Compositional Structure of the Genome: A Review

Citation: Bernaola-Galván, P.; Carpena, P.; Gómez-Martín, C.; Oliver, J.L. Compositional Structure of the Genome: A Review. Biology 2023,12, 849. https://doi.org/ 10.3390/biology12060849 Academic Editor: Weiguo Cao Received: 21 April 2023 Revised: 6 June 2023 Accepted: 7 June 2023 Published: 13 June 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). biology Review Compositional Structure of the Genome: A Review Pedro Bernaola-Galván1, Pedro Carpena 1, Cristina Gómez-Martín2,3 and Jose L. Oliver 3,* 1Department of Applied Physics II and Institute Carlos I for Theoretical and Computational Physics, University of Málaga, 29071 Málaga, Spain; [email protected] (P.B.-G.); [email protected] (P.C.) 2Department of Pathology, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands; [email protected] 3Department of Genetics, Faculty of Sciences, 18071 and Laboratory of Bioinformatics, Institute of Biotechnology, Center of Biomedical Research, University of Granada, 18100 Granada, Spain *Correspondence: [email protected] Simple Summary: DNA structural biology deals with the understanding of DNA and three-dimensional chromatin structure, which can determine its function in the cell. The key structural properties of the DNA fiber, such as stability, flexibility, and susceptibility to damage, largely rely on the composition of the DNA sequence. Variations in the nucleotide sequence result in a patchy chromosome structure, which is formed due to the differential GC content of exons, introns, regulatory elements, repeats, etc. The compositional structure of a genome at different length scales may be revealed via the use of entropic segmentation algorithms or fluctuation analysis of DNA walks. The former algorithms divide the four-symbol nucleotide sequence, or its two-symbol variants, into an array of compositionally homogeneous, non-overlapping domains, isochores, and compositional superstructures, all of which are hierarchically organized in the chromosome. Once the compositional structure of a genome is known, the compositional genome signature or sequence compositional complexity (SCC) can be computed, enabling the comparison of genome structures. Abstract: As the genome carries the historical information of a species’ biotic and environmental interactions, analyzing changes in genome structure over time by using powerful statistical physics methods (such as entropic segmentation algorithms, fluctuation analysis in DNA walks, or measures of compositional complexity) provides valuable insights into genome evolution. Nucleotide frequencies tend to vary along the DNA chain, resulting in a hierarchically patchy chromosome structure with heterogeneities at different length scales that range from a few nucleotides to tens of millions of them. Fluctuation analysis reveals that these compositional structures can be classified into three main categories: (1) short-range heterogeneities (below a few kilobase pairs (Kbp)) primarily attributed to the alternation of coding and noncoding regions, interspersed or tandem repeats densities, etc.; (2) isochores, spanning tens to hundreds of tens of Kbp; and (3) superstructures, reaching sizes of tens of megabase pairs (Mbp) or even larger. The obtained isochore and superstructure coordinates in the first complete T2T human sequence are now shared in a public database. In this way, interested researchers can use T2T isochore data, as well as the annotations for different genome elements, to check a specific hypothesis about genome structure. Similarly to other levels of biological organization, a hierarchical compositional structure is prevalent in the genome. Once the compositional structure of a genome is identified, various measures can be derived to quantify the heterogeneity of such structure. The distribution of segment G+C content has recently been proposed as a new genome signature that proves to be useful for comparing complete genomes. Another meaningful measure is the sequence compositional complexity (SCC), which has been used for genome structure comparisons. Lastly, we review the recent genome comparisons in species of the ancient phylum Cyanobacteria, conducted by phylogenetic regression of SCC against time, which have revealed positive trends towards higher genome complexity. These findings provide the first evidence for a driven progressive evolution of genome compositional structure. Biology 2023,12, 849. https://doi.org/10.3390/biology12060849 https://www.mdpi.com/journal/biology Biology 2023,12, 849 2 of 15 Keywords: DNA compositional structure; sequence compositional complexity; segment compositional signature; hierarchical genome structure; evolutionary adaptive trends 1. Introduction DNA structural biology focuses on the understanding of the three-dimensional structure of DNA, which plays a vital role in determining its function in the cell. Key structural properties of the DNA fiber, such as its stability, flexibility, or susceptibility to damage, largely depend on the composition of the DNA sequence (i.e., the specific arrangement of nucleotides within the DNA sequence). Notably, regions of DNA that exhibit a high proportion of guanine and cytosine (known as GC-rich regions) tend to possess a more stable structure due to the stronger hydrogen bonding that occurs between these nucleotides. The presence of histones or histone-like proteins in the genome can also have a significant impact on genome structure organization and long-range genome interactions. Recent advances have enabled chart maps of histone modifications and related chromatin structures. These maps provide insights into the intricate relationship between chromatin and genome function, emerging in the understanding of large-scale domains and higher-ordered chromatin organization [1]. Since the genome encodes all historical information regarding a species’ biotic and environmental interactions, the analysis of genome compositional changes may provide important insights into the organization and evolution of genome structure over time [ 2 – 4 ]. Pioneering studies by Bernardi and collaborators [ 5 – 8 ] used the analytical ultracentrifugation of bulk DNA to uncover the observation that mammalian genomes are made up of isochores, which are long DNA segments of a typical size around 300 kb, and they are fairly homogeneous in G+C content. Nucleotide composition has been related to important genome features such as the so-called genomic code [ 9 ], the 3-D structure of DNA [ 10 ], or the existence of topologically associating domains (TADs) in the genome [ 11 , 12 ]. The recent achievement of the first complete 3.055 billion base pair sequence of a human genome [ 13 ] now provides an unprecedented opportunity to examine the isochore theory and study the evolution of DNA sequence structure. The application of statistical physics methods (such as power spectra, fluctuation analysis in DNA walks, or entropic segmentation) has facilitated the study of large-scale genome structures [ 14 – 17 ], revealing the presence of longrange, power law correlations in DNA sequences and suggesting a fractal (scale-invariant) structure of the genome. However, such a genome landscape directly contradicted the well-known characteristic lengths observed in most genome elements (genes, exons, introns, transposable elements, and so on). It was also particularly inconsistent with the isochore theory of the genome, which describes the genome as a mosaic of compositionally homogeneous segments known as isochores [ 2 , 6 , 18 – 21 ]. The paradox between a scale-invariant versus an isochore model for the genome was resolved by discovering that deviations from power law behavior can be observed in correlations [ 22 ] and that such deviations can be associated with isochore-like regions in, at that time, the best-sequenced eukaryotic genome assemblies [ 21 , 23 ]. Subsequent studies revealed that isochores are not the longest compositional domains in the genome as they are organized at even longer scales into compositional superstructures that are about two orders of magnitude longer than isochores [ 24 ]. By using a segmentation model that accounts for long-range correlations, these authors were able to determine a genomic map of the chromosome boundaries of superstructures based on rigorous statistical criteria. Several measures of genome complexity are now available [ 25 – 28 ]; we refer the reader to [ 29 ] for a recent review. Some of these measures rely on the frequencies of k-words or k-mers. The complexity of the DNA sequence is computed at a length scale given by the word size (k), which maximizes the variability of word frequencies. However, given the presence of different length scales in the genome [ 15 , 21 ], choosing the right value for the parameter kis not an easy task. Biology 2023,12, 849 3 of 15 An ideal measure of compositional structural complexity would consider the array of homogeneous domains of different lengths and compositions along the genome. Subsequently, it would calculate a complexity value based on statistical criteria. Sequence compositional complexity, or SCC [ 25 ], fulfills these criteria. This method first decomposes the nucleotide sequence into a number of homogeneous compositional domains under strict statistical criteria and then incorporates an entropic measure that accounts for the length and compositional differences among these domains. Recently, SCC has been employed to assess genome complexity in Cyanobacteria [ 3 ], providing the first evidence for the driven progressive evolution of genome compositional structure. This review provides an overview of the theory and methods for DNA sequence segmentation (Section 2) and the modifications of the original segmentation algorithm, which can be used to find the largest genome compositional structures: isochores (Section 3) and superstructures (Section 4). Sections 5and 6focus on the use of compositional segmentation to quantify the compositional structure of genomes. Specifically, we show that the distribution of segment G+C content has the desirable properties of a genome signature (Section 5), while in Section 6, we review sequence compositional complexity (SCC), a measure particularly convenient for quantifying the complexity of genome structure. In Section 7, we show the usefulness of SCC for uncovering phylogenetic trends in the ancient phylum Cyanobacteria, in which evidence for the driven progressive evolution of SCC was first found. Finally, in Section 8, we present conclusions. 2. DNA Sequence Segmentation Given the pervasive spatial heterogeneity in nucleotide composition found in most genomes [ 30 ], the identification of compositional domains within a sequence is essential to understand genome structure [ 2 ]. As a result, this task holds significant importance in computational molecular biology [ 31 ], being the key step in understanding the spatial, largescale structure of the genome. In simple DNA sequences that lack long-range correlations, such as those predominantly integrated by coding regions in prokaryotes, compositional patches can be easily identified [ 32 ]. However, for complex long-range correlated DNA sequences typical of eukaryotic genomes, the identification of homogeneous compositional domains becomes more challenging given the lack of a characteristic patch length [ 33 , 34 ]. To overcome this problem, a statistical approach capable of estimating, with a given level of confidence, the location of the boundaries separating compositional patches in a sequence should be used. To divide a four-symbol DNA sequence into an array of compositionally homogeneous, non-overlapping domains, a heuristic, iterative segmentation algorithm [ 19 , 35 – 37 ] can be used. In brief, given a DNA sequence Sof size N, a sliding cursor is moved along the sequence (i = 1, . . . ,N), and the position, i, that optimizes a proper measure of compositional divergence between the left (S 1 ) and right (S 2 ) parts is selected. We choose the Jensen– Shannon divergence [ 36 ] as a divergence measure, as it can be directly applied to symbolic nucleotide sequences. The Jensen–Shannon divergence (JS) between two sequences, S 1 and S2, is defined as follows: JS(S1,S2)=H(S)−hn1 NH(S1)+n2 NH(S2)i(1) where n 1 and n 2 are the sizes of sequences S 1 and S 2 ,Sis the sequence of size N=n 1 + n 2 obtained by putting together S 1 and S 2 , and H( · ) is the Shannon entropy of the distribution of the relative frequencies of symbol occurrences: H(S)=−∑ i=A,T,C,G filog2fi(2) If the divergence is statistically significant at a given significance level (e.g., s = 0.95), the sequence is split into two segments. Note that each pair of resulting segments is more homogeneous than the original sequence. The two resulting segments are then Biology 2023,12, 849 4 of 15 independently subjected to a new round of segmentation. The process continues iteratively over the new segments while sufficient significance continues appearing. Since Shannon entropy is invariant under symbol interchange, the segmentation algorithm and the SCC values derived from it are invariable relative to sequence orientation. Note that this segmentation algorithm can be easily generalized to accommodate other alphabets that are different from the standard four-letter one (A, T, C, and G) while preserving its properties. For instance, a twelve-letter alphabet (the four letters in the three codon positions) can be used to determine coding region borders [ 38 ]. In addition, if the sole interest lies in the compositional structure of G+C content, the algorithm can be adapted to a two-letter alphabet: Prior to segmentation, the four-letter DNA sequence is converted into a binary sequence with only two symbols comprising S (strong) when the nucleotide is C or G and W (weak) when the nucleotide is A or T. This mapping of the DNA sequence into a binary sequence is commonly known as strong/weak or simply the S/W rule. Specifically, this alphabet is used in the prediction of isochore boundaries (Section 3) and the search for compositional superstructures (Section 4.1). The statistical significance level, s, represents the probability that the difference between adjacent domains is not solely due to statistical fluctuations, assuming the null hypothesis that the sequence is random and i.i.d. (independent and identically distributed). By adjusting the value of this parameter, one can obtain the underlying distribution of segment lengths and nucleotide compositions at different levels of detail [ 37 ], thus conveniently fulfilling one of the key requirements of a complexity measure [ 28 ]. Choosing a random i.i.d. sequence as the null hypothesis serves as a reference for homogeneity. In other words, a sequence is considered heterogeneous (and therefore, should be segmented) when differences in composition exceed what would be expected in a random i.i.d. sequence. Recent improvements to this segmentation algorithm [ 37 ] have enabled the segmentation of sequences with long-range correlations. The presence of correlations makes these sequences much more heterogeneous than random i.i.d. sequences; consequently, the method for computing significance level s, i.e., the reference for homogeneity, needs to be modified, as the algorithm may otherwise identify segments that appear trivially in the sequence due to the correlations. In such cases, the model adopted for homogeneous sequences is fractional Gaussian noise. Implementation details, source codes, and executable binaries for different operating systems can be downloaded from the following website: https://github.com/bioinfoUGR/segment (accessed on 20 April 2023) and https://github.com/bioinfoUGR/isofinder (accessed on 20 April 2023). In all cases, the result is the segmentation of the original sequence into an array of contiguous, non-overlapping segments (or compositional domains) for which their nucleotide composition is homogeneous at the chosen significance level, s. 3. Prediction of Isochore Boundaries at the Sequence Level The genomes of warm-blooded vertebrates (such as mammals and birds) are made up of isochores, which are long DNA segments (~300 kb) that are fairly homogeneous in G+C content and that were first revealed by the analytical ultracentrifugation of bulk DNA [ 5 – 8 ]. The relevance of isochores is derived from the distinctive frequencies of genes, SINE (short interspersed repetitive elements), and recombination frequency, which are all higher in (G+C)-rich isochores, whereas LINEs (long interspersed repetitive elements) are denser in (G+C)-poor isochores [ 7 ]. Beyond compositional differences, the boundaries of isochore often correspond to chromosome regions that differ in replication timing, as observed in the isochores of the human major histocompatibility complex (MHC) locus [ 39 ], or in recombination rates, as observed in the human neurofibromatosis NF1 region [ 40 ]. Isochores can be found in a large variety of taxa, including unicellular eukaryotes [ 41 ], plants [ 42 ], and cold-blooded vertebrates [ 43 ], although they are more conspicuous in the genome of warm-blooded vertebrates (see [ 7 ] and references therein). The isochore theory has expanded our understanding of the complexity and compositional variability of eukaryotic genomes [ 44 ], and it is considered a fundamental level of genome organization [ 45 , 46 ]. The Biology 2023,12, 849 5 of 15 evolutionary origin and maintenance of isochores in present-day genomes has been subject to active debate [20,45,47–50]. The advent of large-scale DNA sequencing projects generating a substantial number of large DNA sequences [ 51 ] has led to the search for a direct test of the isochore theory. Our group developed an algorithm, based on the compositional segmentation described above, that is able to predict isochore boundaries at the sequence level [19,25,36,52]. Most large homogeneous genome regions predicted by this algorithm were identified with Bernardi’s isochores, showing correlations with biological features such as gene density, SINE and LINE (short, long interspersed repetitive elements) densities, recombination rate, and SNP (single nucleotide polymorphism) variability [ 52 , 53 ]; its accuracy as compared with other methods when applied to natural as well as simulated sequences has been proven [ 31 , 52 , 54 , 55 ]. Note however that if one chooses to conduct simulation experiments, a problem arises in which the sequences generated in the experiment have to be at least as complex as the natural ones, which is not an easy task because the sequences of higher organisms (mainly mammals and birds) usually have long-range power law correlations. In this way, a complete, reliable test for the isochore theory requires high-quality T2T genome sequences (see below). The IsoFinder algorithm is designed to predict isochore boundaries [ 19 , 52 , 53 ], and it works as follows. Since we are interested in segments with defined G+C content, in the first place, the sequence to be analyzed is converted into a binary sequence using the S/W rule (Section 2). As we wish to detect only isochore-like DNA segments, we need to modify the original segmentation algorithm in order to avoid the influence of short-scale G+C heterogeneities on statistical significance. Thus, we filter nucleotide heterogeneities below a given minimum length, L 0 , and then compute the GC% content in left and right windows. In this way, we convert the subsequence of length Lleft (Lright) into an array of Lleft/L 0 (Lright/L 0 ) real numbers corresponding to the GC% content of each window of size L 0 .IsoFinder allows the user to choose among three different values of L 0 (1, 2, and 3 kb) to perform the filtering procedure. It is advisable, however, to use L 0 = 3 kb, which corresponds to a homogeneity criterion for mammalian isochores, and it is derived from the ultracentrifugation of DNA of different molecular weights [6]. One of the main limiting factors relative to validating the isochore theory was the lack of high-quality DNA sequences. To overcome this problem, we applied Isofinder to the first complete 3.055 billion base pair T2T sequence of a human genome [ 13 ]. The obtained isochore and superstructure coordinates were then shared in a public database (https: //genome.ucsc.edu/s/oliver/T2T%20human%20isochores (accessed on 20 April 2023 )). Using PacBio HiFi and Oxford Nanopore, ultralong-read sequencing gapless assemblies were obtained for all chromosomes (except Y) of the homozygous CHM13hTERT cell line, which corrects errors in the prior references and introduces nearly 200 million base pairs of additional sequences [ 13 ]. Table 1shows the lengths and GC% of T2T human isochores by chromosome, while Table 2shows a summary statistic. The T2T human isochore map of chromosome 1, obtained with IsoFinder and plotted with the help of the UCSC Genome Browser [ 56 , 57 ], is shown in Figure 1. The online isochore maps for all chromosomes are available at the UCSC Genome Browser: https://genome.ucsc.edu/s/oliver/T2T%20h uman%20isochores (accessed on 20 April 2023). Note that besides the image of isochore maps for every chromosome, this website provides access to tables with specific genome coordinates for each isochore by using Table Browser (https://genome.ucsc.edu/cgi-bi n/hgTables?hgsid=1627501187_vJA8raAFaxmpp4jAbRjEuzBiYujn (accessed on 20 April 2023)). In this way, interested researchers can now use T2T isochore data, as well as the annotations for different genome elements available on this website, to check a specific hypothesis about genome structure. Biology 2023,12, 849 6 of 15 Table 1. Lengths and GC% of T2T human isochores by chromosome. Length GC% Chromosome NMin Median Max Min Median Max chr1 1113 30,004 102,983 5,403,580 32.80 43.02 67.96 chr2 897 30,004 128,804 4,304,270 31.57 41.20 66.39 chr3 656 30,004 127,485 5,001,190 21.54 41.23 62.17 chr4 427 30,004 234,223 5,642,550 23.80 39.15 72.64 chr5 561 30,004 164,800 7,206,270 30.21 40.70 62.46 chr6 510 30,004 163,262 3,500,380 32.19 40.85 58.39 chr7 596 30,004 119,830 3,412,220 33.19 42.44 68.05 chr8 449 30,004 147,916 4,875,420 33.27 41.36 63.98 chr9 501 30,004 107,885 22,256,800 31.74 42.57 65.87 chr10 562 30,004 121,504 3,195,210 32.63 42.09 72.51 chr11 551 30,004 109,143 3,008,680 33.64 42.81 62.56 chr12 505 30,005 115,060 3,649,550 32.99 42.49 63.91 chr13 317 30,004 128,955 10,449,500 21.22 40.27 60.57 chr14 426 30,004 101,284 3,881,480 21.89 42.01 63.58 chr15 461 30,004 104,795 7,482,370 21.37 42.62 62.03 chr16 457 30,004 82,508 12,645,100 33.24 44.86 66.31 chr17 517 30,004 82,785 4,713,850 33.08 45.61 62.57 chr18 237 30,004 180,918 3,584,850 34.03 40.14 56.08 chr19 313 30,004 101,006 2,676,290 35.20 48.09 65.30 chr20 314 30,004 101,682 2,232,290 32.89 44.17 65.15 chr21 178 30,004 95,755 4,852,870 21.63 42.14 63.20 chr22 342 30,004 68,700 1,690,830 21.25 46.26 64.72 chrX 366 30,004 166,946 14,835,700 22.41 40.66 62.24 Biology 2023, 12, x FOR PEER REVIEW 6 of 16 Table 1. Lengths and GC% of T2T human isochores by chromosome. Length GC% Chromosome N Min Median Max Min Median Max chr1 1113 30,004 102,983 5,403 , 580 32.80 43.02 67.96 chr2 897 30,004 128,804 4,304,270 31.57 41.20 66.39 chr3 656 30,004 127,485 5,001,190 21.54 41.23 62.17 chr4 427 30,004 234,223 5,642,550 23.80 39.15 72.64 chr5 561 30,004 164,800 7,206,270 30.21 40.70 62.46 chr6 510 30,004 163,262 3,500,380 32.19 40.85 58.39 chr7 596 30,004 119,830 3,412,220 33.19 42.44 68.05 chr8 449 30,004 147,916 4,875,420 33.27 41.36 63.98 chr9 501 30,004 107,885 22,256,800 31.74 42.57 65.87 chr10 562 30,004 121,504 3,195,210 32.63 42.09 72.51 chr11 551 30,004 109,143 3,008,680 33.64 42.81 62.56 chr12 505 30,005 115,060 3,649,550 32.99 42.49 63.91 chr13 317 30,004 128,955 10,449,500 21.22 40.27 60.57 chr14 426 30,004 101,284 3,881,480 21.89 42.01 63.58 chr15 461 30,004 104,795 7,482,370 21.37 42.62 62.03 chr16 457 30,004 82,508 12,645,100 33.24 44.86 66.31 chr17 517 30,004 82,785 4,713,850 33.08 45.61 62.57 chr18 237 30,004 180,918 3,584,850 34.03 40.14 56.08 chr19 313 30,004 101,006 2,676,290 35.20 48.09 65.30 chr20 314 30,004 101,682 2,232,290 32.89 44.17 65.15 chr21 178 30,004 95,755 4,852,870 21.63 42.14 63.20 chr22 342 30,004 68,700 1,690,830 21.25 46.26 64.72 chrX 366 30,004 166,946 14,835,700 22.41 40.66 62.24 Table 2. Basic length and GC% statistics in T2T human isochores. N Minimum Median Maximum Length (bp) 11,256 30,005.00 116,447.00 22,256,800.00 GC% 11,256 21.22 42.24 72.64 Figure 1. T2T human isochores. The image shows the isochore map of the T2T-CHM13 DNA sequence of human chromosome 1, obtained by plotting the isochores predicted by IsoFinder [19] with the help of the UCSC Genome Browser [56,57]. Blue line indicate the GC content of each isochore. The complete chromosome sequence was obtained by the Telomere-to-Telomere (T2T) Consortium [13], which includes gapless assemblies for all chromosomes except Y. The completed regions include all centromeric satellite arrays and recent segmental duplications. Tracks for G+C density in 5-base windows, genes, and CpG islands, taken from the UCSC Genome Browser database, are also plotted for comparison. The online isochore maps for all chromosomes are available at the UCSC Genome Browser: https://genome.ucsc.edu/s/oliver/T2T%20human%20isochores (accessed on 20 April 2023). Figure 1. T2T human isochores. The image shows the isochore map of the T2T-CHM13 DNA sequence of human chromosome 1, obtained by plotting the isochores predicted by IsoFinder [ 19 ] with the help of the UCSC Genome Browser [ 56 , 57 ]. Blue line indicate the GC content of each isochore. The complete chromosome sequence was obtained by the Telomere-to-Telomere (T2T) Consortium [ 13 ], which includes gapless assemblies for all chromosomes except Y. The completed regions include all centromeric satellite arrays and recent segmental duplications. Tracks for G+C density in 5-base windows, genes, and CpG islands, taken from the UCSC Genome Browser database, are also plotted for comparison. The online isochore maps for all chromosomes are available at the UCSC Genome Browser: https://genome.ucsc.edu/s/oliver/T2T%20human%20isochores (accessed on 20 April 2023). Biology 2023,12, 849 7 of 15 Table 2. Basic length and GC% statistics in T2T human isochores. N Minimum Median Maximum Length (bp) 11,256 30,005.00 116,447.00 22,256,800.00 GC% 11,256 21.22 42.24 72.64 4. Long-Range Correlations and Compositional Superstructures in the Genome The application of statistical physics methods to DNA sequences led to the discovery of long-range correlations, i.e., correlations between nucleotides over long distances along the DNA chain [ 15 – 17 , 33 , 58 ]. The stochastic properties of nucleotide sequences were studied by constructing a 1:1 map of the nucleotide sequence onto a walk (DNA walk). In this way, the mapping was used to provide a quantitative measure of the correlation between nucleotides over long distances along the DNA chain. Long-range power law correlations uncovered by these powerful methods imply a new scale-invariant property of DNA. However, from the point-of-view of genome structure, these long-range, power law fractal correlations also imply that compositional segments should appear at all scales (i.e., showing a power law distribution of segment lengths), a prediction that was confirmed by analyzing the length distributions of compositional domains resulting from sequence segmentation [ 36 ]. The emerging genome landscape of genome structure challenges the conventional notion of characteristic lengths shown by most genomic elements (such as genes, exons, introns, transposable elements, etc.), but above all, it is particularly incompatible with the view of the genome as a mosaic of long homogeneous segments or isochores with a typical size [ 6 , 18 ]. This apparent contradiction was solved by using detrended fluctuation analysis (DFA [ 34 ]), which revealed the presence of at least three characteristic scales in human chromosomes: short-scale elements (genes, introns, exons, repeats, etc.), medium-scale lengths (corresponding to the typical isochore size), and very-large-scale genome elements (compositional superstructures [21]). 4.1. Detection of Genome Compositional Superstructures by Segmentation DFA analysis shows that isochores with a median length of 116 Kbp (Table 2) are not the largest compositional structures in the genome. Two independent methods were used to show that isochores are in fact organized into longer compositional structures [ 24 ]. On one hand, compositional autocorrelation analysis was employed to examine the G+C content of isochores. This analysis revealed that the G+C content of isochores is not independent between each other but exhibits correlations over very large distances, indicating the existence of the clusters of isochores of similar G+C compositions. On the other hand, DNA walks [ 15 ] were used to demonstrate the existence of enormous DNA segments (median size ~6 Mbp, Tables 3and 4) with a defined G+C composition and typical sizes that are consistent with the sizes of the isochore clusters obtained via autocorrelation analysis. These segments, called compositional superstructures [ 24 ], were obtained by means of a modified segmentation algorithm applied to the DNA sequence that was previously converted into a binary S/W sequence (see Section 2). In brief, this modification consists of changing the criterion to evaluate the significance level. While the regular segmentation considers that a sequence is homogeneous (i.e., it remains unsegmented) when its heterogeneity is similar to that in a random sequence, the modified segmentation algorithm takes into account long-range correlations in the DNA chain. In this way, the model for homogeneous DNA sequence comprises fractional Gaussian noise instead of a pure i.i.d. random sequence [ 37 ]. This algorithm, when applied to entire chromosome sequences, is capable of systematically detecting the boundaries between the above-mentioned large compositional superstructures using rigorous statistical criteria. The coordinates for the 113 superstructures detected in T2T human chromosome sequences are shown in Table S1. Table 3shows the statistics of lengths and GC% of human T2T superstructures grouped by chromosome, while Table 4shows a summary statistic. Biology 2023,12, 849 8 of 15 Table 3. Length and GC% statistics of human T2T genome superstructures by chromosome. Length GC% Chromosome NMin Median Max Min Median Max chr1 4 328,708.00 23,816,031.50 200,426,557.00 40.02 45.53 58.20 chr2 4 3,360,333.00 51,107,636.50 137,121,146.00 38.23 43.43 50.92 chr3 3 36,078,355.00 73,698,202.00 91,329,391.00 35.28 39.59 41.28 chr4 4 489,000.00 4,883,872.00 183,318,201.00 37.50 44.89 55.27 chr5 6 1,719,665.00 10,580,851.50 128,874,195.00 38.12 44.70 53.09 chr6 5 4,367,830.00 18,410,867.00 104,707,592.00 37.67 41.22 46.35 chr7 4 2,840,530.00 7,380,668.00 142,965,562.00 39.85 46.63 54.87 chr8 5 532,709.00 18,506,340.00 74,252,182.00 38.17 41.71 55.45 chr9 5 745,901.00 7,358,207.00 105,799,637.00 37.88 48.44 56.06 chr10 6 31,699.00 790,594.50 122,866,079.00 41.00 45.76 72.51 chr11 3 230,004.00 3,096,068.00 131,801,697.00 41.22 42.54 55.51 chr12 5 295,312.00 8,431,710.00 98,848,568.00 39.05 45.18 52.94 chr13 7 167,828.00 3,576,422.00 99,522,852.00 21.87 42.77 57.94 chr14 6 935,799.00 5,282,438.00 78,687,833.00 39.51 45.26 55.98 chr15 1 99,753,195.00 99,753,195.00 99,753,195.00 42.12 42.12 42.12 chr16 8 2,323,621.00 6,794,467.50 37,570,616.00 36.35 46.71 57.98 chr17 3 10,136,275.00 22,311,188.00 51,829,434.00 41.51 45.56 52.19 chr18 6 299,493.00 2,825,872.00 64,251,224.00 35.92 42.88 53.05 chr19 7 33,056.00 4,915,345.00 30,358,228.00 35.71 47.91 56.35 chr20 4 2,912,961.00 16,593,634.00 30,110,026.00 40.53 45.21 55.82 chr21 9 307,155.00 1,240,965.00 22,967,603.00 21.63 43.15 58.18 chr22 2 17,629,880.00 25,662,463.00 33,695,046.00 41.31 44.77 48.23 chrX 6 272,109.00 1,306,918.50 148,323,701.00 39.21 46.71 55.04 Table 4. Summary statistics of length and GC% in human T2T genome superstructures. NMinimum Median Maximum Length (bp) 113 31,699.00 6,111,300.50 200,427,000.00 GC% 113 21.63 45.10 72.51 4.2. Hierarchical Organization of Compositional Genome Structures It is widely known that the GC content of nucleotide sequences usually varies between different genomic elements or regions within the chromosome: exons vs. introns; early vs. late replication genes; regions rich in shortvs. long-interspersed repeats; CpG-poor vs. CpG-rich regions; Gvs. R-chromosome bands; and between different classes of chromosome territories [59,60], isochores [6], and genome superstructures [24]. The lower lengths of isochores compared to superstructures mean that both compositional structures are imbricated in the chromosome, usually being the first contained within the second ones. Table S1 shows the number, length, and GC% of isochores embedded within each superstructure of the T2T human genome, showing again that superstructures are nothing more than the high-level organization of isochores in the genome [ 24 ]. There is a median average of 33 isochores by superstructure, reaching a maximum of 796 isochores within the largest superstructure (>200 Mbp) of human chromosome 1. Note that there also exist some superstructures devoid of isochores, above all in the 5 0 and 3 0 ends of some chromosomes. Biology 2023,12, 849 9 of 15 The isochores’ organization within superstructures in the genome is beautifully illustrated by wavelet analysis (Figure 2), as well as by the genome maps of both structures obtained with the help of the UCSC Genome Browser (Figure 3). Biology 2023, 12, x FOR PEER REVIEW 9 of 16 Figure 2. Variation in nucleotide composition along T2T human chromosome 22 at scales between 10 Kbp and 4 Mbp, as revealed by wavelets. The two genome superstructures of this chromosome (green on the left and red on the right) are clearly revealed. The finer-grained isochore structure at lower scales is also discernible. Figure 3. Comparison of isochore and superstructure maps in human T2T chromosome 21 by means of the UCSC Genome Browser. The online image can be observed at the following website: https://genome.ucsc.edu/cgi-bin/hgTracks?db=hub_3267197_GCA_009914755.4&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr21%3A1%2D45090682&hgsid=1583990213_Mq2SxT3AB7CVJP4gui2lAeO3ZljM (accessed on 20 April 2023). The blue lines indicate the GC content of each isochore or superstructure. The orange arrows point to a region of chromosome 21 with low GC content, known as the big "gene desert." The green arrows indicate a region where isochore and superstructure boundaries overlap. The organization of all these interdependent structural compositional components into different length scales, ranging from individual nucleotides to entire chromosomes, leads to a complex compositional structure of the genome. This genome complexity is hierarchically organized, as shown by the existence of domains within domains [36,61] and isochores within genome superstructures [24]. In this way, as it occurs in other levels of biological organization [62], hierarchical complexity is the rule for the compositional structure of the genome. 4.3. Functional Significance of Compositional Structures The significant variations in gene, SINE, and LINE densities [7,63], as well as differences in replication timing [39] and recombination rates [40], all point to the biological meaning of the compartmentalized genome structure. Figure 2. Variation in nucleotide composition along T2T human chromosome 22 at scales between 10 Kbp and 4 Mbp, as revealed by wavelets. The two genome superstructures of this chromosome (green on the left and red on the right) are clearly revealed. The finer-grained isochore structure at lower scales is also discernible. Biology 2023, 12, x FOR PEER REVIEW 9 of 16 Figure 2. Variation in nucleotide composition along T2T human chromosome 22 at scales between 10 Kbp and 4 Mbp, as revealed by wavelets. The two genome superstructures of this chromosome (green on the left and red on the right) are clearly revealed. The finer-grained isochore structure at lower scales is also discernible. Figure 3. Comparison of isochore and superstructure maps in human T2T chromosome 21 by means of the UCSC Genome Browser. The online image can be observed at the following website: https://genome.ucsc.edu/cgi-bin/hgTracks?db=hub_3267197_GCA_009914755.4&lastVirtModeType=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&position=chr21%3A1%2D45090682&hgsid=1583990213_Mq2SxT3AB7CVJP4gui2lAeO3ZljM (accessed on 20 April 2023). The blue lines indicate the GC content of each isochore or superstructure. The orange arrows point to a region of chromosome 21 with low GC content, known as the big "gene desert." The green arrows indicate a region where isochore and superstructure boundaries overlap. The organization of all these interdependent structural compositional components into different length scales, ranging from individual nucleotides to entire chromosomes, leads to a complex compositional structure of the genome. This genome complexity is hierarchically organized, as shown by the existence of domains within domains [36,61] and isochores within genome superstructures [24]. In this way, as it occurs in other levels of biological organization [62], hierarchical complexity is the rule for the compositional structure of the genome. 4.3. Functional Significance of Compositional Structures The significant variations in gene, SINE, and LINE densities [7,63], as well as differences in replication timing [39] and recombination rates [40], all point to the biological meaning of the compartmentalized genome structure. Figure 3. Comparison of isochore and superstructure maps in human T2T chromosome 21 by means of the UCSC Genome Browser. The online image can be observed at the following website: https://genome.ucsc.edu/cgi-bin/hgTracks?db=hub_3267197_GCA_009914755.4&lastVirtModeTy pe=default&lastVirtModeExtraState=&virtModeType=default&virtMode=0&nonVirtPosition=&posi tion=chr21%3A1%2D45090682&hgsid=1583990213_Mq2SxT3AB7CVJP4gui2lAeO3ZljM (accessed on 20 April 2023 ). The blue lines indicate the GC content of each isochore or superstructure. The orange arrows point to a region of chromosome 21 with low GC content, known as the big “gene desert”. The green arrows indicate a region where isochore and superstructure boundaries overlap. The organization of all these interdependent structural compositional components into different length scales, ranging from individual nucleotides to entire chromosomes, leads to a complex compositional structure of the genome. This genome complexity is hierarchically organized, as shown by the existence of domains within domains [ 36 , 61 ] and isochores within genome superstructures [ 24 ]. In this way, as it occurs in other levels of biological organization [ 62 ], hierarchical complexity is the rule for the compositional structure of the genome. 4.3. Functional Significance of Compositional Structures The significant variations in gene, SINE, and LINE densities [ 7 , 63 ], as well as differences in replication timing [ 39 ] and recombination rates [ 40 ], all point to the biological meaning of the compartmentalized genome structure.