methylation [14]. In the context of model systems, human along with mice and rat (Rattus norvegicus) forms a triumvirate which has been extensively used to study various aspects of mammalian biology [15]. However, although a number of human pathologies have been probed in terms of its epigenetic component using mice and human, reports based on rat are barely handful [6,16–18]. For diseases like cancer, cardiovascular diseases, neurological disor- ders, etc where rat is the primary animal model to investigate physiological alterations, a high resolution DNA methylation map is necessary to understand their regulation at molecular level [15]. Moving a step closer to addressing some vital questions to understand the basic methylome structure, we have for the first time generated a high resolution methylome of a rat liver. Analysis of the rat liver methylome revealed low methylation around transcription start sites (TSS) and high methylation at exons, which is in agreement with previously reported observa- tions in other model systems [19,20]. We observed that although CpG islands in general had low methylation, some of these islands located mainly within exons were methylated. We also observed that intron/exon boundaries had a distinctive methyl- ation pattern with the terminal exons (UTR’s) being lowly methylated. However, if even part of these terminal exons contained protein coding region, they were found to be methylated. Similarly, we observed DNA methylation marks on the coding exons and at introns that are classified in intron retention category of alternate splicing. High methylation at introns predicted their inclusion in transcript as validated using Reverse transcriptase PCR (RT-PCR). Our findings support the notion that DNA methylation, either independently or in conjunction with other components of the epigenome, might play an important role in alternate splicing. Results We generated MeDIP libraries enriched for methylated fraction of the rat liver genome by using Illumina’s sequencing protocol that was modified according to Down et al [8]. The efficiency of enrichment was checked by real-time quantitative PCR using known imprinted regions and regions lacking CG sites as control for methylated and unmethylated regions respectively. We achieved a significant enrichment efficiency of ,30–250 folds as shown by an E value of 4.93 to 8.91 (Table S1). The MeDIP libraries were sequenced on Illumina Genome Analyzer (GAIIx) to generate the first high resolution methylome map of rat liver. We used Mapping and Assembly with Qualities (MAQ) algorithm to assemble the reads onto the reference genome (rn4) downloaded from UCSC Genome Bioinformatics and obtained ,120 million pass filter reads [21,22]. From this, we generated 264,454 methylation peak summits using Model-based analysis of ChIP-Seq (MACS) (Excel S1, Figure S1) [23]. Saturation curves showing the depth of sequencing were plotted by a systematic data reduction approach using MACS which confirmed that we had generated sufficient data to cover the whole genome (Table S2; Figure S2). The ,4.3 GB of sequence data generated in our study was comparable to earlier reports where ,1.4 GB of sequence data (in humans) was shown to provide sufficient genome coverage in a MeDIP-Seq experiment [24]. Our data showed high degree of concordance between two replicates as evident from Figure S3. The correlation of methylation peaks (r = 0.84) obtained in our study was similar to that reported in other studies [25]. We independently validated the results obtained in MeDIP-Seq by randomly sequencing a few methylated and unmethylated regions after bisulfite conversion and cloning (Figure S4). First High Resolution Rat Methylome DNA methylation framework of the Rattus norvegicus genome Individual chromosomal distribution of methylation as a function of GC percentage, RefSeq genes (16,908) and CpG Islands (15,302) in rat genome (data downloaded from UCSC Genome Bioinformatics) is shown in File S1 and representative figures for three chromosomes (1,2 and 3) are shown in Figure 1. To ascertain relative methylation in each bin, we performed a detailed analysis by calculating the ratio of methylated peaks located in a particular class (like exons, introns, promoters, repeats, etc) to the total area of that class in the genome (Figure 2A). As repeats occupy a major portion of the mammalian genome we looked at the methylome architecture in context of different repeat classes and found that they account for half (53.3%) of total methylation peak summits encompassing whole genome [26]. We observed differential methylation of repeat elements with high methylation in simple repeats (41%), DNA repeat elements (20%) and low complexity repeats (15%) (Figure 2B). Within the gene body, exons showed higher methylation than introns and UTRs. The average methylation of promoters was found to be the lowest amongst all the classes in the gene body (Figure 2 Figure S5). Class distribution of CpG Islands based on methylation CpG islands (CGI) are pivotal foci for epigenetic modulations, generally believed to be unmethylated, except for the islands located at the genomic imprinting loci and those present on the inactivated X chromosome [3]. However, recent evidences point to the fact that some of the CpG islands may be methylated [27,28]. We found that of the 15809 CpG islands reported in the UCSC genome bioinformatics for rat, about 6.4% (n = 1020) were methylated (Figure 3). We then categorized both methylated and unmethylated CGIs based on their size (Table S3) and queried the numbers of CGI in each class. We found that a large proportion (48%) of methylated CGI were in the size range of 200–300 bases and the number of CGIs decreased with increase in the size of the islands. Further, we found that methylated CGIs were enriched in exons compared to other classes (37%), while unmethylated ones were mainly present in the promoters followed by exons (Figure 3). Promoter and Gene Body Methylation Regulations of genes are known to be affected by methylation in the promoter or in the gene body [29,30]. As expected, the average methylation pattern at Transcription start site (TSS) in RefSeq genes showed a V shaped curve indicative of low methylation levels at the TSS (Figure 4A). Since all the RefSeq genes are not expressed in a particular tissue, we looked at the methylation pattern around the TSS of highly and lowly expressed genes. To analyze this, we downloaded the data for the genes expressed in liver from a microarray study (GSE19830) [31]. Genes that had expression levels greater or lower than one standard deviation from the mean were considered to be highly or lowly expressed respectively. On plotting the methylation density, we found that the highly expressed genes showed a typical V shape curve at the TSS while lowly expressed ones did not show any such pattern (Figure 4B). To further validate these findings, we employed a high throughput proteomics approach (GeLC-MS followed by mass spectrometry using Orbitrap LTQ) and applying stringent criteria [95% false discovery rate (FDR), with two peptide or one unique peptide hit], we obtained 494 high confidence proteins expressed in the same liver tissue (Excel S2). These expressed proteins also exhibited low level of methylation at their proximal promoter regions (2 kb upstream of TSS) similar to the highly expressed genes. This suggests PLoS ONE | www.plosone.org 2 February 2012 | Volume 7 | Issue 2 | e31621