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Profiling Genome of Tibetan Chicken

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Profiling the genome-wide DNAmethylation pattern of Tibetan chicken using whole genome bisulfite sequencing


Background: Tibetan chickens living at high altitudes show specific adaptations to high-altitude conditions, but the epigenetic modification bases of these adaptations haven't been characterized.

Results: We investigated the genome-wide DNA methylation patterns in Tibetan chicken blood using whole genome bisulfite sequencing (WGBS). Generally, Tibetan chicken exhibited analogous methylation pattern with that of lowland chiken. A total of 3.92% of genomic cytosines were methylcytosines, and 51.22% of cytosines in CG contexts were methylated which was less than those in lowland chicken (55.69%). Moreover, the base next to methylcytosine of mCHG in Tibetan chicken had a preference for T, which was different from that in lowland chicken. In Tibetan chicken, the methylation levels in the promoter were relatively low, while the gene body maintained hypomethylated. DNA methylation levels in upstream regions of the transcription start site (TSS) of geneshad a negative relationship with the gene expression level, and the DNA methylation of gene-body were also negatively related to gene expression.

Conclusions: We firstly generated the genome-wide DNA methylation patterns in Tibetan chicken, and our results will be helpful for future epigenetic studies in adaptations to high-altitude conditions and provide a new idea for the prevention and treatment of mountain sickness and other hypoxia-related diseases to human.

Keywords: Epigenetics, DNA methylation, MethylC-Seq, highland chicken, adaptation, extreme environment.   


DNA methylation is a crucial epigenetic modification that plays a vital role in genomic imprinting [1], transcriptional repression [2], and chromatin activation [3]. In recent years, we have gained knowledge on the association of DNA methylation with cellular differentiation, development, and disease, however, little information is available concerning the DNA methylation modifications under long-term extreme environment.

Environmental aspects influence through both genetic and epigenetic mechanisms [4, 5]. Several studies have tried to establish the relationship between environmental factors and DNA methylation in humans. It was reported that reduced global DNA methylation in whole blood was related to exposure to ambient air pollution at the home addresses of non adults [6]. In malignant cells, airborne benzene induce a significant decrease in the methylation of LINE-1 and AluI, and increasing airborne benzene levels can cause hypermethylation in p15 and hypomethylation in MAGE-1 [7]. The average level of methylation in p16 was increased in patients with benzene poisoning compared with control group, while no alternation was observed in the p15 methylation [8]. Korea et al. revealed that most organochlorine (OC) pesticides were inversely and significantly related to the methylation of Alu [9]. In the prenatal pregnant women, lead exposure was inversely related to genomic DNA methylation in white blood cells [10]. Moreover, base on the epigenetic inheritance mechanisms, adaptive traits that result from the environment can be transferred to the next generation. For instance, environment containing endocrine-disrupting chemicals can affect the germ line and promote disease across offspring via DNA methylation [11].

Above researchs shows that environmental conditions could induce DNA methylation alternation to to influence disease, prompting us to explore whether DNA methylation is associated with the unique adaptations of farm animals to hypoxia and high-dose ultraviolet radiation in high-altitude environments. The Tibetan chicken which lives in high-altitude environment has smaller body, lower heart rate, higher spleen rate and erythrocyte volum than low-altitude chicken. Previous research showed that humans relocating to high-altitudes might undergo acute mountain sickness, high-altitude pulmonary edema, and high-altitude cerebral edema [12]. Whereas, the Tibetan chicken is greatly adapted to the low-oxygen and high-altitude environment and displays good performance in terms of survival and has high reproduction [13]. Therefore, investigation the genome-wide DNA methylation of Tibetan chicken, understanding the effects of DNA methylation on the plateau adaptability, may provide a new idea for the prevention and treatment of mountain sickness and other hypoxia-related diseases to human.

In this study, we perform whole genome bisulfite sequencing (WGBS) on Tibetan chicken blood to analyze their global DNA methylation patterns. The DNA methylome distribution in the Tibetan chicken genome was shown for the first time. Our results will provided an important resource for exploring low-oxygen adaptation mechanism in high-altitude district.



In this study, one Tibetan chicken was obtained from Xiangcheng County in the Ganzi Tibetan Autonomous Prefecture with the living place about 3500 meters above sea level. Blood samples were collected and stored at -20 °C for bisulfite sequencing. Total genomic DNA was collected from the blood with the use of a TIANamp Genomic DNA Kit (Tiangen, Beijing, China). All experiments in this study were performed in accordance with relevant guidelines and regulations, and were approved by the Science and Technology Department of Sichuan Province.

MethylC-Seq library construction and sequencing

DNA was fragmented by sonication with a Sonicator (Sonics & Materials) to a mean size of approxi­mately 250 bp, followed by blunt ending, 3′-end addition of dA, and adapter ligation, in which Illumina methylated adapters were used according to the manufacturer's instructions (Illumina). The bisulfite conversion of Tibetan chicken DNA was carried out using ZYMO EZ DNA Methylation-Gold kit (Zymo Research, Irvine, CA, USA) and ampli­fied via PCR with 12 cycles. Ultra-high-throughput pair-end sequencing was performed by the Illumina Genetic Analyzer (GA2) on the basis of manufacturer instructions. Raw GA sequencing data were processed using Illumina base-calling pipeline (SolexaPipeline-1.0).

Data Filtering

Data filtering was performed via the elimination of the adaptor sequences, contamination and low-quality reads from raw reads. Low-quality reads consist of three types including: 1) Contain adaptor sequence; 2) N base number over 10%; 3) The number of base whose quality less than 20 over 10% was trimmed, and the read which accord with one of them will be removed. Only cleaned data were used for the downstream analyses.

Reads Alignment

On the forward read of each read pair, observed cytosines were replaced with replaced with adenines, and the observed guanines were replaced with adenines on the reverse read of each read pair. The "alignment form" reads were then mapped to the "alignment form" gallus_gallus reference genome by SOAP aligner[14]. Each hit with a single placement with a minimum number of mismatches and and a clear operation chain was defined as unambiguous alignment (uniquely mapped reads) and was used for ascertainment of methyl-cytosine. The copy numbers of the local region was estimateed by calculating the the uniquely mapped reads.

Estimating methylation levels

Methylation level was determined by dividing the number of reads covering each mC by the total reads covering that cytosine, which was also equal the mC/C ratio at each reference cytosine. The function is showed as following:

Methylation level = 100 *

GO enrichment Analysis

GO annotations of Tibetan chicken genes were downloaded from the Ensembl (ftp://ensembl.org/pub/current/otherdata/Gene_ontology/gallus_gallus_glean_gene.go). GO comparative analyses between inter­ested genes groups were performed using BGI WEGO (http://wego.genomics.org.cn/cgi-bin/wego/index.pl).

KEGG Pathway Analysis

Different genes usually interact with each other to exercise their biological functions. Kyoto Encyclopedia of Genes and Genomes(KEGG)is the main public pathway database. Super geometry analyses were conducted to find the KEGG pathways enriched in genes differentially methylated compared to the whole genome. The calculation formula is the same as that in GO function analyses, N represents number of genes with pathway annotation; For the number, n is the number of differentially expressed genes corresponding N, M represents number of all genes which have a particular pathway annotation; m represents numbers of differentially expressed genes which have a particular pathway annotation. Pathway mapped Q value ≤ 0.05 defined as the pathway of significant enrichment. Through significant enrichment of the pathway, we can determine the most main in biochemical pathways and signal transduction pathways.


Global mapping of DNA methylation.

In the present study, blood samples from a Tibetan chicken were used to generate three libraries for genome-wide methylation sequencing. All libraries showed nearly complete bisulfite conversion (99.7%). A total of 41.3 Gb raw data were obtained from three blood samples. After data filtering, 151,345,614, 165,745,108 and 141,554,972 clean reads were generated for the three libraries, respectively. Of the total reads, 75.6% were mapped to the reference genome, with 28 X Whole-genome average coverage depth, which could reveal the data quantity of clean data because of the characteristics of bisulfite sequencing (Table 1 and 2).

Cytosine patterns have 3 major types (CG, CHG and CHH, H represents non-G base, hereinafter inclusive) according to the sequence context. Therefore, we analyzed the relationships between effective sequencing depth and genome coverage for different cytosine patterns (Figure S1, S2). Figure S1 reveals that there is a negative correlation between the effective sequencing depth and the percentage of cytosine in genome. The Figure S2 shows that the distribution of genome coverage varies with sequencing depth accord with the Poisson distribution, and the depth of the distribution`s apex is near to the genome average sequencing depth.

In additon, we performed effective coverage analysis base on three different levels: chromosome, gene region and genomic feature. The effective coverage of all cytosine in each chromosome ranges from 82.77% to 97.86%, except for 24.96% in chr17 , while the CpG effective coverage of each chromosome ranges from 86.74% to 97.5%, except for 23.58% in chr17 (Table S1). Moreover,coverage of all cytosine in CDS and intron region was 95.94% and 93.66%, respectively, and CG coverage in CDS and intron region was 96.04% and 93.45%, respectively (Table S2).

DNA methylation patterns

In Tibetan chicken, the methylation level of all genomic C sites was more than 3.9%. Patterns of Cytosine methylation in Tibetan chicken were found to have three major types (mCG, mCHG and mCHH) according to the sequence context. We discovered overall genome-wide levels of 51.22% CG, 0.4% CHG, and 0.45% CHH methylation in the Tibetan chicken (Table 3). In whole genome, the CG methylation occupied over 96% of cytosine methylation, which is the primary cytosine methylation pattern. However, the rate of mCHH was only 3% and the rate of mCHG was 1%(Fig. 1A).

Methylation status of CG, CHG and CHH differ between species, even varies with different conditions concerning time, space and physiology within a single organism. Figure 1b showed that percentage of the methylation level of methyl-cytosine varies with methylation level. In the tibet chicken blood, more than 75 % of mCG sites were 60-100 % methylated (Fig. 1b). In addition, chromosome1 was used as an instance to illuminate the methyl-cytosine density distribution in chromosome, and the methyl-cytosine density showed large variations throughout the chromosome 1, which was similar to other chromosomes (Fig. 1c)

Proximal Sequence Features Analysis

To identify whether the particular local sequences were markedly enriched as the DNA methylome of Arabidopsis, we analyzed the sequence adjacent to sites of CG and non-CG methylation. The methylation ratios of all potential 9-mer sequences were calculated, and the methylated cytosine was located at the fourth position in these sequences (permitting an analysis of three bases upstream of CHG, and CHH methylation). As shown in figure 2, hardly a sequence preference was found in the CG-flanking regions of the hole genome or in the mCG-flanking regions. Moreover, the highest frequency base that next to the CHG cytosine in genome was A, followed by T and C, while the base following the mCHG methylcytosine has a preference for T, followed by A and C. In CHH context, the fifth position that proximal to the sites of cytosine has a preference for C, and the sixth position prefer to T, which is similar to the mCHH(Fig. 2).

DNA methylation levels of different functional regions

Different genomic features are associated with distinct regulation functions. To study the DNA methylation profile in different genomic features, the heat map was used to present the distribution of methylation level in the CDS, downstream, Genome, intron and upstream (fig. 3). The comparative analysis of mean DNA methylation levels revealed that different gennome regions showed distinguishing DNA methylation levels. Additionally, we analyzed DNA methylation patterns across the transcriptional units at whole genome level. In Tibet chicken, most of the promoter regions have an association with CpG islands and are hypomethylated, which showed a lower CG methylation level than the gene-body and the gene downstream. Moreover, methylation of CG declined sharply before the TSS and increased markedly towards the gene body regions and stayed at a plateau until the 3' end of the gene body, and two obvious peaks were present in the regions of the internol exon and the last exon (Fig. 3). The methylation of CHG had the same varying tendency with the methylation of CG, but was characterised by mitigatory changes compared to the rapid changes of CG methylation. Furthermore, the methylation peaks of both CG and CHG were presented in the internal and last exons in which the methylation lows of CHH appeared.

DNA methylation levels ofpromoter and genebody

Methylation of the promoter suppresses gene expression, but the functional role of gene-body DNA methylation in highly expressed genes has yet to be clarified. To better characterise the methylation of promoter and gene-body, a comprehensive analysis of methylated genes and unmethylated genes in gene-body and upstream2k was performed. In total, 14,018 genes were methylated in both promoter and gene-body, while 505 genes were exclusively methylated in promoter and 409 genes were exclusively methylated in gene-body, and 231 genes unmethylated in both promoter and gene-body (fig. 4A). Gene ontology analysis of methylated and unmethylated genes revealed the top-ranked enriched GO terms were related to the cellular process, metabolic process, and response to stimulus in the biological process (BP) category. The cellular component (CC) category mainly comprised genes involved in cell, cell part, and organelle. Within the molecular function (MF) category, binding, catalytic activity, and transporter activity were highly represented (fig. 4B and 3S). In addition, KEGG analysis showed that genebody methylation genes were clustered in the metabolic pathways, protein processing in endoplasmic reticulum, and calcium signaling pathway, while the genebody unmethylation genes were clustered in metabolic pathways, Fc gamma R-mediated phagocytosis, and endocytosis. Moreover, promoter methylation genes were most involved in ubiquitin mediated proteolysis, oocyte meiosis, and melanoma, while , promoter unmethylation genes were most involved in N-Glycan biosynthesis, Glycosylphosphatidylinositol(GPI)-anchor biosynthesis, and Fat digestion and absorption (fig. 5).

DNA methylation and gene expression level

DNA methylation of promoter generally suppress gene transcription via inducing a compact chromatin structure. We obtained the gene expression profiles of Tibetan chicken from the GEO database. Based on expression levels, all genes were divided into ten groups, from the lowest 10% and to the highest 10%. Furthermore, the genomic regions that 2 kb upstream of the TSS were defined as the proximal promoter, and used the mean methylation as the methylation level of each group. The correlation analysis showed that gene expression level was negatively related to the mean DNA methylation level of the promoter regions (fig. 6A; r=-0.93, p<0.01). Unexpectedly, we found a slightly negative correlation between the gene expression level and the gene-body methylation, though the methylation showed little difference in these ten groups with different expression level (fig. 6B; r=-0.83, p<0.01).


Genomics technologies have been extensively used to investigate the adaptations of humans, animals and plants to extreme conditions [15, 16]. However, the relationships between the adaptions and the epigenetic modifications that result from extreme environmental exposures remains to be further elucidated. To date, the methylation pattern of Tibetan chicken remains unknown. To improve our understanding of the association between epigenetic modifications andadaptations to hypoxia and high-dose ultraviolet radiation in high-altitude environments, we analyzed whole-genome single-base resolution DNA methylomes by WGBS to provide the genomewide DNA methylation patterns in Tibetan chicken blood and interrogate the potential role of DNA methylation in adaptations to high-altitude environments.

Genome-wide DNA methylations of lowland chickens have been researched using MeDIP-seq [17, 18], MBD-Seq [19], and Methyl-MAPS [20], which measure methylation base on immunoprecipitation and restriction enzyme digestion. Compared to WGBS, these technologies generate lower resolution and coverage, and fail to obtain methylation level for CHG and CHH. For example, Only 32 % of CpG coverage was obtained from the study of lowland chicken using Methyl-MAPS [20]. In the other lowland chicken study, the CpG coverage ranges from 83.72 to 91.57 % using MethylC-seq [21]. In the current study, the CpG effective coverage of each chromosome ranges from 86.74% to 97.5%, except for 23.58% CpG coverage of chr17 in Tibet chicken.

In lowland chicken, more than 55.69% of cytosines in CG contexts were methylated which is much higher than those in Tibet chicken (51.22%), while the percentage of mCHG and mCHH in Tibet chicken was higher than those in lowland chicken. In addition, 96.24 %, 0.86 % and 2.89 % of all methylcytosines were present in the CG CHG and CHH context, respectively, while the CG methylation in Tibet chicken occupied only 96% of cytosine methylation. Moreover, the base next to methylcytosine of mCHG in lowland chicken had a preference for A, while that in highland chicken prefer to T. All these indicated that the highland environments decrease the global CG methylation levels of chicken, and change the sequence context preferences for methylation, suggesting that the methylation involve in the adaptations of chicken to high-altitude environments.

In Tibetan chicken genome, the DNA methylation level rapidly down before the TSS and markedly increased towards the gene body regions and stayed at a plateau until the 3' end of the gene body. These methylation features discovered in this study consistently match with those previously reported in bovine placentas [22]. Similar to the lowland chickengenome, the Tibetan chicken genome has two CG methylation peaks in the internal and last exons, but the difference is that the lowland chicken genome showed a mitigatory methylation level in the genome regions before the TSS [21], suggesting that the long-term hypoxia and UV radiation under high-altitude conditions cause methylation alternation.

The promoter plays a crucial role in the regulation of gene transcription and most of the promoter regions are hypomethylated [23], while the gene-body DNA methylation is associated with chromatin structure and elongation efficiency, and prevents spurious transcription initiation [24, 25]. In present study, we found the promoter is hypomethylated, whereas the methylation level in gene-body is relatively high, a finding that is similar to those from previously reported in human [26] and lowland chicken [17]. Hypermethylation of the promoters represses gene transcription [27], and the reduction of methylation at the promoters causes gene overexpression [28]. In human embryonic stem cells, Laurent et al. reported that 20% of the most highly expressed genes displayed the lowest methylation levels in promoter. We analyse the relationship between the methylation and the expression inTibetan chicken, using the method reported in previous studies [17]. Similar to reports in humans [17, 29, 30] and lowland chicken [5], DNA methylation level in 2 kb upstream of genes is negatively related to the gene expression level in Tibetan chicken, this was further evidence that DNA methylation at the promoters is involved in gene silencing.

Methylation in gene-body is more prevalent than in promoter, but the role of gene-body methylation in gene regulation remains unclear. Previous researchs showed that gene-body methylation has an intricate correlation with expression level. Most researchers believed that the methylation of gene-body is positively correlated with gene expression [26, 29, 31, 32], although several researchers have indicated that intragenic methylation might inhibit gene transcription [24]. However, the correlation between gene-body methylation and expression levels in bovine placentas is non-monotonic and the moderately expressed genes show the highest methylation in gene-body [22]. Our data demonstrated that methylation in the gene-body of Tibetan chicken may decrease gene expression. However, methylation in gene-body is just one of the thousands of factors that affect gene transcription. Therefore, further studies centering on the DNA methylation of certain regions that display distinct effect in gene regulation are needed to clarify the complicated epigenetic mechanism underlying high-altitude environments and its relationships with adaptations to hypoxia and high-dose ultraviolet radiation in high-altitude environments.

In summary, the present study provides the first comprehensive analysis of genome-wide DNA methylation patterns in the blood of highland chicken, and our results can be used for future studies on epigenetic gene regulation in highland chicken. This study contributes to the knowledge on epigenetics in highland animals.


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Figure legends

Fig. 1 Global profile of DNA methylation in the Tibet chicken. A Proportion of different methyl-cytosine patterns in Tbet chicken blood. B Distribution of Methylation Level for CG, CHG and CHH. The y axis means the fraction of all mCs while the x axis represents methylation level of methyl-cytosine. Cmethyl-cytosine density distribution. Blue dots indicate methyl-cytosine density in 10-kb windows throughout chromosome. Smoothed lines represent the mC density

Fig. 2 CG and Non-CG proximal sequence features. Logo plots of the sequences proximal to sites of CG, CHG and CHH DNA methylation in each sequence context.

Fig. 3Average methylation level in different gene regions. Each dot denotes the mean methylation level per bin and the respective lines denote the 5-bin moving average.

Fig. 4 Distinguish between methylated and unmethylated genes. A The Number of Methylated Genes and Unmethylated Genes. B GO enrichment analysis for upstream methylation genes and gene-body methylaiton genes. The x axis indicates GO items, the left y axis indicates the proportion of genes involved, and the right y axis indicates the exact number of genes.

Fig. 5 Pathways assignment based on KEGG. A Pathways analysis of gene-body methylation genes. BPathways analysis of gene-body unmethylation genes. C Pathways analysis of upstream methylation genes. D Pathways analysis of upstream unmethylation genes.

Fig. 6 Relationship between DNA methylation and gene expression level in Tet chicken. All genes were divided into ten groups according to expression levels, from the lowest 10% and to the highest 10%. Each point represents the mean expression level and the relative methylation level. A Relationship between promoter DNA methylation and gene expression level. B Relationship between gene-body DNA methylation and gene expression level.


Table 1 Statistics of Data Production



Insert Size (bp)

Conversion rate (%)

Read length (bp)

Clean Reads

Clean data (Gbp)






















Table 2 Reads Alignment


Raw reads (M)

Raw data (Gb)

Mapped reads(M)

Mapped data (Gb)

Average map rate (%)

Whole genome average coverage depth(X)









Table 3 Average Methylation Level for C, CG, CHG and CHH











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