Identifying characteristic scales in the human genome

Carpena, Pedro; Bernaola‐Galván, Pedro; Coronado, Ana V.; Hackenberg, Michael; Oliver, José Luis Tejera

doi:10.1103/physreve.75.032903

Cited by 47 publications

(55 citation statements)

References 30 publications

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“…Since then, researchers from different backgrounds have been searching for long-range correlations in various DNA sequences using either the fluctuation analysis or the power spectrum. The availability of large DNA sequences and complete genomes in public databases did not help resolve the debate, but rather accentuated it [12]. This raises the question: What is the cause of all these conflicting results?…”

Section: Introductionmentioning

confidence: 99%

“…This technique became known as the detrended fluctuation analysis (DFA). The DFA method became then the "standard" technique in analyzing correlations in DNA sequences and other time-series [12], [34]. However, this method has two drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…The controversy is generated by conflicting views that either advocate that noncoding DNA sustains long-range correlations whereas coding DNA behaves like random sequences [1]- [12] or maintains that both Manuscript received September 7, 2007; revised March 8,2008. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Ioan Tabus.…”

Section: Introductionmentioning

confidence: 99%

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Nonstationary Analysis of Coding and Noncoding Regions in Nucleotide Sequences

Bouaynaya

Schonfeld

2008

IEEE J. Sel. Top. Signal Process.

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Abstract-Previous statistical analysis efforts of DNA sequences revealed that noncoding regions exhibit long-range power law correlations, whereas coding regions behave like random sequences or sustain short-range correlations. A great deal of debate on the presence or absence of long-range correlations in nucleotide sequences, and more specifically in coding regions, has ensued. These results were obtained using signal processing techniques for stationary signals and statistical tools for signals with slowly varying trends superimposed on stationary signals. However, it can be verified using statistical tests that genomic sequences are nonstationary and the nature of their nonstationarity varies and is often much more complex than a simple trend. In this paper, we will bring to bear new tools to analyze nonstationary signals that have emerged in the statistical and signal processing community over the past few years. The emergence of these new methods will be used to shed new light and help resolve the issues of i) the existence of long-range correlations in DNA sequences and ii) whether they are present in both coding and noncoding segments or only in the latter. It turns out that the statistical differences between coding and noncoding segments are much more subtle than previously thought using stationary analysis. In particular, both coding and noncoding sequences exhibit long-range correlations, as asserted by a 1 f (n) evolutionary (i.e., time-dependent) spectrum. However, we will use an index of randomness, which we derive from the Hilbert transform, to demonstrate that coding segments, although not random as previously suspected, are often "closer" to random sequences than noncoding segments. Moreover, we analytically justify the use of the Hilbert spectrum by proving that narrowband nonstationary signals result in a small demodulation error using the Hilbert transform.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonstationary Analysis of Coding and Noncoding Regions in Nucleotide Sequences

Bouaynaya

Schonfeld

2008

IEEE J. Sel. Top. Signal Process.

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“…Herzel and co-workers used binary correlation functions and constructed a dependence matrix that would count all the statistical dependences between all nucleotides Groβe, 1995, 1997;Herzel et al, 1998). In applying this covariance matrix to human chromosomal regions, for example, long-range correlations in human DNA have been observed, correlated to G + C distributions or isochoric structure of genomes (Carpena et al, 2007;Oliver et al, 2008), as well as chromatin structure (Audit et al, 2001); periodicities of 3 and 10-11 bases have been verified in yeast and bacterial DNA. Many authors have considered dinucleotide frequency distributions rather than nucleotide correlations (Kogan and Trifonov, 2005;Cohanim et al, 2006a,b;Kogan et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

A method for extracting period~10 modulations from DNA sequence correlations applied to the Drosophila melanogaster genome

Guerra¹,

Licínio²

2012

Genet. Mol. Res.

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ABSTRACT. Nucleosome DNA packaging and positioning within the Drosophila melanogaster genome imposes a weak modulation, with a period of about 10 bp in the genomic composition correlations. We present formalism for extracting such modulations from an irreducible set of six correlation functions calculated along the D. melanogaster genome. These modulations were seen to be stronger for the irreducible self-correlation C zz (k) (strong-weak binding). Using an FFT procedure, we show that the period~10 modulation extracted from such self-correlation is viewed to be an oscillation with period~10.9 overmodulated by an oscillation with period~153. This behavior of the modulation reflects the organization of the eukaryotic genomic DNA. But, since the period~10 modulation dies for k ~150, the constraints imposed by the nucleosome arrangement over the nucleosome sequence composition must be weak, provided that such constraints are the sources for the modulations.

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“…It has been widely used in computer science, biodynamics, bioinformatics, economics, and meteorology [Peng et al, 1994;Buldyrev et al, 1995;Peng et al, 1995;Heneghan & McDarby, 2000;Siwy et al, 2002;Janosi & Muller, 2005;Santhanam et al, 2006;Masugi, 2006;Carpena et al, 2007].…”

Section: Introductionmentioning

confidence: 99%

Detrended Fluctuation Analysis of the TCP-Red Algorithm

Chen

Wong

Tse

et al. 2009

Int. J. Bifurcation Chaos

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It has been observed that Internet gateways employing Transport Control Protocol (TCP) and the Random Early Detection (RED) control algorithm may exhibit instability and oscillatory behavior. Most control methods proposed in the past have been based on analytical models that rely on statistical measurements of network parameters. In this paper, we apply the detrended fluctuation analysis (DFA) method to analyze stability of the TCP-RED system. The DFA is used to analyze time-series data and generate power-law scaling exponents, which indicate the long-range correlations of the time series. We quantify the stability of the TCP-RED system by examining the variation of the DFA power-law scaling exponent when the system parameters are varied. We also study the long-range power-law correlations of TCP window periods.

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Identifying characteristic scales in the human genome

Cited by 47 publications

References 30 publications

Nonstationary Analysis of Coding and Noncoding Regions in Nucleotide Sequences

Nonstationary Analysis of Coding and Noncoding Regions in Nucleotide Sequences

A method for extracting period~10 modulations from DNA sequence correlations applied to the Drosophila melanogaster genome

Detrended Fluctuation Analysis of the TCP-Red Algorithm

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