The paper by Peng et al. investigates the long-range power-law correlations observed in DNA sequences, particularly in noncoding regions. They explore whether these correlations can be attributed to the known mosaic structure of DNA, characterized by "patches" of different nucleotide compositions. The authors analyze two types of control sequences: one without and one with long-range power-law correlations. Using detrended fluctuation analysis (DFA), they distinguish between these sequences by identifying long-range correlations embedded in a patchy landscape. The DFA method is shown to be effective in detecting long-range correlations and avoiding spurious results due to patchiness. The analysis reveals that patchiness alone is insufficient to explain the long-range correlations found in noncoding regions, as the uncorrelated patch model does not produce values of the scaling exponent \(\alpha\) greater than 0.5. The study also demonstrates that the DFA method can identify the characteristic length scale of biased subregions in uncorrelated control sequences. This research provides a rigorous framework for understanding the complex organization of DNA nucleotides and the nature of long-range correlations in genomic sequences.The paper by Peng et al. investigates the long-range power-law correlations observed in DNA sequences, particularly in noncoding regions. They explore whether these correlations can be attributed to the known mosaic structure of DNA, characterized by "patches" of different nucleotide compositions. The authors analyze two types of control sequences: one without and one with long-range power-law correlations. Using detrended fluctuation analysis (DFA), they distinguish between these sequences by identifying long-range correlations embedded in a patchy landscape. The DFA method is shown to be effective in detecting long-range correlations and avoiding spurious results due to patchiness. The analysis reveals that patchiness alone is insufficient to explain the long-range correlations found in noncoding regions, as the uncorrelated patch model does not produce values of the scaling exponent \(\alpha\) greater than 0.5. The study also demonstrates that the DFA method can identify the characteristic length scale of biased subregions in uncorrelated control sequences. This research provides a rigorous framework for understanding the complex organization of DNA nucleotides and the nature of long-range correlations in genomic sequences.