this standard deviation measure on real sequences as the mosaic stucture of genomic DNA sequences may lead to severe bias [102,115]. Now, if one is interested in the behavior of the correlation function C(n) between nucleotides separated by a distance n, equation (2) implies that…”

Section: Sequencesmentioning

confidence: 99%

“…There have been some phenomenological attempts to differentiate local patchiness from long-range correlations using 'ad hoc methods' such as the so-called 'min-max method' [64] and the 'detrended fluctuation analysis' [108,109]. Alternatively, the wavelet transform (WT) [110,111,112,113,114] has been proposed as a very powerful technique for fractal analysis of DNA sequences [102,115] (see also Ref. [116] where the WT is used as a tool for visualizing regular patterns in DNA sequences).…”

Section: Introductionmentioning

confidence: 99%

“…As already experienced in various fields, the WT can be seen as a mathematical microscope that is well suited for characterizing the scaling properties of fractal objects and this even in the presence of some polynomial component [113,117,118,119,120]. By considering analyzing wavelets that make the microscope blind to low frequency trends, one can reveal and quantify the scale invariance properties of DNA walks [102,115,121]. In a previous work, by applying the so-called wavelet transform modulus maxima (WTMM) method [119,120,122] to the analysis of various genomic sequences mainly selected in the human genome, we have found that the fluctuations in the patchy landscapes of both coding and noncoding DNA walks are homogeneous with Gaussian statistics [102,115].…”

Section: Introductionmentioning

confidence: 99%

“…By considering analyzing wavelets that make the microscope blind to low frequency trends, one can reveal and quantify the scale invariance properties of DNA walks [102,115,121]. In a previous work, by applying the so-called wavelet transform modulus maxima (WTMM) method [119,120,122] to the analysis of various genomic sequences mainly selected in the human genome, we have found that the fluctuations in the patchy landscapes of both coding and noncoding DNA walks are homogeneous with Gaussian statistics [102,115]. The main consequence of this result is the justification of using a single exponent, namely the Hurst or roughness exponent H , to characterize the underlying fractal organization of DNA sequences.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Wavelet Analysis of DNA Bending Profiles reveals Structural Constraints on the Evolution of Genomic Sequences

Audit

Vaillant

Arnéodo

et al. 2004

Journal of Biological Physics

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Abstract. Analyses of genomic DNA sequences have shown in previous works that base pairs are correlated at large distances with scale-invariant statistical properties. We show in the present study that these correlations between nucleotides (letters) result in fact from long-range correlations (LRC) between sequence-dependent DNA structural elements (words) involved in the packaging of DNA in chromatin. Using the wavelet transform technique, we perform a comparative analysis of the DNA text and of the corresponding bending profiles generated with curvature tables based on nucleosome positioning data. This exploration through the optics of the so-called 'wavelet transform microscope' reveals a characteristic scale of 100 − 200 bp that separates two regimes of different LRC. We focus here on the existence of LRC in the small-scale regime ( 200 bp). Analysis of genomes in the three kingdoms reveals that this regime is specifically associated to the presence of nucleosomes. Indeed, small scale LRC are observed in eukaryotic genomes and to a less extent in archaeal genomes, in contrast with their absence in eubacterial genomes. Similarly, this regime is observed in eukaryotic but not in bacterial viral DNA genomes. There is one exception for genomes of Poxviruses, the only animal DNA viruses that do not replicate in the cell nucleus and do not present small scale LRC. Furthermore, no small scale LRC are detected in the genomes of all examined RNA viruses, with one exception in the case of retroviruses. Altogether, these results strongly suggest that small-scale LRC are a signature of the nucleosomal structure. Finally, we discuss possible interpretations of these small-scale LRC in terms of the mechanisms that govern the positioning, the stability and the dynamics of the nucleosomes along the DNA chain. This paper is maily devoted to a pedagogical presentation of the theoretical concepts and physical methods which are well suited to perform a statistical analysis of genomic sequences. We review the results obtained with the so-called waveletbased multifractal analysis when investigating the DNA sequences of various organisms in the three kingdoms. Some of these results have been announced in B. Audit et al. [1,2].

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Section: Sequencessupporting

confidence: 63%