Computational Biology and Chemistry 53 (2014) 71–78
Contents lists available at ScienceDirect
Computational Biology and Chemistry
journal homepage: www.elsevier.com/locate/compbiolchem
Research Article
DNA clustering and genome complexity
Francisco Diosa,b
, Guillermo Barturena,b
, Ricardo Lebróna,b
, Antonio Ruedac
,
Michael Hackenberga,b
, José L. Olivera,b,∗
a
Dpto. de Genética, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain
b
Lab. de Bioinformática, Inst. de Biotecnología, Centro de Investigación Biomédica, 18100 Granada, Spain
c
Plataforma Andaluza de Genómica y Bioinformática (GBPA), Edificio INSUR, Calle Albert Einstein, 41092 Sevilla, Spain
a r t i c l e i n f o
Article history:
Accepted 11 July 2014
Available online 23 August 2014
Keywords:
Clustering
Genome elements
Genome complexity
Hierarchical clustering
a b s t r a c t
Early global measures of genome complexity (power spectra, the analysis of fluctuations in DNA walks or
compositional segmentation) uncovered a high degree of complexity in eukaryotic genome sequences.
The main evolutionary mechanisms leading to increases in genome complexity (i.e. gene duplication
and transposon proliferation) can all potentially produce increases in DNA clustering. To quantify such
clustering and provide a genome-wide description of the formed clusters, we developed GenomeCluster,
an algorithm able to detect clusters of whatever genome element identified by chromosome coordinates.
We obtained a detailed description of clusters for ten categories of human genome elements, including
functional (genes, exons, introns), regulatory (CpG islands, TFBSs, enhancers), variant (SNPs) and repeat
(Alus, LINE1) elements, as well as DNase hypersensitivity sites. For each category, we located their clusters
in the human genome, then quantifying cluster length and composition, and estimated the clustering
level as the proportion of clustered genome elements. In average, we found a 27% of elements in clusters,
although a considerable variation occurs among different categories. Genes form the lowest number of
clusters, but these are the longest ones, both in bp and the average number of components, while the
shortest clusters are formed by SNPs. Functional and regulatory elements (genes, CpG islands, TFBSs,
enhancers) show the highest clustering level, as compared to DNase sites, repeats (Alus, LINE1) or SNPs.
Many of the genome elements we analyzed are known to be composed of clusters of low-level entities. In
addition, we found here that the clusters generated by GenomeCluster can be in turn clustered into highlevel
super-clusters. The observation of ‘clusters-within-clusters’ parallels the ‘domains within domains’
phenomenon previously detected through global statistical methods in eukaryotic sequences, and reveals
a complex human genome landscape dominated by hierarchical clustering.
© 2014 Elsevier Ltd. All rights reserved.
1. Introduction
The increase in genome complexity from prokaryotes to eukaryotes
has been mainly driven by a gradual growth in the number of
genes, as well as a more sudden growth in the number of introns
and transposable genetic elements (Lynch and Conery, 2003). Interestingly,
the involved evolutionary mechanisms, such as gene
duplication or the increment in the rate of transposon proliferation,
can all potentially produce DNA clustering. Gene duplication
(Ohno, 1970; Sankoff, 2001) produces at first gene copies clustered
in tandem, while at least some mobile elements might initially
insert nearly at random but can be later differentially eliminated
∗ Corresponding author at: Dpto. de Genética, Facultad de Ciencias, Universidad
de Granada, 18071-Granada, Spain.
E-mail address: oliver@ugr.es (J.L. Oliver).
from some genome regions and accumulated in others (Hackenberg
et al., 2005; Jurka et al., 2004). Therefore, a quantitative description
of the formed clusters, as well as an estimation of the clustering
level shown by distinct categories of genome elements, can increment
our understanding of the evolution of genome complexity at
the sequence level.
The measurement of genome complexity at the sequence level
began as soon as genome sequences of sufficient length were
available. Three independent groups (Li and Kaneko, 1992a; Peng
et al., 1992; Voss, 1992) applied global statistical methods (i.e.
power spectra, analysis of fluctuations in DNA walks) to uncover
large-scale genome structure. The emerging view was a complex,
patchy genome with long-range, power-law correlations, thus
implying that compositional domains should appear at all scales
(Bernaola-Galván et al., 1996; Carpena et al., 2007; Li and Kaneko,
1992a,b; Li et al., 1994). Noteworthy, segmenting these complex,
long-range correlated sequences leads to the observation of the
http://dx.doi.org/10.1016/j.compbiolchem.2014.08.011
1476-9271/© 2014 Elsevier Ltd. All rights reserved.
72 F. Dios et al. / Computational Biology and Chemistry 53 (2014) 71–78
‘domains-within-domains’ phenomenon in eukaryotic, but not in
prokaryotic species (Bernaola-Galván et al., 1996; Li and Kaneko,
1992a; Li et al., 1994; Oliver et al., 2004, 2001; Román-Roldán et al.,
1998). This phenomenon beautifully mimics the hierarchical nature
of biological complexity originated from integrating collections
of objects at one level into entities of the next higher hierarchical
level: exons clustered within the genes, genes integrated into
chromosomes, prokaryotic cells united in an eukaryotic cell, cells
combined in multicellular organisms, etc. (Schuster, 1996). At the
sequence level, this model works by the integration of nucleotides
into sequence motifs (Nussinov et al., 1986; Stormo, 2000) or compositional
domains (Bernaola-Galván et al., 1996; Oliver et al.,
1999), domains forming isochores (Bernardi et al., 1985), isochores
organized into larger chromosome superstructures (Carpena et al.,
2011), and so on.
However, with the exception of DNA repeats (Hackenberg
et al., 2005; Jurka and Kapitonov, 2007; Jurka and Kohany, 2005;
Jurka et al., 2005, 2004; Price et al., 2004; Sellis et al., 2007;
Stankiewicz et al., 2004), the chromosome organization of such
genome complexity remains largely unexplored. We are interested
in characterizing additional, more general based sources of DNA
clustering able to contribute to genome complexity. On the basis
of the CpGcluster algorithm (Hackenberg et al., 2006), we have
developed a new program (GenomeCluster) able to detect clusters
of whatever elements in the genome, provided that chromosome
coordinates are available for them. In this way, using the hg19
assembly of the human genome, we were able to retrieve the clusters
not only of repetitive DNA or transposable elements (as Alus or
LINE1), but also of functional elements (as genes, exons or introns),
regulatory elements (as CpG islands, TFBSs or enhancers), variation
sites (as SNPs) or DNase hypersensitivity sites. We explore here
the genome-wide clustering of all these elements in the human
genome, thus providing a basis to discuss the relation between DNA
clustering and genome complexity.
2. Materials and methods
2.1. Datasets
Chromosome coordinates for most of the human genome elements
analyzed here were obtained from the UCSC Table Browser
(http://genome.ucsc.edu/cgi-bin/hgTables?hgsid=357122457).
The hg19 assembly for the human genome was used throughout.
The refGene table (Pruitt et al., 2005) was used to retrieve the coordinates
of genes, exons and introns. Only the longest transcript of
each gene was considered. The coordinates for TFBSs and DNase
hypersensitivity sites of the ENCODE project (Bernstein et al.,
2012) were retrieved from the tables wgEncodeRegTfbsClusteredV3
and wgEncodeRegDNaseClusteredV2, respectively. Alu and LINE1
coordinates were retrieved from the rmsk table of repeat elements
predicted by RepeatMasker (Jurka et al., 2005). The All snp137
set (Sherry et al., 2001) was used to retrieve SNP coordinates.
CpG island coordinates were those predicted by CpGcluster, using
the genome intersection as the distance threshold and a p-value
cutoff of 1E-5 (Hackenberg et al., 2011). The enhancer dataset
was derived by selecting ENCODE TFBSs tagged with the Gene
Ontology term GO:0003705 (RNA polymerase II distal enhancer
sequence-specific DNA binding transcription factor activity) and
carrying the histone signatures characterizing active enhancers
(H3K4me1 and H3K27ac) (see Zentner et al., 2011 for details).
2.2. The GenomeCluster algorithm
The algorithm has two main steps. First, based on a distance
threshold, the individual genome elements below this threshold are
clustered. Second, by means of the negative binomial distribution,
a p-value is associated to each genome cluster. This p-value can
then be used as a cutoff: low-significant clusters (i.e. above a given
p-value) can be filtered out.
2.2.1. The genome intersection point as the distance threshold
The clustering analysis of genome elements presented here
is inspired by the level statistics of quantum-disordered systems
(Carpena et al., 2009). The spatial distribution of distances (d)
between adjacent occurrences of a particular genome element can
be characterized by the spacing distribution P(d). For systems as the
energy levels of quantum disordered systems, the corresponding
P(d) follows the Poisson distribution. However, the random distribution
P(d) is Poissonian only for continuous distance distributions,
which is valid for the energy levels, but not for the genome elements,
where the distances are integers. The discrete counterpart
of the Poisson distribution is the geometric distribution, which was
used to compute the theoretical (expected) distance distribution of
genome elements.
As shown previously for CpG dinucleotides, the observed and
expected distance distributions show an intersection point separating
intra-cluster from inter-cluster distances (see Fig. 1 in
Hackenberg et al., 2006). It seems reasonable therefore to use this
point as a distance threshold to identify the elements belonging
to each cluster. In most human chromosomes, the intersection lies
near the median of the observed distance distribution between consecutive
CpG dinucleotides. Thus, the default option in the original
CpGcluster program was to use the median as the distance threshold
to identify the clusters. However, we later found notable differences
for other chromosomes, concluding that the median is not a good
estimator for the intersection (Hackenberg et al., 2011). Therefore,
in the WordCluster algorithm we added an option to compute the
genome intersection as the point showing the maximum difference
between observed and expected cumulative density functions
(CDFs) of the distances (see Fig. 1 in Hackenberg et al., 2011 and
Fig. 1 (bottom) in the present paper). The GenomeCluster algorithm
inherits this method, and although the script still allows for the
use of the median (or some other percentile), the recommended
(default) option is taking the genome (or chromosome) intersection
point as the distance threshold.
When the data pertain to just one chromosome, the chromosome
intersection is defined by the intersection between the
Fig. 1. Top: observed and expected distance distributions for the SNPs of hg19. Note
that short distances are overrepresented and the large ones underrepresented as
compared to the expected distances (geometric distribution). The first cross between
both curves separates both regimes. Bottom: the intersection between both curves
(called the genomic intersection) can be precisely computed (71 bp) as the maximum
difference between the observed and expected cumulative density functions
(CDFs).
F. Dios et al. / Computational Biology and Chemistry 53 (2014) 71–78 73
observed and the expected distance distributions in the chromosome.
With genome-wide data, the observed distance distributions
for all the chromosomes are merged together, then calculating the
genome intersection point.
2.2.2. Statistical significance of genome clusters
To associate a p-value to each of the genome clusters (i.e. the
probability of such a cluster appearing by chance in a random
sequence) we used the negative binomial distribution (also known
as Pascal or Pólya distribution) which can be conveniently tailored
to the requirements of genome clusters (Hackenberg et al., 2011).
In general, this distribution can be applied to experiments with
dichotomous outcomes (either success or failure) and gives the
probability of having a certain number of failures when the number
of successes was fixed in advance, taking into account that the
experiment must always end with a success.
By translating these requirements to a genome context, the
successes were equated with the occurrences of the particular
genome element being analyzed, while the failures are equated to
non-genome elements. When the genome intersection is used to
estimate the distance threshold, the success probabilities are not
calculated for each chromosome separately, but a genome wide
success probability (probability to find the element in the entire
genome) is calculated.
2.2.3. Implementation
As mentioned above, to detect clusters of genome elements,
we used a generalization of the algorithms CpGcluster
(Hackenberg et al., 2006) and WordCluster (Hackenberg et al.,
2011) to develop GenomeCluster (the corresponding Perl script
is available at http://bioinfo2.ugr.es/GenomeCluster/software/).
Instead of searching the sequence for CpG dinucleotides (as
in CpGcluster) or k-mer occurrences (as in WordCluster), the
GenomeCluster algorithm uses directly the chromosome coordinates
of a given genome element retrieved from any annotation
table. A requirement is that the table is given in the standard BED
format (http://genome.ucsc.edu/FAQ/FAQformat.html#format1).
Then, the linear physical distances between consecutive genome
elements in the chromosome are directly determined from the
coordinates.
The option ‘start’ was used for all the genome elements analyzed
here. This means that the start of each element is used to measure
the distances (in bp) between consecutive elements in the chromosome.
Therefore, some inter-element distances may need to be
interpreted in a slightly different way than usual. For example, gene
distances are not the typical intergenic distances that are measured
from a gene’s end to the following gene’s start.
The un-sequenced chromosome stretches (i.e. ‘islands of Ns’) are
previously identified for each chromosome by means of a Python
script (N.py, also available at our website); the distances between
genome elements including one or more islands of Ns were discarded
before computing the distance threshold and the statistical
significance for the clusters.
3. Results
We first look for the distance distributions between genome elements.
Fig. 1 (top) shows a first example with the observed and
expected distance distributions for the SNPs of hg19. Note that
short distances are overrepresented, while large ones are underrepresented,
as compared to the expected distances (geometric
distribution). The first crossing point between both curves (the
genomic intersection, as distances for all the chromosomes are
merged together) separates intra- from inter-cluster distances. It
can be accurately computed (71 bp) as the maximum difference
Fig. 2. Top: observed and expected distance distributions for the Alus of hg19. Bottom:
maximum difference between the observed and expected (CDFs) pointing to a
genome intersection of 1140 bp. See the legend of Fig. 1 for further details. The two
peaks at 135 and 309 bp probably correspond to the lengths of the Alu monomer
and of the entire element, respectively.
between observed and expected cumulative density functions
(CDFs, Fig. 1 bottom).
As a second example, we plot distance distributions for human
Alu retroelements (Fig. 2). The genome intersection here is larger
(1140 bp); note that it can be easily distinguished from other lower,
spurious intersection points by using the maximum difference
between observed and expected CDFs (Fig. 2 bottom). The peak
at 309 bp surely corresponds to the length of the entire Alu elements,
while that at 135 bp may correspond to the length of the
Alu monomer, as these repeats are often found fragmented in the
genome (Hackenberg et al., 2005; Jurka et al., 2005).
We then used the script GenomeCluster with default parameters
(i.e. genome intersection as distance threshold, p-value ≤ 1E−5,
clustering method = ‘start’) to search for clusters of genome elements
in the 24 chromosomes (22 autosomes + X + Y) of the hg19
human genome assembly. We analyzed ten categories of elements
(listed in Table 1), including gene-based annotations (as genes,
exons or introns), regulatory elements (as CpG islands, TFBSs,
enhancers), repeats elements (as Alus or LINE1), variation sites
(SNPs) and DNase hypersensitivity sites.
In total, we analyzed more than 64 million elements and found
that 27% of them are organized into 424,113 genome clusters. However,
a considerable variation occurs between different element
categories (see Table 1). A good indicator of clustering level is the
percentage of clustered elements (displayed between parentheses
in column 4 of Table 1). The highest clustering levels were found
for TFBSs and enhancers. The genes and CpG islands show moderate,
and strikingly similar, clustering percentages, while exons and
introns double the clustering of the genes. The lower percentages
of clustered elements were found for repeats (Alus and, above all,
LINE1), DNase sites and SNPs. Genes, DNase sites or Alus form he
clusters with more elements (Table 1, column 6), while the clusters
with a minor number of elements were those for CpG islands or
enhancers.
Cluster counts (Table 1, column 3) show that the highest absolute
numbers of clusters are formed by SNPs and TFBSs, although
their cluster lengths (Table 1, column 5) are the shortest ones. Genes
form the lowest number of clusters, but they are the longest ones,
both in bp and average number of components (Table 1, column
6). This seems to suggest a relation between cluster and genomeelement
sizes. However, the clustering level does not follow this
rule (e.g. the lowest clustering level was obtained for the SNPs and
the largest one for TFBSs, which are only of moderate size). Another
74 F. Dios et al. / Computational Biology and Chemistry 53 (2014) 71–78
Table 1
Clusters of genome elements pertaining to ten different categories in the human genome (hg19).
Genome entity Number of elements Number of
clusters
Elements forming
clusters (%)
Mean cluster
length (bp) ± SD
Mean number of elements
by cluster ± SD
Genes 19,152 206 4408 (23%) 441,645 ± 294,208 21 ± 13
Exons 198,933 5089 78,425 (39%) 16,035 ± 10,234 15 ± 11
Introns 179,781 5178 73,506 (41%) 14,692 ± 9778 14 ± 10
CpG islands 204,834 5563 44,408 (22%) 3384 ± 3143 8 ± 6
TFBSs 4,380,444 160,519 2,707,380 (62%) 230 ± 158 17 ± 15
Enhancers 318,454 25,944 176,925 (56%) 649 ± 672 7 ± 3
DNase sites 1,281,988 5838 121,274 (9%) 10,214 ± 3914 21 ± 7
Alus 1,175,329 8020 158,121 (13%) 8140 ± 4593 20 ± 10
LINE1 1,480,420 898 13,860 (1%) 5226 ± 2522 15 ± 6
SNPs 55,448,579 206,858 2,146,020 (4%) 25 ± 84 10 ± 61
interesting observation was that several gene clusters in different
chromosomes are over one megabase in length; however, there are
chromosomes (as 13, 18 or Y) where the genes do not form clusters.
Noteworthy, these three chromosomes are known to have the
lowest numbers of both genes and GC-rich isochores (Pavlicek et al.,
2002).
3.1. Genome-wide maps of genome clusters
Genome-wide maps of the obtained genome clusters can be
browsed by means of a track hub – i.e., a public web-accessible
directory of genomic data that can be viewed and formatted
through the UCSC Genome Browser as they were native tracks. Fig. 3
shows an example map obtained with this facility. Once the data
are at the UCSC site, the tables of chromosome coordinates for the
different cluster sets can be downloaded by the user simply switching
to the Table Browser, choosing the group ‘Genome Cluster’ and
setting the appropriate options to format the output.
3.2. ‘Circos’ maps
Positional information of genome clusters can be also viewed
as circular maps for each chromosome generated by means
of the software-package Circos (Krzywinski et al., 2009). Fig. 4
shows an example for chromosome 19 and images for the
remaining chromosomes are available at http://bioinfo2.ugr.es/
GenomeCluster/circos-maps/. Intra and inter-chromosome heterogeneity
in the number, length and density of clusters for different
element categories can be easily compared with the help of these
images.
4. Discussion
Genes (Ben-Elazar et al., 2013; Durand and Sankoff, 2003;
Firneisz et al., 2003; Kendal, 2004; Lercher et al., 2002; Li et al.,
2005; Neel, 1961; Thomas, 2002; Wright et al., 2007), CpG
dinucleotides (Bird, 1986; Hackenberg et al., 2006), Alu retrotransposons
(Hackenberg et al., 2005; Jurka and Kohany, 2005; Jurka
et al., 2004, 2002; Pavlícek et al., 2001; Sellis et al., 2007), TFBSs
(Berman et al., 2002; Boeva et al., 2007; Murakami et al., 2004), 3D
structural motifs in ribosomal RNA (Sargsyan and Lim, 2010), SNPs
(Amos, 2010), somatic mutations in cancer (Nik-Zainal et al., 2012),
and many DNA k-mers (Hackenberg et al., 2012) are all known to
occur in clusters. The analysis we carried out here with the help of
the GenomeCluster algorithm confirms and generalizes these observations.
In addition, we were able to detect the clustering level,
the chromosome coordinates, the length and the composition of
the clusters for a wide variety of functional, regulatory or repeat
elements, as well as for variation sites.
4.1. Clusters of functional elements
Functional and regulatory elements (genes, CpG islands, TFBSs,
enhancers) show a high proportion of clustered elements (40.8%
in average, see Table 1), in agreement with recent enrichment/depletion
experiments showing that highly clustered words
(DNA k-mers) are significantly enriched in the functional part of
the genome (Hackenberg et al., 2012). Both exons and introns also
show high clustering levels (39% and 41%, respectively); the mean
numbers of these elements by cluster (15 ± 11 and 14 ± 10 in averages,
respectively) agree with the fact that each intron is flanked
Fig. 3. Clusters of genome elements pertaining to ten different categories in a region of 936,982 bp of human chromosome 19. The image was obtained using the UCSC track
facility.
F. Dios et al. / Computational Biology and Chemistry 53 (2014) 71–78 75
Fig. 4. Clusters of genome elements pertaining to different categories in the human chromosome 19 represented as a series of concentric circles by means of the program
Circos (Krzywinski et al., 2009). Histograms of densities and tiles of genome clusters are shown for each genome category. Five representative categories are drawn from
outside to inside: TFBSs, enhancers, Alus, CpG islands and genes.
by exons at both sides. The small difference between these two
proportions may be due to single-exon genes. Some of the introns in
the human genome are hundred thousand bp in length (Sakharkar
et al., 2004), and therefore some genes could contain two or more
exon clusters. In fact, we found 813 (or 4.2%) of such genes in hg19
(see an example in Fig. 5).
Since TFBSs are often located within gene bodies (Wittkopp and
Kalay, 2011), one would expect similar levels of clustering for these
two types of elements. However, Table 1 shows that TFBSs show
a far higher proportion (62%) of clustered elements than genes
(23%). The fact that a plethora of transcription factors binds within
protein-coding regions, in addition to nearby noncoding regions
Fig. 5. The 183 exons of the gene NEB (chr2: 152,341,853–152,591,001) are grouped by our algorithm into 8 identifiable clusters. This gene encodes nebulin, a giant modular
protein component of the cytoskeletal matrix within the sarcomeres of skeletal muscle. The encoded protein contains approximately 30-amino acid long modules that can
be classified into 7 types and other repeated modules. Of the 183 exons in the nebulin gene, at least 43 are alternatively spliced. The figure also shows four of the several
thousand transcript variants predicted for nebulin by the RefSeq Project.
76 F. Dios et al. / Computational Biology and Chemistry 53 (2014) 71–78
Fig. 6. Hierarchical clustering for CpG dinucleotides in a region of 1625 bp of human chromosome 22 (24,712,761–24,714,385). The dinucleotides are clustered within CpG
islands, which in turn are clustered within larger CpG-island clusters. Both CpG islands and genome clusters for CpG islands are statistically significant (p-value ≤ 0.00001).
(Stergachis et al., 2013; Weatheritt and Babu, 2013) may explain
these results.
4.2. Repeat and variant clusters
Given its higher density in GC-rich regions (Lander et al., 2001;
Pavlícek et al., 2001), Alu retrotransposons are thought to be one
of the most clustered elements in the genome (Hackenberg et al.,
2005; Jurka and Kohany, 2005; Jurka et al., 2004, 2002; Pavlícek
et al., 2001; Sellis et al., 2007). However, we found that, in comparison
with other genome elements (Table 1), Alus show only a
moderate number of clusters. In addition, the percentage of clustered
elements is low (only the 13% of Alus are forming clusters),
although the mean number of elements by cluster is one of the
higher ones (20 ± 10 in average).
A low clustering level (4%) was found for variation sites (table
All SNPs137 at UCSC). When common (table Common SNPs137)
and Not-common (the difference between All and Common tables)
variants were separately analyzed, 2% and 5% of clustered elements
were found, respectively. Clinically associated SNPs (tables
SNPFlagged and ClinVar of the UCSC Table Browser) show far higher
clustering levels (74% and 79%, respectively). However, these data
should be taken with caution, since sample sizes of flagged SNPs
are much smaller. An additional caution is that clinically associated
SNPs co-localize with gene bodies, and therefore the clustering of
genes, exons and introns are probably conditioning the clustering
levels we found for this class of SNPs.
The non-random distribution of SNPs in the human genome has
been explained by mutational non-independence (Amos, 2010),
and also by the action of natural selection, with purifying selection
eliminating SNPs from functional regions and balancing selection
promoting the clustering of SNPs (Varela and Amos, 2010; Zhao
et al., 2003).
4.3. Clusters within clusters
It is well known that clusters of low-level entities in fact compose
many of the genome elements analyzed here. For example,
CpG islands are due to the clustering of CpG dinucleotides, TFBSs
were obtained by clustering peaks of transcription factor occupancy,
DNase sites are clusters of peaks of DNase hypersensitivity,
and so on. We investigated therefore if the clusters obtained with
GenomeCluster could be in turn grouped forming structures of a
higher rank. We found that all the genome elements analyzed in
this work (Table 1) were able to form ‘super-clusters’ when they
were taken as units to feed again the GenomeCluster script. Further
work is needed, however, to properly determine the statistical and
biological significance of these higher-order superstructures.
The emerging view was, therefore, a genome landscape
dominated by hierarchical clustering, thus supporting
previous observations by global statistical methods of
‘domains-within-domains’ in eukaryotic genomes (BernaolaGalván
et al., 1996; Li and Kaneko, 1992a; Li et al., 1994; Oliver
et al., 2004, 2001; Román-Roldán et al., 1998). Examples of hierarchical
clustering are shown in Fig. 5 (the exons form clusters
within a gene coding for a modular protein) and 6 (the dinucleotide
CpG is clustered within CpG islands, which are in turn clustered
within larger clusters of CpG-islands). To date, the ‘domainswithin-domains’
phenomenon has been uniquely observed in the
complex, long-range correlated sequences of eukaryotic genomes,
but not in bacterial genomes (see Fig. 3 in Bernaola-Galván et al.,
1996); thus, it seems to be related only to complex genomes.
4.4. Perspectives
The present approach to describe the organization and evaluate
the role of genome clusters in genome complexity has the limitation
that we only searched for clusters of elements belonging to a
same category, i.e. only homoclusters were detected. Most probably,
as envisaged by the circos maps (Fig. 4), the situation in the
genome is more complex, with homoclusters of a same element
type intermixed or co-clustered with homoclusters of other element
types, a process that would lead to heteroclusters of disparate
genome elements. The next step therefore would be to develop
computational tools able to reliably detect such heteroclusters, and
investigate how these are organized, leading to the hugely complex
genome structure anticipated by global statistical measures of
genome complexity (Fig. 6).
Another, more general, limitation of our study is that it only consider
genome clustering along the one-dimensional chromosome
sequence, thus ignoring the spatial clustering that may result from
the 3D organization of the chromosomes within the nucleus, which
can put together genome elements actually far in the chromosome
or even located on different chromosomes (Trieu and Cheng, 2014).
The 3D vicinity of genetic elements is surely most relevant to gene
function (Pennisi, 2011), but unfortunately the sequence data we
used here do not allow to address this interesting problem. The
chromosomal contact data generated by Hi-C chromosome conformation
capturing techniques (Lieberman-Aiden et al., 2009) should
allow to approach this problem in the near future.
Web supplementary data
Supplementary data associated with this article can be found, in
the online version, at http://bioinfo2.ugr.es/GenomeCluster/.
Acknowledgements
We are most grateful to Wentian Li by testing the software and
solve some bugs. Helpful comments from two reviewers are also
greatly acknowledged. This work was carried out by using the facilities
of the Computational Genomics and Bioinformatics Group,
F. Dios et al. / Computational Biology and Chemistry 53 (2014) 71–78 77
Dept. of Genetics, Inst. of Biotechnology, University of Granada
(Spain).
References
Amos, W., 2010. Even small SNP clusters are non-randomly distributed: is this evidence
of mutational non-independence? Proc. R. Soc. B 277, 1443–1449.
Ben-Elazar, S., Yakhini, Z., Yanai, I., 2013. Spatial localization of co-regulated genes
exceeds genomic gene clustering in the Saccharomyces cerevisiae genome.
Nucleic Acids Res. 41, 2191–2201.
Berman, B.P., Nibu, Y., Pfeiffer, B.D., Tomancak, P., Celniker, S.E., Levine, M., Rubin,
G.M., Eisen, M.B., 2002. Exploiting transcription factor binding site clustering to
identify cis-regulatory modules involved in pattern formation in the Drosophila
genome. Proc. Natl. Acad. Sci. U.S.A. 99, 757–762.
Bernaola-Galván, P., Román-Roldán, R., Oliver, J., 1996. Compositional segmentation
and long-range fractal correlations in DNA sequences. Phys. Rev. E: Stat. Phys.
Plasmas Fluids Relat. Interdiscip. Top. 53, 5181–5189.
Bernardi, G., Olofsson, B., Filipski, J., Zerial, M., Salinas, J., Cuny, G., Meunier-Rotival,
M., Rodier, F., 1985. The mosaic genome of warm-blooded vertebrates. Science
228, 953–958.
Bernstein, B.E., Birney, E., Dunham, I., Green, E.D., Gunter, C., Snyder, M., Kundaje,
A., Aldred, S.F., Collins, P.J., Davis, C.A., Doyle, F., Epstein, C.B., Frietze, S., Harrow,
J., Kaul, R., Khatun, J., Lajoie, B.R., Landt, S.G., Lee, B.-K., Pauli, F., Rosenbloom,
K.R., Sabo, P., Safi, A., Sanyal, A., Shoresh, N., Simon, J.M., Song, L., Trinklein,
N.D., Altshuler, R.C., Brown, J.B., Cheng, C., Djebali, S., Dong, X., Ernst, J., Furey,
T.S., Gerstein, M., Giardine, B., Greven, M., Hardison, R.C., Harris, R.S., Herrero,
J., Hoffman, M.M., Iyer, S., Kelllis, M., Kheradpour, P., Lassman, T., Li, Q., Lin, X.,
Marinov, G.K., Merkel, A., Mortazavi, A., Parker, S.C.J.S.L., Reddy, T.E., Rozowsky,
J., Schlesinger, F., Thurman, R.E., Wang, J., Ward, L.D., Whitfield, T.W., Wilder,
S.P., Wu, W., Xi, H.S., Yip, K.Y., Zhuang, J., Pazin, M.J., Lowdon, R.F., Dillon, L.A.L.,
Adams, L.B., Kelly, C.J., Zhang, J., Wexler, J.R., Good, P.J., Feingold, E.A., Crawford,
G.E., Dekker, J., Elinitski, L., Farnham, P.J., Giddings, M.C., Gingeras, T.R., Guigó, R.,
Hubbard, T.J., Kellis, M., Kent, W.J., Lieb, J.D., Margulies, E.H., Myers, R.M., Starnatoyannopoulos,
J.A., Tennebaum, S.A., Weng, Z., White, K.P., Wold, B., Yu, Y.,
Wrobel, J., Risk, B.A., Gunawardena, H.P., Kuiper, H.C., Maier, C.W., Xie, L., Chen,
X., Mikkelsen, T.S., Gillespie, S., Goren, A., Ram, O., Zhang, X., Wang, L., Issner,
R., Coyne, M.J., Durham, T., Ku, M., Truong, T., Eaton, M.L., Dobin, A., Lassmann,
T., Tanzer, A., Lagarde, J., Lin, W., Xue, C., Williams, B.A., Zaleski, C., Röder, M.,
Kokocinski, F., Abdelhamid, R.F., Alioto, T., Antoshechkin, I., Baer, M.T., Batut,
P., Bell, I., Bell, K., Chakrabortty, S., Chrast, J., Curado, J., Derrien, T., Drenkow,
J., Dumais, E., Dumais, J., Duttagupta, R., Fastuca, M., Fejes-Toth, K., Ferreira, P.,
Foissac, S., Fullwood, M.J., Gao, H., Gonzalez, D., Gordon, A., Howald, C., Jha, S.,
Johnson, R., Kapranov, P., King, B., Kingswood, C., Li, G., Luo, O.J., Park, E., Preall,
J.B., Presaud, K., Ribeca, P., Robyr, D., Ruan, X., Sammeth, M., Sandu, K.S., Schaeffer,
L., See, L.-H., Shahab, A., Skancke, J., Suzuki, A.M., Takahashi, H., Tilgner, H.,
Trout, D., Walters, N., Wang, H.H., Hayashizaki, Y., Reymond, A., Antonarakis, S.E.,
Hannon, G.J., Ruan, Y., Carninci, P., Sloan, C.A., Learned, K., Malladi, V.S., Wong,
M.C., Barber, G.P., Cline, M.S., Dreszer, T.R., Heitner, S.G., Karolchik, D., Kirkup,
V.M., Meyer, L.R., Long, J.C., Maddren, M., Raney, B.J., Grasfeder, L.L., Giresi, P.G.,
Battenhouse, A., Sheffield, N.C., Showers, K.A., London, D., Bhinge, A.A., Shestak,
C., Schaner, M.R., Kim, S.K., Zhang, Z.Z.Z., Mieczkowski, P.A., Mieczkowska,
J.O., Liu, Z., McDaniell, R.M., Ni, Y., Rashid, N.U., Kim, M.J., Adar, S., Wang, T.,
Winter, D., Keefe, D., Iyer, V.R., Sandhu, K.S., Zheng, M., Wang, P., Gertz, J., Vielmetter,
J., Partridge, E.C., Varley, K.E., Gasper, C., Bansal, A., Pepke, S., Jain, P.,
Amrhein, H., Bowling, K.M., Anaya, M., Cross, M.K., Muratet, M.A., Newberry,
K.M., McCue, K., Nesmith, A.S., Fisher-Aylor, K.I., Pusey, B., DeSalvo, G., Balasubramanian,
S.S., Davis, N.S., Meadows, S.K., Eggleston, T., Newberry, J.S., Levy,
S.E., Absher, D.M., Wong, W.H., Blow, M.J., Visel, A., Pennachio, L.A., Elnitski, L.,
Petrykowska, H.M., Abyzov, A., Aken, B., Barrell, D., Barson, G., Berry, A., Bignell,
A., Boychenko, V., Bussotti, G., Davidson, C., Despacio-Reyes, G., Diekhans, M.,
Ezkurdia, I., Frankish, A., Gilbert, J., Gonzalez, J.M., Griffiths, E., Harte, R., Hendrix,
D.A., Hunt, T., Jungreis, I., Kay, M., Khurana, E., Leng, J., Lin, M.F., Loveland, J.,
Lu, Z., Manthravadi, D., Mariotti, M., Mudge, J., Mukherjee, G., Notredame, C., Pei,
B., Rodriguez, J.M., Saunders, G., Sboner, A., Searle, S., Sisu, C., Snow, C., Steward,
C., Tapanari, E., Tress, M.L., van Baren, M.J., Washieti, S., Wilming, L., Zadissa,
A., Zhengdong, Z., Brent, M., Haussler, D., Valencia, A., Raymond, A., Addleman,
N., Alexander, R.P., Auerbach, R.K., Bettinger, K., Bhardwaj, N., Boyle, A.P., Cao,
A.R., Cayting, P., Charos, A., Cheng, Y., Eastman, C., Euskirchen, G., Fleming, J.D.,
Grubert, F., Habegger, L., Hariharan, M., Harmanci, A., Iyenger, S., Jin, V.X., Karczewski,
K.J., Kasowski, M., Lacroute, P., Lam, H., Larnarre-Vincent, N., Lian, J.,
Lindahl-Allen, M., Min, R., Miotto, B., Monahan, H., Moqtaderi, Z., Mu, X.J., O’Geen,
H., Ouyang, Z., Patacsil, D., Raha, D., Ramirez, L., Reed, B., Shi, M., Slifer, T., Witt,
H., Wu, L., Xu, X., Yan, K.-K., Yang, X., Struhl, K., Weissman, S.M., Tenebaum, S.A.,
Penalva, L.O., Karmakar, S., Bhanvadia, R.R., Choudhury, A., Domanus, M., Ma, L.,
Moran, J., Victorsen, A., Auer, T., Centarin, L., Eichenlaub, M., Gruhl, F., Heerman,
S., Hoeckendorf, B., Inoue, D., Kellner, T., Kirchmaier, S., Mueller, C., Reinhardt,
R., Schertel, L., Schneider, S., Sinn, R., Wittbrodt, B., Wittbrodt, J., Jain, G., Balasundaram,
G., Bates, D.L., Byron, R., Canfield, T.K., Diegel, M.J., Dunn, D., Ebersol,
A.K., Frum, T., Garg, K., Gist, E., Hansen, R.S., Boatman, L., Haugen, E., Humbert, R.,
Johnson, A.K., Johnson, E.M., Kutyavin, T.M., Lee, K., Lotakis, D., Maurano, M.T.,
Neph, S.J., Neri, F.V., Nguyen, E.D., Qu, H., Reynolds, A.P., Roach, V., Rynes, E.,
Sanchez, M.E., Sandstrom, R.S., Shafer, A.O., Stergachis, A.B., Thomas, S., Vernot,
B., Vierstra, J., Vong, S., Weaver, M.A., Yan, Y., Zhang, M., Akey, J.A., Bender, M.,
Dorschner, M.O., Groudine, M., MacCoss, M.J., Navas, P., Stamatoyannopoulos, G.,
Stamatoyannopoulos, J.A., Beal, K., Brazma, A., Flicek, P., Johnson, N., Lukk, M.,
Luscombe, N.M., Sobral, D., Vaquerizas, J.M., Batzoglou, S., Sidow, A., Hussami,
N., Kyriazopoulou-Panagiotopoulou, S., Libbrecht, M.W., Schaub, M.A., Miller,
W., Bickel, P.J., Banfai, B., Boley, N.P., Huang, H., Li, J.J., Noble, W.S., Bilmes, J.A.,
Buske, O.J., Sahu, A.O., Kharchenko, P.V., Park, P.J., Baker, D., Taylor, J., Lochovsky,
L., 2012. An integrated encyclopedia of DNA elements in the human genome.
Nature 489, 57–74.
Bird, A., 1986. CpG-rich islands and the function of DNA methylation. Nature 321,
209–213.
Boeva, V., Clément, J., Régnier, M., Roytberg, M.A., Makeev, V.J., 2007. Exact p-value
calculation for heterotypic clusters of regulatory motifs and its application in
computational annotation of cis-regulatory modules. Algorithms Mol. Biol. 2,
13.
Carpena, P., Bernaola-Galván, P., Coronado, A., Hackenberg, M., Oliver, J., 2007. Identifying
characteristic scales in the human genome. Phys. Rev. E 75, 032903.
Carpena, P., Bernaola-Galván, P., Hackenberg, M., Coronado, A., Oliver, J., 2009. Level
statistics of words: finding keywords in literary texts and symbolic sequences.
Phys. Rev. E 79, 035102.
Carpena, P., Oliver, J.L., Hackenberg, M., Coronado, A.V., Barturen, G., BernaolaGalván,
P., 2011. High-level organization of isochores into gigantic superstructures
in the human genome. Phys. Rev. E 83, 031908.
Durand, D., Sankoff, D., 2003. Tests for gene clustering. J. Comput. Biol. 10, 453–482.
Firneisz, G., Zehavi, I., Vermes, C., Hanyecz, A., Frieman, J.A., Glant, T.T., 2003. Identification
and quantification of disease-related gene clusters. Bioinformatics
(Oxford, Engl.) 19, 1781–1786.
Hackenberg, M., Bernaola-Galván, P., Carpena, P., Oliver, J., 2005. The biased distribution
of Alus in human isochores might be driven by recombination. J. Mol.
Evol. 60, 365–377.
Hackenberg, M., Carpena, P., Bernaola-Galván, P., Barturen, G., Alganza, A.M., Oliver,
J.L., 2011. WordCluster: detecting clusters of DNA words and genomic elements.
Algorithms Mol. Biol. 6, 2.
Hackenberg, M., Previti, C., Luque-Escamilla, P., Carpena, P., Martínez-Aroza, J.,
Oliver, J., 2006. CpGcluster: a distance-based algorithm for CpG-island detection.
BMC Bioinform. 7, 446.
Hackenberg, M., Rueda, A., Carpena, P., Bernaola-Galván, P., Barturen, G., Oliver, J.L.,
2012. Clustering of DNA words and biological function: a proof of principle. J.
Theor. Biol. 297, 127–136.
Jurka, J., Kapitonov, V.V., Pavlicek, A., Klonowski, P., Kohany, O., Walichiewicz, J.,
2005. Repbase update, a database of eukaryotic repetitive elements. Cytogenet.
Genome Res. 110, 462–467.
Jurka, J., Kapitonov, V., 2007. Repetitive sequences in complex genomes: structure
and evolution. Annu. Rev. Genomics Hum. Genet. 8, 241–259.
Jurka, J., Kohany, O., 2005. Clustering, duplication and chromosomal distribution of
mouse SINE retrotransposons. Cytogenet. Genome Res. 110, 117–123.
Jurka, J., Kohany, O., Pavlicek, A., Kapitonov, V.V., Jurka, M.V., 2004. Duplication,
coclustering, and selection of human Alu retrotransposons. Proc. Natl. Acad. Sci.
U.S.A. 101, 1268–1272.
Jurka, J., Krnjajic, M., Kapitonov, V.V., Stenger, J.E., Kokhanyy, O., 2002. Active Alu
elements are passed primarily through paternal germlines. Theor. Popul. Biol.
61, 519–530.
Kendal, W., 2004. A scale invariant clustering of genes on human chromosome 7.
BMC Evol. Biol., http://dx.doi.org/10.1186/1471-2148-4-3.
Krzywinski, M., Schein, J., Birol, I., Connors, J., Gascoyne, R., Horsman, D., Jones, S.J.,
Marra, M.A., 2009. Circos: an information aesthetic for comparative genomics.
Genome Res. 19, 1639–1645.
Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J., Devon, K.,
Dewar, K., Doyle, M., FitzHugh, W., Funke, R., Gage, D., Harris, K., Heaford, A.,
Howland, J., Kann, L., Lehoczky, J., LeVine, R., McEwan, P., McKernan, K., Meldrim,
J., Mesirov, J.P., Miranda, C., Morris, W., Naylor, J., Raymond, C., Rosetti, M., Santos,
R., Sheridan, A., Sougnez, C., Stange-Thomann, N., Stojanovic, N., Subramanian,
A., Wyman, D., Rogers, J., Sulston, J., Ainscough, R., Beck, S., Bentley, D., Burton,
J., Clee, C., Carter, N., Coulson, A., Deadman, R., Deloukas, P., Dunham, A., Dunham,
I., Durbin, R., French, L., Grafham, D., Gregory, S., Hubbard, T., Humphray,
S., Hunt, A., Jones, M., Lloyd, C., McMurray, A., Matthews, L., Mercer, S., Milne,
S., Mullikin, J.C., Mungall, A., Plumb, R., Ross, M., Shownkeen, R., Sims, S., Waterston,
R.H., Wilson, R.K., Hillier, L.W., McPherson, J.D., Marra, M.A., Mardis, E.R.,
Fulton, L.A., Chinwalla, A.T., Pepin, K.H., Gish, W.R., Chissoe, S.L., Wendl, M.C.,
Delehaunty, K.D., Miner, T.L., Delehaunty, A., Kramer, J.B., Cook, L.L., Fulton,
R.S., Johnson, D.L., Minx, P.J., Clifton, S.W., Hawkins, T., Branscomb, E., Predki,
P., Richardson, P., Wenning, S., Slezak, T., Doggett, N., Cheng, J.F., Olsen, A., Lucas,
S., Elkin, C., Uberbacher, E., Frazier, M., Gibbs, R.A., Muzny, D.M., Scherer, S.E.,
Bouck, J.B., Sodergren, E.J., Worley, K.C., Rives, C.M., Gorrell, J.H., Metzker, M.L.,
Naylor, S.L., Kucherlapati, R.S., Nelson, D.L., Weinstock, G.M., Sakaki, Y., Fujiyama,
A., Hattori, M., Yada, T., Toyoda, A., Itoh, T., Kawagoe, C., Watanabe, H., Totoki,
Y., Taylor, T., Weissenbach, J., Heilig, R., Saurin, W., Artiguenave, F., Brottier, P.,
Bruls, T., Pelletier, E., Robert, C., Wincker, P., Smith, D.R., Doucette-Stamm, L.,
Rubenfield, M., Weinstock, K., Lee, H.M., Dubois, J., Rosenthal, A., Platzer, M.,
Nyakatura, G., Taudien, S., Rump, A., Yang, H., Yu, J., Wang, J., Huang, G., Gu, J.,
Hood, L., Rowen, L., Madan, A., Qin, S., Davis, R.W., Federspiel, N.A., Abola, A.P.,
Proctor, M.J., Myers, R.M., Schmutz, J., Dickson, M., Grimwood, J., Cox, D.R., Olson,
M.V., Kaul, R., Shimizu, N., Kawasaki, K., Minoshima, S., Evans, G.A., Athanasiou,
M., Schultz, R., Roe, B.A., Chen, F., Pan, H., Ramser, J., Lehrach, H., Reinhardt, R.,
McCombie, W.R., de la Bastide, M., Dedhia, N., Blocker, H., Hornischer, K., Nordsiek,
G., Agarwala, R., Aravind, L., Bailey, J.A., Bateman, A., Batzoglou, S., Birney,
E., Bork, P., Brown, D.G., Burge, C.B., Cerutti, L., Chen, H.C., Church, D., Clamp, M.,
Copley, R.R., Doerks, T., Eddy, S.R., Eichler, E.E., Furey, T.S., Galagan, J., Gilbert,
J.G., Harmon, C., Hayashizaki, Y., Haussler, D., Hermjakob, H., Hokamp, K., Jang,
78 F. Dios et al. / Computational Biology and Chemistry 53 (2014) 71–78
W., Johnson, L.S., Jones, T.A., Kasif, S., Kaspryzk, A., Kennedy, S., Kent, W.J., Kitts,
P., Koonin, E.V., Korf, I., Kulp, D., Lancet, D., Lowe, T.M., McLysaght, A., Mikkelsen,
T., Moran, J.V., Mulder, N., Pollara, V.J., Ponting, C.P., Schuler, G., Schultz, J., Slater,
G., Smit, A.F., Stupka, E., Szustakowski, J., Thierry-Mieg, D., Thierry-Mieg, J., Wagner,
L., Wallis, J., Wheeler, R., Williams, A., Wolf, Y.I., Wolfe, K.H., Yang, S.P., Yeh,
R.F., Collins, F., Guyer, M.S., Peterson, J., Felsenfeld, A., Wetterstrand, K.A., Patrinos,
A., Morgan, M.J., de Jong, P., Catanese, J.J., Osoegawa, K., Shizuya, H., Choi, S.,
Chen, Y.J., 2001. Initial sequencing and analysis of the human genome. Nature
409, 860–921.
Lercher, M.J., Urrutia, A.O., Hurst, L.D., 2002. Clustering of housekeeping genes provides
a unified model of gene order in the human genome. Nat. Genet. 31,
180–183.
Li, Q., Lee, B.T.K., Zhang, L., 2005. Genome-scale analysis of positional clustering of
mouse testis-specific genes. BMC Genomics 6, 7.
Li, W., Kaneko, K., 1992a. Long-range correlations and partial 1/f spectrum in a
noncoding DNA sequence. Europhys. Lett. 17, 555–660.
Li, W., Kaneko, K., 1992b. DNA correlations. Nature 360, 635–636.
Li, W., Marr, T., Kaneko, K., 1994. Understanding long-range correlations in DNA
sequences. Phys. D: Nonlinear Phenom. Vol. 75, 392–416.
Lieberman-Aiden, E., van Berkum, N.L., Williams, L., Imakaev, M., Ragoczy, T., Telling,
A., Amit, I., Lajoie, B.R., Sabo, P.J., Dorschner, M.O., Sandstrom, R., Bernstein,
B., Bender, M.A., Groudine, M., Gnirke, A., Stamatoyannopoulos, J., Mirny, L.A.,
Lander, E.S., Dekker, J., 2009. Comprehensive mapping of long-range interactions
reveals folding principles of the human genome. Science (New York, N.Y.)
326, 289–293.
Lynch, M., Conery, J.S., 2003. The origins of genome complexity. Science (New York,
N.Y.) 302, 1401–1404.
Murakami, K., Kojima, T., Sakaki, Y., 2004. Assessment of clusters of transcription
factor binding sites in relationship to human promoter, CpG islands and gene
expression. BMC Genomics 5, 16.
Neel, J.V., 1961. The hemoglobin genes: a remarkable example of the clustering
of related genetic functions on a single mammalian chromosome. Blood 18,
769–777.
Nik-Zainal, S., Alexandrov, L.B., Wedge, D.C., Van Loo, P., Greenman, C.D., Raine, K.,
Jones, D., Hinton, J., Marshall, J., Stebbings, L.A., Menzies, A., Martin, S., Leung, K.,
Chen, L., Leroy, C., Ramakrishna, M., Rance, R., Lau, K.W., Mudie, L.J., Varela, I.,
McBride, D.J., Bignell, G.R., Cooke, S.L., Shlien, A., Gamble, J., Whitmore, I., Maddison,
M., Tarpey, P.S., Davies, H.R., Papaemmanuil, E., Stephens, P.J., McLaren,
S., Butler, A.P., Teague, J.W., Jönsson, G., Garber, J.E., Silver, D., Miron, P., Fatima,
A., Boyault, S., Langerød, A., Tutt, A., Martens, J.W.M., Aparicio, S.A.J.R., Borg,
Å., Salomon, A.V., Thomas, G., Børresen-Dale, A.-L., Richardson, A.L., Neuberger,
M.S., Futreal, P.A., Campbell, P.J., Stratton, M.R., 2012. Mutational processes
molding the genomes of 21 breast cancers. Cell 149, 979–993.
Nussinov, R., Owens, J., Maizel, J.V., 1986. Sequence signals in eukaryotic upstream
regions. Biochim. Biophys. Acta 866, 109–119.
Ohno, S., 1970. Evolution by Gene Duplication. Springer-Verlag.
Oliver, J., Román-Roldán, R., Pérez, J.B.-G.P., 1999. SEGMENT: identifying compositional
domains in DNA sequences. Bioinformatics 15, 974–979.
Oliver, J.L., Bernaola-Galván, P., Carpena, P., Román-Roldán, R., 2001. Isochore chromosome
maps of eukaryotic genomes. Gene 276, 47–56.
Oliver, J.L., Carpena, P., Hackenberg, M., Bernaola-Galván, P., 2004. IsoFinder: computational
prediction of isochores in genome sequences. Nucleic Acids Res. 32,
W287–W292.
Pavlícek, A., Jabbari, K., Paces, J., Paces, V., Hejnar, J.V., Bernardi, G., 2001. Similar
integration but different stability of Alus and LINEs in the human genome. Gene
276, 39–45.
Pavlicek, A., Paces, J., Clay, O., Bernardi, G., 2002. A compact view of isochores in the
draft human genome sequence. FEBS Lett. 511, 165–169.
Peng, C.K., Buldyrev, S.V., Goldberger, A.L., Havlin, S., Sciortino, F., Simons, M., Stanley,
H.E., 1992. Long-range correlations in nucleotide sequences. Nature 356,
168–170.
Pennisi, E., 2011. Mysteries of the cell. Does a gene’s location in the nucleus matter?
Science (New York, N.Y.) 334, 1050–1051.
Price, A.L., Eskin, E., Pevzner, P.A., 2004. Whole-genome analysis of Alu repeat elements
reveals complex evolutionary history. Genome Res. 14, 2245–2252.
Pruitt, K.D., Tatusova, T., Maglott, D.R., 2005. NCBI Reference Sequence (RefSeq): a
curated non-redundant sequence database of genomes, transcripts and proteins.
Nucleic Acids Res. 33, D501–D504.
Román-Roldán, R., Bernaola-Galván, P., Oliver, J., 1998. Sequence compositional
complexity of DNA through an entropic segmentation method. Phys. Rev. Lett.
80, 1344–1347.
Sakharkar, M.K., Chow, V.T.K., Kangueane, P., 2004. Distributions of exons and
introns in the human genome. In Silico Biol. 4, 387–393.
Sankoff, D., 2001. Gene and genome duplication. Curr. Opin. Genet. Dev. 11, 681–684.
Sargsyan, K., Lim, C., 2010. Arrangement of 3D structural motifs in ribosomal RNA.
Nucleic Acids Res. 38, 3512–3522.
Schuster, P., 1996. How does complexity arise in evolution. Complexity 2, 22–30.
Sellis, D., Provata, A., Almirantis, Y., 2007. Alu and LINE1 distributions in the human
chromosomes: evidence of global genomic organization expressed in the form
of power laws. Mol. Biol. Evol. 24, 2385–2399.
Sherry, S.T., Ward, M.H., Kholodov, M., Baker, J., Phan, L., Smigielski, E.M., Sirotkin,
K., 2001. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29,
308–311.
Stankiewicz, P., Shaw, C.J., Withers, M., Inoue, K., Lupski, J.R., 2004. Serial segmental
duplications during primate evolution result in complex human genome
architecture. Genome Res. 14, 2209–2220.
Stergachis, A.B., Haugen, E., Shafer, A., Fu, W., Vernot, B., Reynolds, A., Raubitschek,
A., Ziegler, S., LeProust, E.M., Akey, J.M., Stamatoyannopoulos, J.A., 2013. Exonic
transcription factor binding directs codon choice and affects protein evolution.
Science 342, 1367–1372.
Stormo, G.D., 2000. DNA binding sites: representation and discovery. Bioinformatics
16, 16–23.
Thomas, J., 2002. White House Concerns Block Doubling Bill Jumbled DNA Separates
Chimps and Humans Koski Steps Down After Bumpy Ride. The First Director of
a Federal Office Created., pp. 64–65.
Trieu, T., Cheng, J., 2014. Large-scale reconstruction of 3D structures of human chromosomes
from chromosomal contact data. Nucleic Acids Res. 42, e52.
Varela, M.A., Amos, W., 2010. Heterogeneous distribution of SNPs in the human
genome: microsatellites as predictors of nucleotide diversity and divergence.
Genomics 95, 151–159.
Voss, R., 1992. Evolution of long-range fractal correlations and 1/f noise in DNA base
sequences. Phys. Rev. Lett. 68, 3805–3808.
Weatheritt, R.J., Babu, M.M., 2013. The hidden codes that shape protein evolution.
Science 342, 1325–1326.
Wittkopp, P., Kalay, G., 2011. Cis-regulatory elements: molecular mechanisms
and evolutionary processes underlying divergence. Nat. Rev. Genet.
13, 59–69.
Wright, M.A., Kharchenko, P., Church, G.M., Segrè, D., 2007. Chromosomal periodicity
of evolutionarily conserved gene pairs. Proc. Natl. Acad. Sci. U.S.A. 104,
10559–10564.
Zentner, G.E., Tesar, P.J., Scacheri, P.C., 2011. Epigenetic signatures distinguish multiple
classes of enhancers with distinct cellular functions. Genome Res. 21,
1273–1283.
Zhao, Z., Fu, Y.-X., Hewett-Emmett, D., Boerwinkle, E., 2003. Investigating single
nucleotide polymorphism (SNP) density in the human genome and its implications
for molecular evolution. Gene 312, 207–213.