# Comparative Analysis of CpG Islands in Four Fish Genomes

Authors: Leng Han; Zhongming Zhao
Journal: Comparative and Functional Genomics
 (2008)

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## Abstract

There has been much interest in CpG islands (CGIs), clusters of CpG dinucleotides in GC-rich regions, because they are considered gene markers and involved in gene regulation. To date, there has been no genome-wide analysis of CGIs in the fish genome. We first evaluated the performance of three popular CGI identification algorithms in four fish genomes (tetraodon, stickleback, medaka, and zebrafish). Our results suggest that Takai and Jones' (2002) algorithm is most suitable for comparative analysis of CGIs in the fish genome. Then, we performed a systematic analysis of CGIs in the four fish genomes using Takai and Jones' algorithm, compared to other vertebrate genomes. We found that both the number of CGIs and the CGI density vary greatly among these genomes. Remarkably, each fish genome presents a distinct distribution of CGI density with some genomic factors (e.g., chromosome size and chromosome GC content). These findings are helpful for understanding evolution of fish genomes and the features of fish CGIs.


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## Body

## 1. Introduction

CGIs
are clusters of CpG dinucleotides in GC-rich regions, usually ~1 kb long [1]. They are identified in
the promoter regions of approximately 50% of genes in vertebrate genomes and
are considered gene markers. CpG islands are usually unmethylated in a genome,
especially in the promoter regions [2], in contrast, ~80% of
CpG dinucleotides in the mammalian genomes are methylated [2, 3]. The mutation rate of
the methylated CpG (5mCpG) to TpG was estimated to be ~10–50-folds higher
than that of the unmethylated CpG site due to a high rate of deamination at the
5mCpG, which subsequently leads to an overall loss of CpG dinucleotides and a
potential loss of CGIs [4, 5]. Recent studies found
that CGIs may be methylated under an abnormal condition or even in normal
cells. Weber et al. [6] found an association of
DNA methylation in CpG-poor promoters in the germline with an increased loss of
CpG dinucleotides, implying that characteristics of the CGIs have been weakened
or even vanished in the course of evolution. Methylation of promoter-associated
CpG islands has been found to play an important role in gene silencing, genomic
imprinting, X-chromosome inactivation, and carcinogenesis [7, 8].Antequera
and Bird [9] hypothesized that CGIs
arose at the dawn of vertebrate evolution and gene-associated CGIs might be
lost due to de novo
methylation. The number of CGIs varies greatly in mammalian genomes, for
example, ~20,500 mouse CGIs compared to ~37,500 human CGIs and ~58,300 dog
CGIs, even though they have similar gene numbers and genome sizes. Comparisons
of CGIs among a few model mammalian genomes, especially between the human and
mouse, have been performed [9–11]. Those studies revealed
that the mouse has undergone a faster CpG loss than the human, thus, has fewer
CGIs and weaker CGI characteristics. The loss of CGIs in those studies was
largely attributed to the methylation. However, methylation could not explain
all the differences of CGIs in vertebrate genomes. For example, the dog genome
has a much larger number of CGIs and higher CGI density than other mammalian
genomes, but this large difference is mainly caused by many more CGIs in the
dog’s noncoding regions (unpublished data). The number of gene-associated CGIs in
the dog genome is not much different from that in other mammalian genomes.
Moreover, previous analyses of CGIs in the chicken genome revealed a high
concentration of CGIs on microchromosomes [12, 13]. These
results suggest that some other genomic factors might have also played
important roles in the course of CGI evolution.Animals evolved in the direction of cold-blooded vertebrates
to warm-blooded vertebrates. Bird’s early study [3] found a different CpG
distribution among vertebrates and found that the ratio of the observed over the expected CpGs (ObsCpG/ExpCpG)
in cold-blooded vertebrates (e.g., fish) was much higher than in warm-blooded
vertebrates (e.g., human and mouse), suggesting a lower or even lack of
methylation process in cold-blooded vertebrates. So far, it remains largely
unknown of CGIs and their distribution in nonmammalian genomes, especially in
the fish, reptile, and amphibian. Fish, which is among the first appeared vertebrates on earth, still has ancient
noncoding elements conserved with the human [14]. Several fish genomes have
been sequenced recently. This provides us an opportunity to examine and compare
CGIs in fish genomes.In
1987, Gardiner-Garden and Frommer [15] first proposed an
algorithm for scanning CGIs in a DNA sequence. This algorithm, which uses three
search parameters GC content, ObsCpG/ExpCpG, and length,
has been widely applied in numerous analyses of CGIs in single genes or small
sets of genomic sequences. However, this algorithm significantly inflates the
number of CGIs because many repeats (e.g., Alu),
which are abundant in vertebrate genomes, also meet the criteria of this
algorithm. To solve this problem, Takai and Jones [7] performed a systematic
evaluation of the three parameters in Gardiner-Garden and Frommer’s algorithm
and provided an optimal set of parameters. Starting from here, we abbreviated
these two algorithms as “GF” and “TJ” to save space. TJ’s algorithm can
effectively exclude the false positive results from repeats and also more
likely identify CGIs associated with the 5′ end of genes [7]. However, their
evaluation was mainly based on the human genome. Whether it is suitable for
other genomes, especially the cold-blooded vertebrates, needs further
investigation. More recently, Hackenberg et
al. [16] developed a new
algorithm, namely, CpGcluster, that entirely depends on the statistical
significance of a CpG cluster from the random sequence in the same genome. One
of its major features is no requirement of minimum length for a CGI. Besides
these three major algorithms, there are some other applications such as CpGProD
[17] and CpGIE [18], which are essentially
based on TJ’s algorithm. These applications give out similar CGI findings
because they modified only some of the parameters (e.g., size of the sliding
window, number of steps for scanning CGIs).In this study, we first
evaluated the performance of three popular CGI identification algorithms. Then,
we performed a systematic analysis of CGIs in five publicly available fish
genomes (tetraodon, stickleback, medaka, zebrafish, and
fugu) and examined CGI density at the chromosome level in four of them
(except for fugu because of the lack of assembled chromosome data). We also compared
the features of fish CGIs to other vertebrate genomes.


## 2. Materials and Methods

### 2.1. Genome Sequences and
other Genome Information

We
downloaded the reference sequences of five fish genomes (tetraodon,
stickleback, medaka, zebrafish, and fugu) from UCSC Genome Browser (http://genome.ucsc.edu/).
The genomic sequences have been assembled into chromosomes in four fish genomes
(tetraodon, stickleback, medaka, and zebrafish) but not in the fugu genome.
Therefore, we analyzed and compared CpG islands mainly in the four fish genomes
in this study. The
number of genes in each fish genome was retrieved from the Ensembl database (http://www.ensembl.org/, build 49).We used the EMBOSS package [19] to calculate the genome
size, GC content, and ObsCpG/ExpCpG in these genomes.
Table 1 summarizes the statistics of these fish genomes.Table 1
Summary of five fish genomes. CpG islands in this table were
identified by Takai and Jones’ algorithm [7]. The number of genes in each fish
genome was based on the Ensembl database (http://www.ensembl.org/, build 49). ObsCpG/ExpCpG:
the ratio of the observed over the expected CpG dinucleotides in a fish genome.
Common nameSpecies nameLength (Mb)Number of genesGC content (%)ObsCpG/ExpCpGNumber of CGIsCGI density (/Mb)TetraodonTetraodon nigroviridis18728 63945.90.60130 175161.6SticklebackGasterosteus aculeatus39122 31044.50.66261 768157.8MedakaOryzias latipes58220 15940.10.47921 52237.0ZebrafishDanio rerio152425 58236.50.53122 39214.7FuguTakifugu rubripes35119 24445.50.56547 251134.5


### 2.2. Algorithms for the Identification of CGIs

We
scanned CGIs in genomic sequences using three algorithms. First, we applied
TJ’s algorithm, which is optimized for searching CGIs associated with the5′ end of genes in the human and other
mammalian genomes. Its search criteria are: GC content ≥ 55%, ObsCpG/ExpCpG≥0.65, and length ≥ 500 bp. Second, we used GF’s algorithm: GC content > 50%,
ObsCpG/ExpCpG>0.60, and length > 200 bp. These
parameters were from the original publication [15], but we applied them to
only the nonrepeat portions of the genomes as many repeats in the genomes also
meet these parameters [1, 20].In TJ’s algorithms, there are eight iterative steps to scan all the
possible CGIs in a genome. (1) Set a window size to be 200 bases at the start
position of a sequence and calculate GC content (%) and ObsCpG/ExpCpG in the first window. Here, ObsCpG/ExpCpG =
NCpG/(NC × NG) × N where NCpG, NC,
NG, and N are, respectively, the number of dinucleotide CpGs,
nucleotide Cs, nucleotide Gs, and all nucleotides (A, C, G, and T) in the sequence
(i.e., 200 nucleotides). Shift the window 1 base each time until the window
meets the criteria for a CGI. (2) Once a seed window (i.e., it meets the
criteria) is found, move the window 200 bases afterward and then evaluate the
new window again. (3) Repeat step 2 until the window does not meet the
criteria. (4) Shift the last window 1 base each time toward the 5′ end until it meets the criteria. (5)
Evaluate the whole segment (i.e., from the start position of the seed window to
the end position of the current window). If it does not meet the criteria, trim
1 base from each side until it meets the criteria. (6) Connect two individual
CGI fragments if they are separated by less than 100 bases. (7) Repeat step 5
to evaluate the new sequence segment until it meets the criteria. (8) Reset
start position immediately after the CGI identified at step 7 and go to step 1.
This computational procedure has been implemented in the CpG island searching
program (CpGi130) [21], which was used in this
study. Similar steps were implemented for GF’s
algorithm.Third,
we applied CpGcluster developed by Hackenberg et al. [16] to scan CGIs in
genomes. There are two main steps in the implementation of CpGcluster. (1)
Search CpG clusters based on statistical properties of the physical distances
between neighboring CpG dinucleotides on a DNA sequence. (2) Assign a P-value,
the probability of such a cluster appearing by chance in a random sequence, to
each CpG cluster in step 1. Those clusters with a P-value less than 10−5 were considered statistically significant CGIs. No minimum size length is
required in CpGcluster.


## 2.1. Genome Sequences and
other Genome Information

We
downloaded the reference sequences of five fish genomes (tetraodon,
stickleback, medaka, zebrafish, and fugu) from UCSC Genome Browser (http://genome.ucsc.edu/).
The genomic sequences have been assembled into chromosomes in four fish genomes
(tetraodon, stickleback, medaka, and zebrafish) but not in the fugu genome.
Therefore, we analyzed and compared CpG islands mainly in the four fish genomes
in this study. The
number of genes in each fish genome was retrieved from the Ensembl database (http://www.ensembl.org/, build 49).We used the EMBOSS package [19] to calculate the genome
size, GC content, and ObsCpG/ExpCpG in these genomes.
Table 1 summarizes the statistics of these fish genomes.Table 1
Summary of five fish genomes. CpG islands in this table were
identified by Takai and Jones’ algorithm [7]. The number of genes in each fish
genome was based on the Ensembl database (http://www.ensembl.org/, build 49). ObsCpG/ExpCpG:
the ratio of the observed over the expected CpG dinucleotides in a fish genome.
Common nameSpecies nameLength (Mb)Number of genesGC content (%)ObsCpG/ExpCpGNumber of CGIsCGI density (/Mb)TetraodonTetraodon nigroviridis18728 63945.90.60130 175161.6SticklebackGasterosteus aculeatus39122 31044.50.66261 768157.8MedakaOryzias latipes58220 15940.10.47921 52237.0ZebrafishDanio rerio152425 58236.50.53122 39214.7FuguTakifugu rubripes35119 24445.50.56547 251134.5


## 2.2. Algorithms for the Identification of CGIs

We
scanned CGIs in genomic sequences using three algorithms. First, we applied
TJ’s algorithm, which is optimized for searching CGIs associated with the5′ end of genes in the human and other
mammalian genomes. Its search criteria are: GC content ≥ 55%, ObsCpG/ExpCpG≥0.65, and length ≥ 500 bp. Second, we used GF’s algorithm: GC content > 50%,
ObsCpG/ExpCpG>0.60, and length > 200 bp. These
parameters were from the original publication [15], but we applied them to
only the nonrepeat portions of the genomes as many repeats in the genomes also
meet these parameters [1, 20].In TJ’s algorithms, there are eight iterative steps to scan all the
possible CGIs in a genome. (1) Set a window size to be 200 bases at the start
position of a sequence and calculate GC content (%) and ObsCpG/ExpCpG in the first window. Here, ObsCpG/ExpCpG =
NCpG/(NC × NG) × N where NCpG, NC,
NG, and N are, respectively, the number of dinucleotide CpGs,
nucleotide Cs, nucleotide Gs, and all nucleotides (A, C, G, and T) in the sequence
(i.e., 200 nucleotides). Shift the window 1 base each time until the window
meets the criteria for a CGI. (2) Once a seed window (i.e., it meets the
criteria) is found, move the window 200 bases afterward and then evaluate the
new window again. (3) Repeat step 2 until the window does not meet the
criteria. (4) Shift the last window 1 base each time toward the 5′ end until it meets the criteria. (5)
Evaluate the whole segment (i.e., from the start position of the seed window to
the end position of the current window). If it does not meet the criteria, trim
1 base from each side until it meets the criteria. (6) Connect two individual
CGI fragments if they are separated by less than 100 bases. (7) Repeat step 5
to evaluate the new sequence segment until it meets the criteria. (8) Reset
start position immediately after the CGI identified at step 7 and go to step 1.
This computational procedure has been implemented in the CpG island searching
program (CpGi130) [21], which was used in this
study. Similar steps were implemented for GF’s
algorithm.Third,
we applied CpGcluster developed by Hackenberg et al. [16] to scan CGIs in
genomes. There are two main steps in the implementation of CpGcluster. (1)
Search CpG clusters based on statistical properties of the physical distances
between neighboring CpG dinucleotides on a DNA sequence. (2) Assign a P-value,
the probability of such a cluster appearing by chance in a random sequence, to
each CpG cluster in step 1. Those clusters with a P-value less than 10−5 were considered statistically significant CGIs. No minimum size length is
required in CpGcluster.


## 3. Results

### 3.1. Evaluation of Algorithms on CGI Identifications in Fish Genomes

We evaluated whether the three major algorithms could
reliably identify CGIs in fish genomes. Figure1 shows the numbers of CGIs and
the CGI densities identified by these algorithms in four fish genomes
(tetraodon, stickleback, medaka, and zebrafish). Overall, these three
algorithms gave out much different numbers of CGIs and, correspondingly, CGI
densities. First, we compared the results from GF’s and TJ’s algorithms. GF’s
algorithm gave out a much larger number of CGIs than TJ’s, which is expected
because the former one used much less
stringent
criteria (e.g., minimum length 200 bp). Such a large difference has been shown
in other studies [11, 16]. It is
important to note that, although the large difference was observed in each
genome (e.g., in the tetraodon, 75 771 CGIs by GF’s algorithm versus 30 175 by
TJ’s algorithm), both algorithms gave out the same comparative results among
genomes. For example, both algorithms had the same rank of CGI density:
tetraodon > stickleback > medeka > zebrafish. Because the number of
CGIs identified by GF’s algorithm is always substantially greater than the
number of genes in a mammalian genome [1, 20], which
also holds in these four fish genomes here (Table 1, gene number ranged from 20 159–28 639), we consider TJ’s algorithm is more
suitable for CGI identification in fish genomes.Evaluation of algorithms on CGI
identifications in four fish genomes. (a) Number of CGIs, (b) CGI density (per Mb). The
black, white, and gray bars represent the CGIs identified by Takai and Jones’ [7], Gardiner-Garden and Frommer’s [15], and CpGcluster [16] algorithms, respectively.
(a)(b)We next compared the
performance of TJ’s
algorithm with CpGcluster. These two algorithms generated different results
too. The number of CGIs in the zebrafish was 171 865 by CpGcluster, which is 7.7 times that (22 392) by TJ’s algorithm. Conversely, we
found a smaller number of CGIs in the stickleback (47 386) by CpGcluster than that (61 768)
by TJ’s algorithm. Furthermore, CGI
density, which was measured by the average counts of CGIs in 1-Mb sequence, was
nearly the same among the four genomes by using CpGcluster, opposite to the
great variation found by TJ’s algorithm (Figure1(b)). Because CpGcluster
identified a CGI by its statistical significance from a random sequence in the
same genome, its CGIs were identified relative to the genome characteristics.
This likely eliminated the influence of some genomic factors on CGIs. For
example, the GC content of the zebrafish genome (36.5%) is much lower than that
(45.9%) of the tetraodon genome. According to the traditional definition, CGIs
are in the GC-rich regions. This means that, without considering other factors,
it is expected to find more CGIs in tetraodon than in zebrafish. This indeed was
observed by both TJ’s and
GF’s algorithms. However, CpGcluster evaluated the CpG clusters (i.e., CGIs)
from the sequence background in the same genome, which effectively eliminated
the difference between the genomes. This is why we observed similar CGI density
among the four fish genomes by the CpGcluster.We further examined the
length distribution of CGIs identified by the three algorithms. In the
tetraodon, 53% of GF’s CGIs had length between 200 and 500 bp while 47% longer
than 500 bp. As expected, TJ’s algorithm had longer CGIs: 72% CGIs whose
lengths were between 500–1000 bp and 28%
were >1000 bp. Surprisingly, almost all the CGIs identified by CpGcluster
(98%) were shorter than 200 bp (Figure2). The similar length distribution was
observed in other fish genomes. It has been widely accepted that CGIs are often
longer than 500 bp [1, 2]. Therefore, at least for
comparative genomic analysis, we suggest that TJ’s algorithm is more suitable for identification of CGIs in
fish genomes than the other two algorithms.Length
distribution of CGIs identified by the three algorithms. The data were based on
the tetraodon sequences, but the similar pattern was observed in other fish
genomes. The black, white, and gray bars represent the CGIs identified by Takai
and Jones’ [7], Gardiner-Garden and Frommer’s
[15], and CpGcluster [16] algorithms,
respectively.
(a)(b)


### 3.2. CGIs vary Greatly among Fish Genomes

According
to our evaluation above, we applied Takai and Jones’ algorithm to identify CGIs
in fish genomes. Table1 shows the number of CGIs and CGI density in each
genome. The number of CGIs, which ranged from 21 522 (medaka) to 61 768
(stickleback), varied greatly among the five fish genomes. Strikingly, there
were 61 768 CGIs in only 391 Mb stickleback genomic sequences compared to only
22 392 CGIs in 1524 Mb zebrafish sequences. Because the genome size varied
greatly, we calculated the CGI density and made another comparison. Again, CGI
density varied greatly: CGI density in both the tetraodon (161.6 CGIs/Mb) and
stickleback (157.8 CGIs/Mb) was approximately 11-fold higher than that in the
zebrafish (14.7 CGIs/Mb).We
next examined CGI density at the chromosomal level in the four fish genomes
(tetraodon, stickleback, medaka, and zebrafish). The fugu data were excluded
because of the lack of assembled chromosomes. Interestingly, when we plotted
CGI density over some chromosome parameters (size, GC content, and ObsCpG/ExpCpG),
we found that the chromosomes from each fish genome clustered but overall they
were separated from other fish genomes (Figure 3). This distinct pattern is
especially obvious in the plots of CGI density over chromosome GC content
(Figure 3(b)) and over chromosome ObsCpG/ExpCpG (Figure 3(c)).
Such a feature was not observed in the mammalian genomes [22]. Moreover, the plots in
Figure 3 indicate a significant negative correlation between CGI density and
log10(chromosome size) (r =
−0.81, P = 5.5 × 10−23), a
significant positive correlation between CGI density and chromosome GC content
(r = 0.96, P = 7.9 × 10−50), and, as expected, a significant
positive correlation between CGI density and chromosome ObsCpG/ExpCpG (r = 0.86, P = 2.4 × 10−28) in fish genomes. However, the
relationship between CGI density and chromosome size in each genome is much
different (Figure 3(a)). In both the tetraodon and stickleback, CGI density was
high and also varied greatly. Conversely, CGI density in the zebrafish and
medaka had a small variation among their chromosomes (Figure 3(a)). Overall,
the correlation between CGI density and chromosome GC content was strong
regardless only one genome or all four fish genomes being considered (Figure 3(b)),
suggesting that chromosome GC content is likely a major genetic factor
influencing CGI density.Distinct features of CpG islands in
four fish genomes. (a) Distribution of CGI density (per Mb) with Log10 (chromosome
size), (b) distribution of CGI density (per Mb) with chromosome GC content (%),
(c) distribution of CGI density (per Mb) with chromosome ObsCpG/ExpCpG.
(a)(b)(c)Among the five fish species we studied, zebrafish first diverged
about 110–160 million years
ago (MYA) [23] and its genome size is
the largest. An interesting feature in the zebrafish is that its CGI density is
similar among its chromosomes. This feature might be attributed to its similar
chromosome GC content (Figure 3(b)). Three closely related fish species,
tetraodon, fugu, and stickleback, diverged 60–80 MYA and are
evolutionarily related. They have similar genomic features such as small genome
size, high GC content, and high ObsCpG/ExpCpG (Table 1). Tetraodon
and stickleback had the similar distribution in Figure 3. Medaka was most
recently evolved and has intermediate genomic features compared to other four
fish species.


## 3.1. Evaluation of Algorithms on CGI Identifications in Fish Genomes

We evaluated whether the three major algorithms could
reliably identify CGIs in fish genomes. Figure1 shows the numbers of CGIs and
the CGI densities identified by these algorithms in four fish genomes
(tetraodon, stickleback, medaka, and zebrafish). Overall, these three
algorithms gave out much different numbers of CGIs and, correspondingly, CGI
densities. First, we compared the results from GF’s and TJ’s algorithms. GF’s
algorithm gave out a much larger number of CGIs than TJ’s, which is expected
because the former one used much less
stringent
criteria (e.g., minimum length 200 bp). Such a large difference has been shown
in other studies [11, 16]. It is
important to note that, although the large difference was observed in each
genome (e.g., in the tetraodon, 75 771 CGIs by GF’s algorithm versus 30 175 by
TJ’s algorithm), both algorithms gave out the same comparative results among
genomes. For example, both algorithms had the same rank of CGI density:
tetraodon > stickleback > medeka > zebrafish. Because the number of
CGIs identified by GF’s algorithm is always substantially greater than the
number of genes in a mammalian genome [1, 20], which
also holds in these four fish genomes here (Table 1, gene number ranged from 20 159–28 639), we consider TJ’s algorithm is more
suitable for CGI identification in fish genomes.Evaluation of algorithms on CGI
identifications in four fish genomes. (a) Number of CGIs, (b) CGI density (per Mb). The
black, white, and gray bars represent the CGIs identified by Takai and Jones’ [7], Gardiner-Garden and Frommer’s [15], and CpGcluster [16] algorithms, respectively.
(a)(b)We next compared the
performance of TJ’s
algorithm with CpGcluster. These two algorithms generated different results
too. The number of CGIs in the zebrafish was 171 865 by CpGcluster, which is 7.7 times that (22 392) by TJ’s algorithm. Conversely, we
found a smaller number of CGIs in the stickleback (47 386) by CpGcluster than that (61 768)
by TJ’s algorithm. Furthermore, CGI
density, which was measured by the average counts of CGIs in 1-Mb sequence, was
nearly the same among the four genomes by using CpGcluster, opposite to the
great variation found by TJ’s algorithm (Figure1(b)). Because CpGcluster
identified a CGI by its statistical significance from a random sequence in the
same genome, its CGIs were identified relative to the genome characteristics.
This likely eliminated the influence of some genomic factors on CGIs. For
example, the GC content of the zebrafish genome (36.5%) is much lower than that
(45.9%) of the tetraodon genome. According to the traditional definition, CGIs
are in the GC-rich regions. This means that, without considering other factors,
it is expected to find more CGIs in tetraodon than in zebrafish. This indeed was
observed by both TJ’s and
GF’s algorithms. However, CpGcluster evaluated the CpG clusters (i.e., CGIs)
from the sequence background in the same genome, which effectively eliminated
the difference between the genomes. This is why we observed similar CGI density
among the four fish genomes by the CpGcluster.We further examined the
length distribution of CGIs identified by the three algorithms. In the
tetraodon, 53% of GF’s CGIs had length between 200 and 500 bp while 47% longer
than 500 bp. As expected, TJ’s algorithm had longer CGIs: 72% CGIs whose
lengths were between 500–1000 bp and 28%
were >1000 bp. Surprisingly, almost all the CGIs identified by CpGcluster
(98%) were shorter than 200 bp (Figure2). The similar length distribution was
observed in other fish genomes. It has been widely accepted that CGIs are often
longer than 500 bp [1, 2]. Therefore, at least for
comparative genomic analysis, we suggest that TJ’s algorithm is more suitable for identification of CGIs in
fish genomes than the other two algorithms.Length
distribution of CGIs identified by the three algorithms. The data were based on
the tetraodon sequences, but the similar pattern was observed in other fish
genomes. The black, white, and gray bars represent the CGIs identified by Takai
and Jones’ [7], Gardiner-Garden and Frommer’s
[15], and CpGcluster [16] algorithms,
respectively.
(a)(b)


## 3.2. CGIs vary Greatly among Fish Genomes

According
to our evaluation above, we applied Takai and Jones’ algorithm to identify CGIs
in fish genomes. Table1 shows the number of CGIs and CGI density in each
genome. The number of CGIs, which ranged from 21 522 (medaka) to 61 768
(stickleback), varied greatly among the five fish genomes. Strikingly, there
were 61 768 CGIs in only 391 Mb stickleback genomic sequences compared to only
22 392 CGIs in 1524 Mb zebrafish sequences. Because the genome size varied
greatly, we calculated the CGI density and made another comparison. Again, CGI
density varied greatly: CGI density in both the tetraodon (161.6 CGIs/Mb) and
stickleback (157.8 CGIs/Mb) was approximately 11-fold higher than that in the
zebrafish (14.7 CGIs/Mb).We
next examined CGI density at the chromosomal level in the four fish genomes
(tetraodon, stickleback, medaka, and zebrafish). The fugu data were excluded
because of the lack of assembled chromosomes. Interestingly, when we plotted
CGI density over some chromosome parameters (size, GC content, and ObsCpG/ExpCpG),
we found that the chromosomes from each fish genome clustered but overall they
were separated from other fish genomes (Figure 3). This distinct pattern is
especially obvious in the plots of CGI density over chromosome GC content
(Figure 3(b)) and over chromosome ObsCpG/ExpCpG (Figure 3(c)).
Such a feature was not observed in the mammalian genomes [22]. Moreover, the plots in
Figure 3 indicate a significant negative correlation between CGI density and
log10(chromosome size) (r =
−0.81, P = 5.5 × 10−23), a
significant positive correlation between CGI density and chromosome GC content
(r = 0.96, P = 7.9 × 10−50), and, as expected, a significant
positive correlation between CGI density and chromosome ObsCpG/ExpCpG (r = 0.86, P = 2.4 × 10−28) in fish genomes. However, the
relationship between CGI density and chromosome size in each genome is much
different (Figure 3(a)). In both the tetraodon and stickleback, CGI density was
high and also varied greatly. Conversely, CGI density in the zebrafish and
medaka had a small variation among their chromosomes (Figure 3(a)). Overall,
the correlation between CGI density and chromosome GC content was strong
regardless only one genome or all four fish genomes being considered (Figure 3(b)),
suggesting that chromosome GC content is likely a major genetic factor
influencing CGI density.Distinct features of CpG islands in
four fish genomes. (a) Distribution of CGI density (per Mb) with Log10 (chromosome
size), (b) distribution of CGI density (per Mb) with chromosome GC content (%),
(c) distribution of CGI density (per Mb) with chromosome ObsCpG/ExpCpG.
(a)(b)(c)Among the five fish species we studied, zebrafish first diverged
about 110–160 million years
ago (MYA) [23] and its genome size is
the largest. An interesting feature in the zebrafish is that its CGI density is
similar among its chromosomes. This feature might be attributed to its similar
chromosome GC content (Figure 3(b)). Three closely related fish species,
tetraodon, fugu, and stickleback, diverged 60–80 MYA and are
evolutionarily related. They have similar genomic features such as small genome
size, high GC content, and high ObsCpG/ExpCpG (Table 1). Tetraodon
and stickleback had the similar distribution in Figure 3. Medaka was most
recently evolved and has intermediate genomic features compared to other four
fish species.


## 4. Discussion

In this
study, we performed the first systematic survey of CGIs in four fish genomes.
We found that the number of CGIs and the CGI density varied greatly in these
fish genomes. Moreover, the CGI density in these fish genomes was significantly
correlated with some genomic factors at the chromosome level such as chromosome
size, GC content, and ObsCpG/ExpCpG. It seems that the
correction between the CGI density and GC content is strong. However, this work
is still preliminary. Future work is warranted for identifying other genomic
factors that are also correlated with CGI distribution and for evaluating which
genomic factor(s) prevailed in the course of CGI evolution in fish genomes.The
extent of CGI variation among fish genomes is stronger than other vertebrate
genomes. We did a similar analysis of CGIs in 9 mammals (human, chimpanzee,
macaque, mouse, rat, dog, cow, horse, and opossum) whose whole genomes have
been assembled. Among the 9 mammalian genomes, the highest CGI density (25.3 CGIs/Mb, the dog genome) was 3.4 times the lowest CGI density (7.5 CGIs/Mb, the
opossum genome). This is much weaker than the ~11-fold difference observed in
the fish genomes (Table1). Interestingly, each fish genome had a distinct
distribution of CGI density at the chromosome level (Figure 3); a pattern was
not found in mammalian genomes. This unique feature might be caused by genetic
(sequence composition evolution) and environmental factors such as water
temperature, speed of flow, extent of light in different depth of water during
the long evolutionary period after the divergence of common ancestor of fishes.Strong CpG depletion is a common feature in mammalian
genomes, for example, ~75–80% of CpG dinucleotides were
depleted in the human and mouse genomes [1, 24, 25].
However, CpGs presented much more frequently in fish than in mammalian genomes.
The ObsCpG/ExpCpG ratio ranged from 0.479 to 0.662 in these fish genomes (Table 1),
remarkably higher than that in mammals. Methylation and subsequent deamination
is a main process to cause CpG depletion in warm-blooded vertebrate genomes. The ObsCpG/ExpCpG ratio in fishes may suggest a
similar influence of methylation/deamination in cold-blooded vertebrates, but
the extent was much weaker. A further comparative genomics analysis including
gene information may help us uncover how methylation/deamination process and
other genetic factors (e.g., recombination) influenced sequence composition
changes and CGI evolution in vertebrate genomes.We applied three different algorithms to identify CGIs in
the fish genome. The TJ and GF algorithms have been frequently applied to scan
CGIs in mammalian genomes and the CpGcluster was developed recently. The three algorithms gave out a much different
number of CGIs, CGI density, and CGI length distribution, but our evaluation clearly
indicated that the criteria of GF and CpGcluster algorithms were too generous.
For example, for many genes that had one CGI per gene locus identified
by TJ’s algorithm, we often found more than one but shorter CGI scattered in
the same region by GF’s algorithm or CpGcluster. Overall, our evaluation
suggests that TJ’s algorithm is likely most
suitable for CGI identifications in fish genomes. Because high quality gene annotations or
high-throughput experimental verification of CGIs has not made available in
nonmammalian genomes, an evaluation of gene-associated CGIs is restricted at
present. Thus, although the conclusions in this study would hold by any of the
three algorithms, caution should be used when identifying CGI(s) for a specific
gene in a
nonmammalian genome, especially in a cold-blooded vertebrate genome.



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*Source: 565631-2008-04-29.xml*