- Research article
- Open Access
Both selective and neutral processes drive GC content evolution in the human genome
© Pozzoli et al; licensee BioMed Central Ltd. 2008
Received: 18 December 2007
Accepted: 27 March 2008
Published: 27 March 2008
Mammalian genomes consist of regions differing in GC content, referred to as isochores or GC-content domains. The scientific debate is still open as to whether such compositional heterogeneity is a selected or neutral trait.
Here we analyze SNP allele frequencies, retrotransposon insertion polymorphisms (RIPs), as well as fixed substitutions accumulated in the human lineage since its divergence from chimpanzee to indicate that biased gene conversion (BGC) has been playing a role in within-genome GC content variation. Yet, a distinct contribution to GC content evolution is accounted for by a selective process. Accordingly, we searched for independent evidences that GC content distribution does not conform to neutral expectations. Indeed, after correcting for possible biases, we show that intron GC content and size display isochore-specific correlations.
We consider that the more parsimonious explanation for our results is that GC content is subjected to the action of both weak selection and BGC in the human genome with features such as nucleosome positioning or chromatin conformation possibly representing the final target of selective processes. This view might reconcile previous contrasting findings and add some theoretical background to recent evidences suggesting that GC content domains display different behaviors with respect to highly regulated biological processes such as developmentally-stage related gene expression and programmed replication timing during neural stem cell differentiation.
Mammalian genomes are non homogeneous with respect to base composition; striking variations in GC content occur over scales of hundred kilobases to megabases. The so called isochoric structure of the human genome was initially described by Bernardi and coworkers  and isochores were conceived as long genomic regions fairly homogeneous in their GC composition. Full sequencing of the human genome  indicated that the isochore model might need slight revision in that long regions are less compositionally homogeneous than previously thought and transitions at composition domains less sharp, so that the term "GC-content domain" was proposed instead of "isochore". Whatever designation we decide to adopt, the fact remains that isochores/GC content domains represent a large-scale genomic feature lacking a satisfactory interpretation. Indeed, the scientific debate is still open as to whether such a compositional heterogeneity is a selected or neutral trait and different hypothesis have been proposed [3, 4]. The biased gene conversion (BGC) model [5, 6] envisages a situation whereby recombination drives GC content in mammalian genomes through the preferential fixation of GC alleles following parental chromosome hetroduplex formation at meiosis. The effect is due to the bias toward GC nucleotides over AT during DNA repair at mismatched bases . The model therefore conceives of GC content variation as a by-product of recombination and, although supported by extensive evidence [8–13], its ability to explain isochore formation and maintenance has recently been criticized on different grounds. Spencer et al.  have indicated that recombination rates are too fast-evolving to have permanent effects on base composition; the authors therefore suggested that the cause-consequence relationships might be the other way round with GC rich regions promoting the occurrence of recombination hotspots. Also, several studies have suggested that GC content variation results from a selective process [15–20]. In particular, a role for GC content in chromatin organization and, therefore, gene regulation has been proposed [16, 18, 19]. Indeed, GC content has been shown to covary with genomic properties such as regulated replication or expression timing [21, 22], DNA bendability  and ability to B-Z transition , while the existence of a relationship between gene expression level (or breadth) and GC content is still controversial [9, 24, 25]. Nonetheless, a positive effect of increased coding sequence GC content on transcriptional efficiency has recently been experimentally demonstrated .
Up to now, with the exception of the above mentioned studies on gene expression, evidences of selection acting on GC content per se have been scant (see  for review). This might partially be due to difficulty in discriminating between BGC and weak selection.
Here we analyze SNP allele frequencies, retrotransposon insertion polymorphisms (RIPs), as well as fixed substitutions accumulated in the human lineage since its divergence from chimpanzee to indicate that both biased gene conversion (BGC) and selection have been playing a role in GC content variation.
Gene and intergenic sequences as well as intron/exon boundaries were obtained from the UCSC genome annotation database , assembly hg17. Gene selection was performed as previously described . Isochore boundary coordinates were derived from a previous work . Fine-scale recombination rates and recombination hotspot locations were obtained from the UCSC database; they are based on HapMap Phase I data . Pseudogene sequences and genomic locations derive from Pseudogene.org [29, 30]; only duplicated pseudogenes were selected and genes that generated more than one pseudogene were discarded (this procedure limits the number of observations but avoids multiple ties in statistical analysis). Also we retained only gene-pseudogenes pairs located in the same isochore type (for example, both gene and pseudogene located in isochores H1). The final data set consisted of 364 gene-pseudogene pairs. Duplicated pseudogenes often represent gene fragments; we therefore aligned gene-pseudogene couples using ClustalW  and corresponding intron-pseudointron pairs were retained only if they were both longer than 25 bp. Expression data were obtained as previously described  and derive from microarray data on 72 healthy human tissues. Mean expression level was calculated as the mean averaged over all tissues (counting as zero all tissues in which there is no detectable expression). Peak expression was calculated as the maximum expression level across all tissues and expression breadth was the number of distinct tissues expressing the gene.
Biallelic SNP locations and allele frequencies were downloaded from the HapMap web site  (non-redundant dataset, release 21a). Since previous authors  have indicated African populations as having genetic variation patterns most compatible with a constant population size, SNP allele frequencies were obtained for Yoruba (YRI), and derive from the genotyping of 60 individuals. The ancestral allele was inferred by alignment with the chimpanzee sequence (UCSC genome browser, assembly panTro1); SNPs were discarded when orthologous chimpanzee regions were unavailable or did not match either human allele. A total of about 2.2 million GC->AT and 1.7 million AT->GC SNPs were retained. We next purged SNPs at CpG sites, as well as those with no associated allele frequencies: the final dataset comprised more than 2 million SNPs.
For the analysis of substitution rates and stationary GC content (GC*), SNPs deriving from the Seattle SNP database , which derive from resequencing experiments, were used; for 206 human genes in the Seattle SNP dataset both chimpanzee and macaque orthologous loci could be retrieved.
Data on polymorphic repeat insertions were obtained through the UCSC genome database (RIPs track) and derive from the dbRIP database ; RIPs which have been associated with a human genetic disease were discarded. Also, polymorphic insertions were not included in the study if less than 10 instances were described for the same retrotransposon subfamily. Fixed transposon instances were identified and categorized using the UCSC annotation tables that rely on RepeatMasker. Since fixed and polymorphic repeat instances derive from different sources, we verified that no systematic bias occurs in the detection of either insertion events by calculating correlation between polymorphic and fixed chromosomal frequencies; significant correlations were retrieved for Alus, SVAs and L1s (Spearman rho = 0.854, 0.439 and 0.923, respectively; all p values < 0.05). Reference sequences for different retrotransposon subtypes were derived from Repbase Update [35, 36].
Analysis of allele frequency spectra
Introns/intergenic spacers were divided in 1 kb windows (1 kbseqs) starting from the most 5' nucleotide position (with respect to the chromosome orientation) and extending through the intron/intergenic region in 1000 bp non-overlapping steps (residual nucleotides in 3' were discarded). The following features were then calculated (or retrieved) for all 1 kbseqs: (1) fine scale recombination rate, (2) GC content, (3) allele frequencies of comprised SNPs, (4) expression parameters (peak, mean level and breadth) of the corresponding genes. In order to analyze allele frequency spectra after controlling for recombination rate, we applied the following procedure: starting from all 1 kbseqs, we identified couples of 1 kbseqs that differed less than 10% in recombination rate but displayed extremely different GC contents; in particular, we asked one partner of the recombination-coupled 1 kbseqs to be located below the 30th percentile in the distribution of 1 kbseqs GC content and the other one above the 70th percentile. This approach yielded two groups of sequences having extremely similar recombination rates (the equality of medians was checked using the Wilcoxon Rank Sum Test) but very different GC contents. A similar procedure was applied to analyze allele frequency spectra after controlling for recombination rate; in particular, 1 kbseq couples were created having similar GC content (a difference lower than 5% was required) but extremely different recombination rates. Again, two groups of sequences were obtained and used for comparisons.
The same approach described above can be extended to control for two variables: for example, in order to compare allele frequency spectra between highly and lowly expressed sequences, 1 kbseqs couples were identified that displayed both similar GC content and recombination rate (less than 5% and 10% difference, respectively) but extremely different expression levels. To allow comparisons between introns and intergenic spacers, percentile values were calculated over the complete set of 1 kbseqs, irrespective of their location.
In order to quantify the displacement of GC vs AT derived allele frequency distributions observed in Quantile-Quantile plots, differences between corresponding percentiles in the two distributions were summed. These measures were used to compare different groups of sequences selected on the basis of relevant variables (for example high and low GC content or recombination rate). We used bootstrapping procedures to assess the statistical significance of differences in allele frequencies shifts. In particular, 1000 permutations were performed and p values were calculated after normality assessment through the Shapiro-Wilk Test.
Multispecies alignments, substitution rates and stationary GC content
Orthologous human-chimpanzee-macaque regions were retrieved using the liftOver utility from UCSC (assemblies: panTro1 and rheMac2) with a cutoff of at least 70% remapping bases. Three-way species alignments were performed using MAVID .
In order to calculate substitution rates and GC* after controlling for ancestral GC content or recombination rate, a procedure similar to the one described above for SNP allele frequencies was applied, with the only difference that the inferred ancestral GC content was used instead of human GC content. In particular, 1 kbseqs couples were created (on the basis of either recombination rate or ancestral GC content) and their position subsequently mapped onto the 3-way species (human/chimpanzee/macaque) alignments; at this stage windows containing less than 600 perfectly aligning bases (i.e. the same nucleotide in the 3 species) were discarded and, for the remaining ones, the ancestral sequence was reconstructed by parsimony (only positions where the macaque was identical to either human or chimpanzee were considered).
The number of 1 kbseqs couples and the corresponding number of sites (in MB) that were analyzed for each comparison are reported in table notes. The number of sites does not exactly correspond to the number of sequences multiplied by 1000 because the presence of gaps in the human sequence (as compared to the two primates) can result in alignments longer than 1000 bp.
Substitution rates and stationary GC content were calculated using a previously developed neighbor-dependent substitution model [38, 39]. For each comparison, the two 1 kbseqs groups were then divided in 20 paired sub-samples of equal size; GC* and substitution rates were calculated for each sub-sample; average values are reported in the tables, together with p values obtained from two tailed Wilcoxon Rank Sum Tests for paired samples.
For the analysis of intron GC content in relation to size, we discarded first introns (due to their increased sequence constraints) and introns shorter than 750 bp (in order to spare constrained sequences at splice sites).
For the analysis, of recombination rates in long and short introns, for each gene, two introns were selected so that one was longer than 80th and the other shorter than 20th size percentile of introns length distribution. If no introns satisfied the criteria, the gene was not analyzed. Recombination rates were calculated for 500 bp centered around the median position of each intron. Differences in recombination rates were evaluated using the Wilcoxon Rank Sum Test.
For the analysis of fixed variations in recombination hotspots, we selected 897 hotspot on the basis of their size (smaller than 5 kb) and recombination rate (above the 80th percentile of the distribution of all hotspots); in 790 cases both chimpanzee and macaque orthologous regions could be retrieved.
Results and Discussion
Analysis of SNP allele frequencies
The analysis of SNP allele frequencies is a convenient strategy to study GC content evolution for two main reasons. First, when SNP allele frequencies are analyzed, no requirement for base composition stationarity is needed; this is relevant since base composition has been shown not to be at equilibrium in mammals [42, 43]. Second, given the fast evolution of recombination rates and hotspots [44, 45], allele frequencies of SNPs, which represent relatively recent variations, should carry the most evident signature of recombination-associated fixation biases.
Starting from our gene set, we therefore used the chimpanzee sequence to infer the ancestral allele so that variations could be classified as either GC->AT or AT->GC (SNPs at CpG sites were excluded). As previously noted  treating SNPs as independent data, despite the extensive presence of linkage disequilibrium in the human genome , introduces no bias since linkage is expected to be independent from the GC/AT status of individual SNPs.
These data suggest that GC content or other related features affect SNP allele segregation independently of recombination rates, although we cannot formally rule out the possibility that extinct recombination hotspots have played a role in the allele frequency spectra we observe. Indeed, as reported above, recombination hotspots are fast evolving [44, 45] and, therefore, the observed increased segregation of GC alleles in GC-rich regions might have been caused by the presence of an hotspot which is now inactive. Yet, if this latter were the case, given the relatively small effect that recombination has played in recent primate history on GC variation (see below), and given that most SNPs are specific to humans (and, therefore, relatively young), a direct role for GC content in promoting recombination events must be postulated to explain our results.
Since GC content has been shown to increase transcriptional activity  and some authors detected a positive correlation between gene expression parameters and GC content [16, 18, 19, 46], we wished to determine whether expression level, rather than GC content per se, was responsible for increased segregation of GC alleles. Yet, after controlling for both GC content and recombination rates (as described in methods, we used a similar approach to the one described above) we detected no significant difference in SNP allele frequencies between high- and low-level expressed genes (Figure 1c and Additional file 1). These data are not consistent with selection acting on highly or broadly expressed human genes to increase (or maintain) their GC content, although we cannot exclude that such a selection has acted during vertebrate evolution and subsequently relaxed in humans (further data on gene expression level and GC content evolution are reported below).
Analysis of retrotransposon insertion polymorphisms
We next wished to verify whether polymorphic and fixed repetitive elements were differently distributed depending on isochore type. Isochores were classified according to a recent  description and are referred to as L1, L2, H1, H2, and H3, in order of increasing GC levels. The results of transposable element distribution are reported in figure 2B and indicate that fixed Alus are significantly enriched (Chi Square Test, p < 10-5) within heavy isochores compared to polymorphic instances, while no different isochore distribution of fixed vs polymorphic repeats was evident for SVAs (possibly because of the small number of polymorphic insertions, n = 60) or L1s. For further confirmation we performed this same analysis using IsoFinder isochores  and the same results were obtained (see Additional file 2). These data confirm the preferential integration of Alus and L1s in AT-rich regions (polymorphic L1 and Alu distributions are relatively similar, Figure 2B), but indicate that additional forces, which relate to GC content, drive their fixation.
We believe that our results differ from previous reports showing no different GC content surrounding polymorphic and fixed Alus [51, 52] because of the larger sample of polymorphic elements we analyzed.
Analysis of substitution rates and stationary GC content
Substitution rates and GC* in intronic regions
Fixed GC content
Fixed recombination rate
A/T -> C/G
1.9 × 10-6
5.6 × 10-3
A/T -> G/C
1.9 × 10-6
1.9 × 10-6
A/T -> T/A
6.3 × 10-5
5.7 × 10-6
C/G -> G/C
1.9 × 10-6
1.2 × 10-2
C/G -> A/T
1.9 × 10-5
1.9 × 10-6
C/G -> T/A
1.9 × 10-6
1.9 × 10-6
CpG -> TpG
1.9 × 10-6
1.9 × 10-6
1.9 × 10-6
1.9 × 10-6
Number of sites (Mb)
Still, the data we report here are consistent with selection acting to maintain GC content but also with the presence of mutation biases operating in different GC content regions. In order to evaluate this latter possibility we calculated substitution rates and GC* using either fixed variations or SNPs; while SNPs can reasonably be thought to reflect mutation rates, fixed variations depend on both mutation rates and fixation probabilities. In this case, in order to avoid biases towards high frequency variants, the analysis was restricted to intronic regions deriving from 206 fully resequenced genes (see methods). Also, given the influence, documented above, of recombination on mutation rates, we used only gene regions (1 kb windows) showing low crossover rates.
Substitutions rates and GC* calculated for fixed substitutions and SNPs
A/T -> C/G
4.1 × 10-1
7.6 × 10-1
A/T -> G/C
6.9 × 10-2
7.3 × 10-1
A/T -> T/A
5.0 × 10-1
6.6 × 10-1
C/G -> G/C
5.5 × 10-1
9.0 × 10-1
C/G -> A/T
1.3 × 10-1
2.9 × 10-1
C/G -> T/A
7.3 × 10-1
CpG -> TpG
7.3 × 10-3
6.7 × 10-1
1.3 × 10-4
3.7 × 10-1
Number of sites (Mb)
Finally, we wished to verify whether analysis of substitution rates and GC* confirmed our above indication that gene expression levels have not been influencing base composition evolution in recent human history. In addition to serving as a useful confirmation, this approach allows analysis of fixed variations at CpGs, which is not feasible using SNP allele frequency spectra (due to recurrent mutations at these dinucleotides); this is relevant to the topic we are addressing since previous authors have indicated that both gene GC content and CpG level correlate with gene expression parameters . Again, we analyzed substitution rates and GC* in genes displaying narrow and wide expression breadth, after controlling for both GC content and recombination rates: we found no significant differences in either substitution rates (including CpG->TpG) or GC* between the two groups of sequences (not shown).
Local excess of AT->GC fixed variations at recombination hotspots
Average frequency of fixed substitutions in recombination hotspots and control regions
5' hotspot flank
5' control flank
3' hotspot flank
3' control flank
AT -> GC
GC -> AT
AT -> AT
GC -> GC
Intron GC distribution deviates from neutral expectations
We speculated that these results might originate from the preferential location of genes with short introns in regions displaying an extreme GC content. Yet, we verified that this is not the case, since introns belonging to the same gene tend to recapitulate the distributions observed above; in particular, introns belonging to genes located in light isochores tend to display an increase in GC content with size; those located in heavy isochores behave in the opposite manner. This is shown in figure 3: we selected genes having more than 15 introns and calculated, for each one, the correlation coefficient between the masked GC content of its intervening regions and their residual size; the distributions of correlation coefficients are shifted to positive and negative values for genes located in light and heavy isochores (Figure 3C), respectively. The significance of this finding was assessed by re-sampling (GC content and intron size were randomly assorted 1000 times for each gene).
All these analyzes have been performed after removal of transposable elements from both GC and size calculations; still, it might be argued that old, unrecognizable transposable elements have contributed to both intronic GC content and size, therefore explaining the observed distributions. In order to verify that this is not the sole explanation for our findings, we analyzed nonrepetitive GC content and residual intron length in intron-pseudointron pairs: old transposable elements gave the same contribution to both intron and pseudointrons (as their insertion predated pseudogene duplication) and therefore, once recognizable transposable elements have been masked, any difference in GC distribution is expected to be accounted for by repeat-independent events. Data are reported in figure 3D and show the homogenization of GC content in short pseudointrons (compared to real ones) located in light or heavy isochores.
It should be noted that many different isochore-identification methods have been described. We therefore verified that the results above were also obtained using IsoFinder  isochore definition (see additional file 4 for figures and details); also, the same results are obtained when the gene GC content (rather than isochore attribution) is used to define "light" (average GC content < 0.41) and "heavy" genes (see additional file 4 for figures and details).
In summary, these data indicate that intron GC content and size do not evolve independently; even when possible confounding effects such as size variation, presence of transposable elements and skewed genomic location are taken into account, isochore-specific correlations exist between intron size and GC content. Although there is no theoretic basis to expect it, we verified that no significant difference exists between recombination rates of long and short introns in both heavy and light isochores (not shown, see methods for details). Therefore, the data we report here can hardly be reconciled with a vision whereby BGC alone drives GC content evolution; rather, these finding might be consistent with a role of both base composition and intron size in gene regulation mediated by nucleosome positioning or chromatin conformation, as previously proposed [18, 23]. In agreement with this view, it has recently been shown  that a considerable amount of human intronic sequence is weakly selected, possibly due to its functioning in chromatin structure and transcription regulation.
A possible caveat of the data we report here concerns the accuracy of recombination rate measures; the data we used derive from HapMap and refer to crossover rates (and not gene conversion rates); evidences have suggested that, although crossovers and conversions arise from the same recombination-initiating events , the ratio of conversions to crossovers can vary among hotspots [55, 56]. It is therefore possible that correction for recombination rates leaves a residual; still, there is no a priori reason to expect the residual error to be skewed depending on background GC content. Also, as stated above, analysis of substitution rates in GC-poor vs GC-rich regions do not parallel rates in low-vs high-recombining regions, which would be expected if the same effect (i.e. BGC) were operating in both comparisons. Given this premise and taking into account the analysis of polymorphic repeat insertion and intron GC content distribution, we consider that the more parsimonious explanation for our results is that GC content is subjected to the action of both weak selection and BGC in the human genome with features such as nucleosome positioning or chromatin conformation possibly representing the final target of selective processes. This view might reconcile previous contrasting findings [6, 8–13, 15–20] and add some theoretical background to recent evidences suggesting that GC content domains display different behaviors with respect to highly regulated biological processes such as developmentally-stage related gene expression  and programmed replication timing during neural stem cell differentiation .
We wish to thank Roberto Giorda for discussion and helpful comments.
- Bernardi G, Olofsson B, Filipski J, Zerial M, Salinas J, Cuny G, Meunier-Rotival M, Rodier F: The mosaic genome of warm-blooded vertebrates. Science. 1985, 228: 953-958. 10.1126/science.4001930.View ArticlePubMedGoogle Scholar
- International Human Genome Sequencing Consortium: Initial sequencing and analysis of the human genome. Nature. 2001, 409: 860-921. 10.1038/35057062.View ArticleGoogle Scholar
- Eyre-Walker A, Hurst LD: The evolution of isochores. Nat Rev Genet. 2001, 2: 549-555. 10.1038/35080577.View ArticlePubMedGoogle Scholar
- Duret L, Eyre-Walker A, Galtier NA: A new perspective on isochore evolution. Gene. 2006, 385: 71-74. 10.1016/j.gene.2006.04.030.View ArticlePubMedGoogle Scholar
- Galtier N, Piganeau G, Mouchiroud D, Duret L: GC-content evolution in mammalian genomes: the biased gene conversion hypothesis. Genetics. 2001, 159: 907-911.PubMed CentralPubMedGoogle Scholar
- Marais G: Biased gene conversion: implications for genome and sex evolution. Trends Genet. 2003, 19: 330-338. 10.1016/S0168-9525(03)00116-1.View ArticlePubMedGoogle Scholar
- Brown TC, Jiricny J: Different base/base mispairs are corrected with different efficiencies and specificities in monkey kidney cells. Cell. 1988, 54: 705-711. 10.1016/S0092-8674(88)80015-1.View ArticlePubMedGoogle Scholar
- Birdsell JA: Integrating genomics, bioinformatics, and classical genetics to study the effects of recombination on genome evolution. Mol Biol Evol. 2002, 19: 1181-1197.View ArticlePubMedGoogle Scholar
- Lercher MJ, Smith NG, Eyre-Walker A, Hurst LD: The evolution of isochores: evidence from SNP frequency distributions. Genetics. 2002, 162: 1805-1810.PubMed CentralPubMedGoogle Scholar
- Montoya-Burgos JI, Boursot P, Galtier N: Recombination explains isochores in mammalian genomes. Trends Genet. 2003, 19: 128-130. 10.1016/S0168-9525(03)00021-0.View ArticlePubMedGoogle Scholar
- Kudla G, Helwak A, Lipinski L: Gene conversion and GC-content evolution in mammalian Hsp70. Mol Biol Evol. 2004, 21: 1438-1444. 10.1093/molbev/msh146.View ArticlePubMedGoogle Scholar
- Meunier J, Duret L: Recombination drives the evolution of GC-content in the human genome. Mol Biol Evol. 2004, 21: 984-990. 10.1093/molbev/msh070.View ArticlePubMedGoogle Scholar
- Webster MT, Smith NG, Hultin-Rosenberg L, Arndt PF, Ellegren H: Male-driven biased gene conversion governs the evolution of base composition in human alu repeats. Mol Biol Evol. 2005, 22: 1468-1474. 10.1093/molbev/msi136.View ArticlePubMedGoogle Scholar
- Spencer CC, Deloukas P, Hunt S, Mullikin J, Myers S, Silverman B, Donnelly P, Bentley D, McVean G: The influence of recombination on human genetic diversity. PLoS Genet. 2006, 2: e148-10.1371/journal.pgen.0020148.PubMed CentralView ArticlePubMedGoogle Scholar
- Vinogradov AE: Bendable genes of warm-blooded vertebrates. Mol Biol Evol. 2001, 18: 2195-2200.View ArticlePubMedGoogle Scholar
- Vinogradov AE: Isochores and tissue-specificity. Nucleic Acids Res. 2003, 31: 5212-5220. 10.1093/nar/gkg699.PubMed CentralView ArticlePubMedGoogle Scholar
- Bernardi G: Structural and evolutionary genomics. Natural Selection in Genome Evolution. 2004, Amsterdam: ElsevierGoogle Scholar
- Vinogradov AE: Noncoding DNA, isochores and gene expression: nucleosome formation potential. Nucleic Acids Res. 2005, 33: 559-563. 10.1093/nar/gki184.PubMed CentralView ArticlePubMedGoogle Scholar
- Vinogradov AE: Dualism of gene GC content and CpG pattern in regard to expression in the human genome: magnitude versus breadth. Trends Genet. 2005, 21: 639-643. 10.1016/j.tig.2005.09.002.View ArticlePubMedGoogle Scholar
- Costantini M, Clay O, Auletta F, Bernardi G: An isochore map of human chromosomes. Genome Res. 2006, 16: 536-541. 10.1101/gr.4910606.PubMed CentralView ArticlePubMedGoogle Scholar
- Hiratani I, Leskovar A, Gilbert DM: Differentiation-induced replication-timing changes are restricted to AT-rich/long interspersed nuclear element (LINE)-rich isochores. Proc Natl Acad Sci USA. 2004, 101: 16861-16866. 10.1073/pnas.0406687101.PubMed CentralView ArticlePubMedGoogle Scholar
- Ren L, Gao G, Zhao D, Ding M, Luo J, Deng H: Developmental stage related patterns of codon usage and genomic GC content: searching for evolutionary fingerprint by models of stem cell differentiation. Genome Biol. 2007, 8: R35-10.1186/gb-2007-8-3-r35.PubMed CentralView ArticlePubMedGoogle Scholar
- Vinogradov AE: DNA helix: the importance of being GC-rich. Nucleic Acids Res. 2003, 31: 1838-1844. 10.1093/nar/gkg296.PubMed CentralView ArticlePubMedGoogle Scholar
- Semon M, Mouchiroud D, Duret L: Relationship between gene expression and GC-content in mammals: statistical significance and biological relevance. Hum Mol Genet. 2005, 14: 421-427. 10.1093/hmg/ddi038.View ArticlePubMedGoogle Scholar
- Kudla G, Lipinski L, Caffin F, Helwak A, Zylicz M: High guanine and cytosine content increases mRNA levels in mammalian cells. PLoS Biol. 2006, 4: e180-10.1371/journal.pbio.0040180.PubMed CentralView ArticlePubMedGoogle Scholar
- The UCSC genome annotation database. [http://genome.ucsc.edu]
- Pozzoli U, Menozzi G, Comi GP, Cagliani R, Bresolin N, Sironi M: Intron size in mammals: complexity comes to terms with economy. Trends Genet. 2007, 23: 20-24. 10.1016/j.tig.2006.10.003.View ArticlePubMedGoogle Scholar
- The HapMap web site. [http://www.hapmap.org/]
- Karro JE, Yan Y, Zheng D, Zhang Z, Carriero N, Cayting P, Harrison P, Gerstein M: Pseudogene.org: a comprehensive database and comparison platform for pseudogene annotation. Nucleic Acids Res. 2007, 35: D55-60. 10.1093/nar/gkl851.PubMed CentralView ArticlePubMedGoogle Scholar
- Pseudogene.org. [http://www.pseudogene.org/]
- Thompson JD, Higgins DG, Gibson TJ: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994, 22: 4673-4680. 10.1093/nar/22.22.4673.PubMed CentralView ArticlePubMedGoogle Scholar
- Frisse L, Hudson RR, Bartoszewicz A, Wall JD, Donfack J, Di Rienzo A: Gene conversion and different population histories may explain the contrast between polymorphism and linkage disequilibrium levels. Am J Hum Genet. 2001, 69: 831-843. 10.1086/323612.PubMed CentralView ArticlePubMedGoogle Scholar
- The Seattle SNP database. [http://pga.gs.washington.edu/]
- Wang J, Song L, Grover D, Azrak S, Batzer MA, Liang P: dbRIP: a highly integrated database of retrotransposon insertion polymorphisms in humans. Hum Mutat. 2006, 27: 323-329. 10.1002/humu.20307.PubMed CentralView ArticlePubMedGoogle Scholar
- Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J: Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005, 110: 462-467. 10.1159/000084979.View ArticlePubMedGoogle Scholar
- The Repbase Update. [http://www.girinst.org/repbase/update/]
- Bray N, Pachter L: MAVID: Constrained Ancestral Alignment of Multiple Sequences. Genome Res. 14: 693-699. 10.1101/gr.1960404.Google Scholar
- Arndt PF, Hwa T: Identification and measurement of neighbor-dependent nucleotide substitution processes. Bioinformatics. 2005, 21: 2322-2328. 10.1093/bioinformatics/bti376.View ArticlePubMedGoogle Scholar
- The R project. [http://www.r-project.org/]
- Cleveland W: Robust locally weighted regression and smoothing scatterplots. J Amer Statist Assoc. 1979, 74: 829-836. 10.2307/2286407.View ArticleGoogle Scholar
- International HapMap Consortium: A haplotype map of the human genome. Nature. 2005, 437: 1299-1320. 10.1038/nature04226.View ArticleGoogle Scholar
- Duret L, Semon M, Piganeau G, Mouchiroud D, Galtier N: Vanishing GC-rich isochores in mammalian genomes. Genetics. 2002, 162: 1837-1847.PubMed CentralPubMedGoogle Scholar
- Ptak SE, Hinds DA, Koehler K, Nickel B, Patil N, Ballinger DG, Przeworski M, Frazer KA, Paabo S: Fine-scale recombination patterns differ between chimpanzees and humans. Nat Genet. 2005, 37 (4): 429-34. 10.1038/ng0405-445.View ArticlePubMedGoogle Scholar
- Winckler W, Myers SR, Richter DJ, Onofrio RC, McDonald GJ, Bontrop RE, McVean GA, Gabriel SB, Reich D, Donnelly P, Altshuler D: Comparison of fine-scale recombination rates in humans and chimpanzees. Science. 2005, 308: 107-111. 10.1126/science.1105322.View ArticlePubMedGoogle Scholar
- Lercher MJ, Urrutia AO, Pavlicek A, Hurst LD: A unification of mosaic structures in the human genome. Hum Mol Genet. 2003, 12: 2411-2415. 10.1093/hmg/ddg251.View ArticlePubMedGoogle Scholar
- Daniels GR, Deininger PL: Integration site preferences of the Alu family and similar repetitive DNA sequences. Nucleic Acids Res. 1985, 13: 8939-8954. 10.1093/nar/13.24.8939.PubMed CentralView ArticlePubMedGoogle Scholar
- Feng Q, Moran JV, Kazazian HH, Boeke JD: Human L1 retrotransposon encodes a conserved endonuclease required for retrotransposition. Cell. 1996, 87: 905-916. 10.1016/S0092-8674(00)81997-2.View ArticlePubMedGoogle Scholar
- Medstrand P, van de Lagemaat LN, Mager DL: Retroelement distributions in the human genome: variations associated with age and proximity to genes. Genome Res. 2002, 12: 1483-1495. 10.1101/gr.388902.PubMed CentralView ArticlePubMedGoogle Scholar
- Oliver JL, Carpena P, Hackenberg M, Bernaola-Galván P: IsoFinder: computational prediction of isochores in genome sequences. Nucleic Acids Res. 2004, 32: W287-92. 10.1093/nar/gkh399.PubMed CentralView ArticlePubMedGoogle Scholar
- Belle EM, Eyre-Walker A: A test of whether selection maintains isochores using sites polymorphic for Alu and L1 element insertions. Genetics. 2002, 160: 815-817.PubMed CentralPubMedGoogle Scholar
- Cordaux R, Lee J, Dinoso L, Batzer MA: Recently integrated Alu retrotransposons are essentially neutral residents of the human genome. Gene. 2006, 373: 138-144. 10.1016/j.gene.2006.01.020.View ArticlePubMedGoogle Scholar
- Lercher MJ, Hurst LD: Human SNP variability and mutation rate are higher in regions of high recombination. Trends Genet. 2002, 18: 337-340. 10.1016/S0168-9525(02)02669-0.View ArticlePubMedGoogle Scholar
- Webster MT, Smith NG, Ellegren H: Compositional evolution of noncoding DNA in the human and chimpanzee genomes. Mol Biol Evol. 2003, 20: 278-286. 10.1093/molbev/msg037.View ArticlePubMedGoogle Scholar
- Vinogradov AE: "Genome design" model: evidence from conserved intronic sequence in human-mouse comparison. Genome Res. 2006, 16: 347-54. 10.1101/gr.4318206.PubMed CentralView ArticlePubMedGoogle Scholar
- Jeffreys AJ, Neumann R: Factors influencing recombination frequency and distribution in a human meiotic crossover hotspot. Hum Mol Genet. 2005, 14: 2277-2287. 10.1093/hmg/ddi232.View ArticlePubMedGoogle Scholar
- Jeffreys AJ, May CA: Intense and highly localized gene conversion activity in human meiotic crossover hot spots. Nat Genet. 2004, 36: 151-156. 10.1038/ng1287.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.