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Li, Wentian; Bernaola‐Galván, Pedro; Carpena, Pedro; Oliver, José Luis Tejera

doi:10.1186/gb-2002-3-11-preprint0009

Cited by 4 publications

(4 citation statements)

References 21 publications

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“…Thankfully, the human genome is well-known to be compartmentalized into wide (∼10 5 bp) domains with uniform G+C content, commonly called isochores, − with appreciable scatter of the average G+C content when comparing different domains. One of the main isochore characteristics is the fact that the variance of the G+C fluctuations increases significantly with the mean G+C content.…”

Section: Discussionmentioning

confidence: 99%

Effect of Genomic Long-Range Correlations on DNA Persistence Length: From Theory to Single Molecule Experiments

et al. 2010

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Sequence dependency of DNA intrinsic bending properties has been emphasized as a possible key ingredient to in vivo chromatin organization. We use atomic force microscopy (AFM) in air and liquid to image intrinsically straight (synthetic), uncorrelated (hepatitis C RNA virus) and persistent long-range correlated (human) DNA fragments in various ionic conditions such that the molecules freely equilibrate on the mica surface before being captured in a particular conformation. 2D thermodynamic equilibrium is experimentally verified by a detailed statistical analysis of the Gaussian nature of the DNA bend angle fluctuations. We show that the worm-like chain (WLC) model, commonly used to describe the average conformation of long semiflexible polymers, reproduces remarkably well the persistence length estimates for the first two molecules as consistently obtained from (i) mean square end-to-end distance measurement and (ii) mean projection of the end-to-end vector on the initial orientation. Whatever the operating conditions (air or liquid, concentration of metal cations Mg(2+) and/or Ni(2+)), the persistence length found for the uncorrelated viral DNA underestimates the value obtained for the straight DNA. We show that this systematic difference is the signature of the presence of an uncorrelated structural intrinsic disorder in the hepatitis C virus (HCV) DNA fragment that superimposes on local curvatures induced by thermal fluctuations and that only the entropic disorder depends upon experimental conditions. In contrast, the WLC model fails to describe the human DNA conformations. We use a mean-field extension of the WLC model to account for the presence of long-range correlations (LRC) in the intrinsic curvature disorder of human genomic DNA: the stronger the LRC, the smaller the persistence length. The comparison of AFM imaging of human DNA with LRC DNA simulations confirms that the rather small mean square end-to-end distance observed, particularly for G+C-rich human DNA molecules, more likely results from a large-scale intrinsic curvature due to a persistent distribution of DNA curvature sites than from some increased flexibility.

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Section: Discussionmentioning

confidence: 99%

Effect of Genomic Long-Range Correlations on DNA Persistence Length: From Theory to Single Molecule Experiments

et al. 2010

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“…DFA is a robust method suitable for detecting longrange power-law correlations embedded in nonstationary signals [11,12]. It has been successfully applied to a variety of fields where scale-invariant behavior emerges, such as DNA [11,[13][14][15][16][17][18][19][20][21][22][23][24][25][26], cardiac dynamics [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46], human locomotion [5,[47][48][49], circadian rhythm [50][51][52][53], neural receptors in biological systems [54], seismology [55,56], meteorology [57], climate temperat...…”

Section: Introductionmentioning

confidence: 99%

Effect of extreme data loss on long-range correlated and anticorrelated signals quantified by detrended fluctuation analysis

et al. 2010

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Detrended fluctuation analysis (DFA) is an improved method of classical fluctuation analysis for nonstationary signals where embedded polynomial trends mask the intrinsic correlation properties of the fluctuations. To better identify the intrinsic correlation properties of real-world signals where a large amount of data is missing or removed due to artifacts, we investigate how extreme data loss affects the scaling behavior of long-range power-law correlated and anticorrelated signals. We introduce a segmentation approach to generate surrogate signals by randomly removing data segments from stationary signals with different types of long-range correlations. The surrogate signals we generate are characterized by four parameters: (i) the DFA scaling exponent α of the original correlated signal u(i), (ii) the percentage p of the data removed from u(i), (iii) the average length μ of the removed (or remaining) data segments, and (iv) the functional form P(l) of the distribution of the length l of the removed (or remaining) data segments. We find that the global scaling exponent of positively correlated signals remains practically unchanged even for extreme data loss of up to 90%. In contrast, the global scaling of anticorrelated signals changes to uncorrelated behavior even when a very small fraction of the data is lost. These observations are confirmed on two examples of real-world signals: human gait and commodity price fluctuations. We further systematically study the local scaling behavior of surrogate signals with missing data to reveal subtle deviations across scales. We find that for anticorrelated signals even 10% of data loss leads to significant monotonic deviations in the local scaling at large scales from the original anticorrelated to uncorrelated behavior. In contrast, positively correlated signals show no observable changes in the local scaling for up to 65% of data loss, while for larger percentage of data loss, the local scaling shows overestimated regions (with higher local exponent) at small scales, followed by underestimated regions (with lower local exponent) at large scales. Finally, we investigate how the scaling is affected by the average length, probability distribution, and percentage of the remaining data segments in comparison to the removed segments. We find that the average length μr of the remaining segments is the key parameter which determines the scales at which the local scaling exponent has a maximum deviation from its original value. Interestingly, the scales where the maximum deviation occurs follow a power-law relationship with μr. Whereas the percentage of data loss determines the extent of the deviation. The results presented in this paper are useful to correctly interpret the scaling properties obtained from signals with extreme data loss.

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“…Another reason to focus our interest on the G + C composition of DNA is because, since the availability of the complete human genome [76], an intense controversy about the large scale organization of G + C composition of human DNA [45,76–78] has arisen. This problem is closely related to the results presented here since most of the above referred controversy is due to the fact that different authors define “homogeneity” in different ways: random uncorrelated sequence [76], homogeneous above a given scale [43,44], etc.…”

Section: Resultsmentioning

confidence: 99%