Mohanty and Narayana Rao Reply:

Mohanty, Arun; Rao, A. V. S. S. Narayana

doi:10.1103/physrevlett.88.219804

Cited by 7 publications

(15 citation statements)

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“…Interestingly, the famous model of DNA walk representation is found to be a special case of the present analysis with a density distribution corresponding to either a purine or a pyrimidine group. The present analysis seems to support the hypothesis of the joint action of a deterministic and random process as a possible mechanism of generating the sequences [17] with a few exceptions [15]. However, in this work, we do not advocate for any specific model, rather provide an elegant tool to measure the degree of correlations unambiguously so that the interpretation of data including theoretical analysis will become more meaningful.…”

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confidence: 61%

“…However, we have found this agreement even in small cDNA segments where GA and TC contents are quite different. In fact, we have noticed a very general property is that the distribution of any group of nucleotides which has a probability of occurrence p has the same (or nearly the same) variance as that of the distribution of its complementary group that has the probability of occurrence (1 2 p), although both have different distribution functions r n [15]. This is an important observation as we can now use either GA or TC distributions as alternatives to the DNA walk representation along with the individual nucleotide distributions to study the correlations.…”

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confidence: 99%

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Factorial Moments Analyses Show a Characteristic Length Scale in DNA Sequences

Rao

2000

Phys. Rev. Lett.

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A unique feature of most of the DNA sequences, found through the factorial moments analysis, is the existence of a characteristic length scale around which the density distribution is nearly Poissonian. Above this point, the DNA sequences, irrespective of their intron contents, show long range correlations with a significant deviation from the Gaussian statistics, while, below this point, the DNA statistics are essentially Gaussian. The famous DNA walk representation is also shown to be a special case of the present analysis.

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confidence: 99%

Factorial Moments Analyses Show a Characteristic Length Scale in DNA Sequences

Rao

2000

Phys. Rev. Lett.

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“…Biologically the necessity of segmentation in DNA is connected with the different functional properties of the DNA segments with different base composition and arrangement of base pairs; for instance, in coding and noncoding regions, large‐scale chromosome domains, the isochores6 or regions of a characteristic composition in the regulatory sequences of a genome 4. Different techniques, including random walk,7 autocorrelation functions,8 power‐spectra,9 mutual information function,10 wavelet11 and factorial moment analysis,12 are used for statistical analysis of DNA (for review, see references 12–14). The correlations in DNA primary structure are found on different scales – from a few base pairs up to 10 7 base pairs.…”

Section: Introductionmentioning

confidence: 99%

“…The correlations in DNA primary structure are found on different scales – from a few base pairs up to 10 7 base pairs. It is found that within coding parts of DNA the positions of single base pairs are random or have only short‐range correlations, while noncoding parts of DNA tend to have longer‐ranged correlations with repetitive patterns 7–15. In reference 15, it was reported that mutual information function based on four‐letter alphabet has a significantly different functional form in coding and noncoding parts of DNA.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Heteropolymer Sequences Specifying Subsequences with Different Composition and Statistical Properties

Gusev

Vasilevskaya

Makeev

et al. 2003

Macro Theory & Simulations

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We have studied the segmentation of two‐letter AB heterosequences composed of subsequences with different composition and distribution of A and B monomer units along the chain. Our approach is based on the segmentation function S(k) introduced in the present work and on the Jensen–Shannon divergence measure determined with respect to the probabilities of the lengths of uniform blocks of A and B monomer units. It is shown that the function S(k) is extremely sensitive to the sequence statistics. Even visual analysis of S(k) allows judgment on some features of sequence statistics. In particular, function S(k) is constant for random copolymers, it is an oscillating function for random block copolymers and shows monotonic growth up to some constant value for proteinlike copolymers. However, due to significant fluctuations observed for short sequences, the function S(k) can be effectively used only for segmentation of a heterosequence composed of very long subsequences. On the other hand, we find that the Jensen–Shannon divergence measure does not allow one to judge the type of statistics, but is extremely efficient for segmentation of a heterosequence. Therefore, the two introduced functions, being mutually complementary, provide an effective approach for recognizing and segmentation of heterosequences. As an example, the methods developed are applied for concatenating sequences of different proteins.Segmentation function S(k, l, x) as a function of parameter k and starting number x of “window” for a sequence composed of elastin and ribonuclease sequences.magnified imageSegmentation function S(k, l, x) as a function of parameter k and starting number x of “window” for a sequence composed of elastin and ribonuclease sequences.

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“…Note that the factorial moments analyses along DNA sequences have been studied by Mohanty and Rao [18]. A detailed study shows that the moments S p ' do not exhibit a clear scaling with respect to the length ' for most bacterial genomes besides T. pal, but the ESS scaling exists uniformly for all genomes examined.…”

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Scaling and Hierarchical Structures in DNA Sequences

2004

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A method of analyzing DNA correlation structure is introduced. Density fluctuations of nucleotides are shown to display an extended self-similarity scaling when the scale varies between 100 and 8000 base pairs. The scaling is accurately described by a hierarchical structure model of She and Leveque [Phys. Rev. Lett. 72, 336 (1994)]]. The derived model parameter beta is able to quantify moderately large-scale correlations which exist in a true DNA sequence but are absent in its randomly shuffled sequence and in a simulated model sequence by an evolution model of Hsieh et al. [Phys. Rev. Lett. 90, (2003)]]. Finally, it is shown that beta varies with the evolution category and measures the organizational complexity of the genome.

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Mohanty and Narayana Rao Reply:

Cited by 7 publications

References 1 publication

Factorial Moments Analyses Show a Characteristic Length Scale in DNA Sequences

Factorial Moments Analyses Show a Characteristic Length Scale in DNA Sequences

Segmentation of Heteropolymer Sequences Specifying Subsequences with Different Composition and Statistical Properties

Scaling and Hierarchical Structures in DNA Sequences

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