Protection of CpG islands against de novo DNA methylation during oogenesis is associated with the recognition site of E2f1 and E2f2
© Saadeh and Schulz; licensee BioMed Central Ltd. 2014
Received: 18 July 2014
Accepted: 19 September 2014
Published: 21 October 2014
Epigenetic reprogramming during early mammalian embryonic and germ cell development is a genome-wide process. CpG islands (CGIs), central to the regulation of mammalian gene expression, are exceptional in terms of whether, when and how they are affected by epigenetic reprogramming.
We investigated the DNA sequences of CGIs in the context of genome-wide data on DNA methylation and transcription during oogenesis and early embryogenesis to identify signals associated with methylation establishment and protection from de novo methylation in oocytes and associated with post-fertilisation methylation maintenance. We find no evidence for a characteristic DNA sequence motif in oocyte-methylated CGIs. Neither do we find evidence for a general role of regular CpG spacing in methylation establishment at CGIs in oocytes. In contrast, the resistance of most CGIs to de novo methylation during oogenesis is associated with the motif CGCGC, the recognition site of E2f1 and E2f2, transcription factors highly expressed specifically in oocytes. This association is independent of prominent known hypomethylation-associated factors: CGI promoter activity, H3K4me3, Cfp1 binding or R-loop formation potential.
Our results support a DNA sequence-independent and transcription-driven model of de novo CGI methylation during oogenesis. In contrast, our results for CGIs that remain unmethylated are consistent with a model of protection from methylation involving sequence recognition by DNA-binding proteins, E2f1 and E2f2 being probable candidates.
KeywordsEpigenetic reprogramming CpG island gene expression DNA methylation oogenesis genomic imprinting chromatin remodelling
Epigenetics encompass reversible biochemical modifications of DNA and chromatin that do not change the underlying DNA sequence but influence its interpretation by the cellular machinery, particularly with respect to gene expression. Epigenetic modifications are heritable across cell divisions so that cell type identity can be maintained . On the other hand, alterations of epigenetic modifications are at the heart of cell lineage choices during differentiation and, thus, are critical in organism development . DNA methylation (5-methyl-cytosine; 5mC) and post-translational modifications of histone tail residues are the epigenetic modifications most immediately linked to the control of mammalian gene expression, and their genome-wide profiles form cell type-specific combinatorial patterns .
Epigenetic reprogramming is required to generate the totipotent zygote, able to generate all embryonic and extra-embryonic cell types . This is achieved by the erasure of epigenetic marks, DNA methylation in particular, on the genomes contributed by the germ cells and the subsequent establishment of new baseline marks [2, 3]. Epigenetic reprogramming is also required during gametogenesis to uniformly set up the same, sex-specific epigenetic profile across all germ cell genomes, irrespective of their parental origin .
In the germ line, DNA methylation and in particular parental genomic imprints are reset so that the genomes of mature gametes epigenetically reflect the sex of the individual. First, DNA methylation is actively removed from the genomes of primordial germ cells while they migrate to and colonise the genital ridge [6, 7], a process that in the mouse completes at E13.5. The genomes of the developing germ cells are subsequently de novo re-methylated, a phase that in the male germ line is already complete prior to birth, while mouse oocytes do not undergo this process until after birth and only during their final growth phase, with de novo methylation completing between the germinal vesicle and meiosis II-arrested stages  (Figure 1).
The changes in DNA methylation levels during the periods of epigenetic reprogramming outlined above are overall genome-wide trends. Not all genomic regions follow these trends. An important exception are CpG islands (CGIs), CpG dinucleotide-dense regions between a few hundred and a few thousand nucleotides in length that are found at approximately 70% of mammalian gene promoters and play a central role in the regulation of gene expression . The majority of the approximately 23,000 CGIs in the mouse genome identified by CAP-seq  are unmethylated, resisting de novo DNA methylation at all times, in contrast to most of the rest of the genome  (Figure 1). However, in the female germ line, approximately 1,600 CGIs (by extrapolation from the observed fraction of CGIs) acquire methylation during oogenesis [12, 13]. Almost all of these oocyte-methylated CGIs remain unmethylated during spermatogenesis, that is, they are maternal germ line differentially methylated regions (maternal gDMRs). Almost all maternal gDMRs are transient in that maternal allele-specific methylation is lost during post-fertilisation reprogramming. However, there is yet another exceptional subset of 28 CGIs that are part of permanent maternal gDMRs, that is, those that are protected from post-fertilisation reprogramming and persist up to at least E8.5 . Among these are almost all of the maternally methylated imprinting control regions (ICRs) that regulate imprinted, parental allele-specific gene expression .
DNA sequence features are known to play a role in epigenetic reprogramming [15, 16]. We therefore hypothesised that the DNA sequences of the CGIs that acquire DNA methylation in the oocyte contain characteristic DNA sequence features. Using an ab initio DNA sequence motif discovery approach , we replicate and elaborate the previously experimentally determined highly specific association between the above permanent maternal gDMRs and the TGCCGC motif involved in their protection from post- fertilisation reprogramming . The same, thus validated, approach fails to uncover a DNA sequence motif that is characteristic for oocyte-methylated CGIs in general. Those CGIs also do not exhibit a periodic pattern in the spacing of their CpGs, previously suggested to be involved in the targeting of de novo DNA methyltransferases . In contrast, our ab initio approach identifies the CGCGC motif as a novel characteristic feature of those CGIs that are protected from de novo methylation during oogenesis. We show that high CpG density cannot explain this finding. Furthermore, the association of the CGCGC motif with the absence of DNA methylation at CGIs in the oocyte is independent of other factors, such as being an active promoter in the oocyte or having R-loop formation potential. We find that CGCGC is the recognition site of E2f1 and E2f2, transcription factors that, together with co-factors involved in chromatin remodelling, are highly expressed specifically in oocytes.
Results and discussion
An ab initio motif search identifies the Zfp57/Kap1 recognition site as a characteristic feature of permanent maternal gDMRs
In terms of significance, six orders of magnitude separated TGCCGC from the next most significantly enriched distinct motif. We used this property of the E-value distribution in the case of TGCCGC as a benchmark in our subsequent ab initio motif searches. Specifically, we considered a motif to be characteristic of a sequence set if and only if it was reported as statistically significant (E <0.05) and constituted an extreme outlier in terms of significance compared to all other reported distinct motifs. Thus, a motif being characteristic implies high relative merit against the background of all reported motifs and, hence, increases the likelihood that the motif is a true positive finding. Reliance on only the significance metric reported by an ab initio method (for example, the DREME E-value) is prone to bias since the overall scale of the metric can vary widely depending, in a non-trivial manner, on variables like the input set sizes, sequence lengths and (di)nucleotide composition. The notion of a characteristic motif is generalisable to a characteristic (small) subset of reported motifs that all are extreme outliers in terms of significance. Such a subset comprising clearly distinct motifs could indicate the coordinate binding of multiple factors. However, in our experiments below, we did not encounter such subsets.
While being characteristic of DMR CGIs, TGCCGC is however still present in approximately 40% of the other oocyte-methylated CGIs so that the mere presence of TGCCGC is insufficient for protection from post-fertilisation demethylation (Figure 2.C), implying that other factors contribute to this mechanism. TGCCGC being the only characteristic motif renders unlikely that another DNA-binding protein (complex) with high specificity for a distinct recognition site is also involved. However, we observed that the enrichment of TGCCGC is not only due to a relatively large fraction of DMR CGIs in which it is present, but also due to a high density of occurrences (approximately 4 per 1 kbp; Figure 2.D) relative to other CGI groups. Consistent with the observations in , we find all DMR CGIs apart from Slc38a4 harbour at least two instances of TGCCGC (there are two TGCCGC sites within 1 kbp of the Slc38a4 gDMR). Overall, gDMR size is moderately correlated with the number of TGCCGC instances (r2 = 0.33; [see Additional file 1: Table S1]). This suggests that multi-occupancy by the Zfp57/Kap1 protein complex may be required for the permanent protection of a region.
Oocyte-methylated CpG islands and promoters upstream do not harbour characteristic DNA sequence motifs
We hypothesised that the subset of CGIs that become methylated during oogenesis, unlike the vast majority of CGIs, may harbour a characteristic DNA sequence motif that would presumably have a role in targeting the de novo Dnmt3a/Dnmt3l DNA methylation complex. The same ab initio DNA sequence motif discovery approach as above did not identify any significantly enriched motifs and, hence, no characteristic motif in oocyte-methylated CGIs relative to oocyte-unmethylated CGIs. Given the association of CGI shores with tissue-specific methylation , we extended our search to CGIs including +/- 1 kbp of flanking sequence, which yielded sets of nominally significant motifs [see Additional file 2: Figure S1.A]. However, none of the motifs was characteristic as defined above. Moreover, the reported motifs were rich in TpG/CpA dinucleotides and devoid of CpGs [see Additional file 2: Figure S1.A], consistent with sequence evolution of oocyte-methylated CGIs driven by the high mutation rate of 5mC to T due to deamination (approximately tenfold greater rate than for any other substitution mutation) . The results were essentially the same for CGIs including +/- 2 kbp of flanking sequence (data not shown), except that the number of reported motifs increased roughly twofold, and their significance values uniformly were an order of magnitude smaller. Hence, while the overall dinucleotide composition of the reported motifs likely reflects a genuine biological process, individually, each is unlikely to be a recognition sequence. The increases in the number of reported motifs and the simultaneously uniformly decreasing significance values upon including additional flanking sequence illustrate the above mentioned issue of bias in the significance metric when comparing two sets of sequences that increasingly and systematically differ in their dinucleotide composition.
In the absence of a characteristic motif within the oocyte-methylated CGIs themselves, we next examined the sequences of oocyte-active promoters whose transcripts extend across downstream oocyte-methylated CGIs. A detailed study of the Gnas locus demonstrated that transcription through the CGIs associated with the Nespas and Gnas_exon1a permanent maternal gDMRs is necessary for them to gain DNA methylation during oogenesis . More recently, a significant positive association between CGI methylation in the oocyte and CGIs being intragenic relative to oocyte-expressed transcripts was observed in genome-wide data . We therefore hypothesised that the oocyte-active promoters from which these transcripts originate may contain a characteristic sequence motif that is sufficient for the activity of these promoters, thus ensuring the methylation of the downstream CGI.
To avoid false positive results, we employed strict criteria to identify oocyte-active promoters from oocyte RNA-seq, BS-seq, RRBS-seq and H3K4me3 ChIP-seq data [12, 13]. Briefly, transcripts were reconstructed using the Tuxedo protocol , and the region +/- 1 kbp around the TSS of a transcript was considered a promoter if it overlapped an unmethylated CGI and was enriched for H3K4me3. This set of promoter sequences was then split according to whether or not transcripts originating from the respective promoter contained an oocyte-methylated CGI. An ab initio motif search analogous to the above, comparing the two promoter sets (with a downstream oocyte-methylated CGI: n = 103; without: n = 2,017) did not identify any significantly enriched motifs. We then systematically expanded the promoter sequences to include +/- 2 kbp, +/- 4 kbp and +/- 5 kbp of sequence flanking the TSS, yielding three, six and eight nominally significant motifs, none of which was characteristic as defined above [see Additional file 2: Figure S1.B].
We note that the observed lack of characteristic sequence motifs in oocyte-methylated CGIs or promoters upstream does not rule out less parsimonious sequence-based models of DNA methylation establishment. For example, each of multiple distinct combinations of sequence motifs may be sufficient to induce DNA methylation. In theory, none of the individual combinations needs to form a characteristic set of motifs and, hence, all could evade detection by our ab initio approach.
CpGs in oocyte-methylated CpG islands are not characteristically spaced
The smaller proportion (19%) of oocyte-methylated CGIs enriched for pairs of CpGs in relatively close proximity (8 to 10 bp and 12 to 14 bp) compared to unmethylated CGIs (39 to 43%) is consistent with the high mutation rate of 5mC  that over evolutionary time spans is expected to lead to lower CpG density. This is supported by the TG/CA-rich motifs identified above in oocyte-methylated relative to unmethylated CGIs including shores [see Additional file 2: Figure S1]. When we included CGI shores in the analysis of CpG pairs at 8 to 10 bp, the gap between oocyte-methylated and unmethylated CGIs became more pronounced [see Additional file 2: Figure S2], suggesting that the rate of CpG depletion is higher in the shores than in the cores of methylated CGIs. In vitro, Dnmt3a/l preferentially methylates CpG pairs at 8 to 10 bp . Our results provide no evidence that CpG pairs at 8 to 10 bp are preferentially depleted in oocyte-methylated CGIs; that is, the in vitro preference of Dnmt3a/l is not obviously reflected in the sequence evolution of those CGIs.
The 28 DMR CGIs on average exhibit an approximately 9-bp period in CpG spacing and are enriched for CpG pairs at 8 to 10 bp relative to pairs at 12 to 14 bp (Figures 3 and 4). Individually however, they exhibit considerable variability with respect to the existence of periodic CpG spacing, as well as the lengths of the present periods, irrespective of the method used to assess periodicity [see Additional file 3: Supplementary Results and Methods; Additional file 1: Table S2; Additional file 2: Figures S3-S7]. This lack of consistency, even among CGIs belonging to the same permanent maternal gDMR, does not support a general involvement of periodic CpG spacing in targeting Dnmt3a/l to these regions.
Oocyte-methylated CGIs that are not DMR CGIs (the vast majority) lack periodic patterns in their average obs/exp ratios (Figures 3) and are equally depleted in CpG pairs at 8 to 10 bp and 12 to 14 bp (Figure 4). We conclude that regular CpG spacing, particularly with a period of 8 to 10 bp, is not associated with DNA methylation establishment by Dnmt3a/l at CGIs in the oocyte. Together with the lack of a characteristic DNA sequence motif in non-DMR oocyte-methylated CGIs, our results support a sequence-independent model of de novo DNA methylation during oogenesis.
The CGCGC DNA sequence motif is a characteristic feature of unmethylated CpG islands in the oocyte
The pattern of CGCGC motif density values across the different CGI categories is very similar to the pattern for the CpG dinucleotide [see Additional file 2: Figure S8.B], which raised the question of whether the motif occurrences are a simple consequence of the greater CpG density of unmethylated CGIs. To test this hypothesis, we shuffled the sequences of the unmethylated CGIs, while maintaining dinucleotide frequencies as above , and subsequently determined the occurrences of the motif. We observed a approximately 45% reduction in the number of motif occurrences, almost doubling the number of unmethylated CGIs without a motif occurrence and reducing the overall density of occurrences by 38% [see Additional file 2: Figure S8.C-E]. This rules out the globally high CpG density of unmethylated CGIs as the cause of the motif occurrences. However, CpGs are typically not uniformly distributed within a CGI, so the local CpG density varies within a CGI. Thus, locally high CpG density may explain the motif occurrences, or at least a large fraction of them. To test this possibility, we determined the distribution of the number of motif occurrences as a function of local CpG content and compared the distribution obtained for the unmethylated CGIs with the distribution for their shuffled counterparts [see Additional file 2: Figure S9]. Only approximately 5% of the motif occurrences in unmethylated CGIs can be explained by high local CpG density. In conclusion, repetitive sequence elements and high CpG density are unlikely explanations for the enrichment of the CGCGC motif in unmethylated CGIs.
The significant association of the CGCGC motif with unmethylated CpG islands in the oocyte is independent of other, known hypomethylation-associated factors
Previous work by others has associated the typical lack of DNA methylation at CGIs with several factors. Promoter activity of a CGI is generally thought to be incompatible with CGI methylation [9, 26]. R-loop formation potential of a CGI has been reported as a distinguishing feature of unmethylated CGIs in human embryonic stem cells (ESCs) . In mouse ESCs, fibroblasts and brain, the binding of Cfp1 to CGIs is associated with hypomethylation via recruitment of the Set1 histone methyltransferase complex that deposits H3K4me3 [28, 29]. H3K4me3 in turn inhibits the Dnmt3a/l complex and, thus, is directly associated with hypomethylation [30, 31]. Here, we investigated these factors together with the CGCGC motif as a novel fifth factor to estimate the relative strengths of their association with hypo-methylation of CGIs in the oocyte, and their inter-dependencies.
We took a logistic linear regression approach, modelling the binary methylation state (either methylated or unmethylated) of 8,567 CGIs in the oocyte as linear combinations of subsets of binary factors and interaction terms. We refer to the five factors as PA, Rloop, Cfp1, H3K4me3 and Motif, and they are defined as follows: the CGI is/is not an active promoter in the oocyte (PA), the CGI has/does not have R-loop formation potential (Rloop), the CGI is/is not bound by Cfp1 in mouse whole brain tissue (Cfp1), the CGI is/is not enriched for H3K4me3 in the oocyte (H3K4me3), and the CGI does/does not contain the CGCGC motif (Motif). We note that R-loop formation potential, as opposed to actual R-loop formation, is a DNA sequence- and oocyte transcriptome-derived feature, termed G-skew in , that is, more G than C residues in the transcribed strand of a CGI. Thus, apart from Cfp1, all factors incorporate cell type-independent sequence and/or oocyte-specific experimental data. The set of factors was non-redundant since pairwise correlation between factors did not exceed 0.62 and typically was <0.3 [see Additional file 1: Table S3]. The values for all factors for all CGIs are part of Additional file 4 and Additional file 5 (‘Un-methylated associated Factor’ spreadsheet).
First, we determined which of the five factors in isolation have significant predictive value (reduction in model deviance) in terms of predicting the methylation state of a CGI in the oocyte. All factors had significant predictive value. In terms of effect size (reduction in deviance), the CGCGC motif was second after H3K4me3, and R-loop formation potential was a distant last [see Additional file 1: Table S4]. This is in agreement with the observed levels of correlation between the methylation state and each of the factors [see Additional file 1: Table S3].
We next tested whether the addition of the Motif factor to a model comprising one of the other factors significantly improved model fit and, hence, whether the CGCGC motif conveys significant additional, independent power to predict the methylation state. We found this to be true for all pairwise combinations of the Motif factor with one of the other factors [see Additional file 1: Table S4; Additional file 2: Figure S10.B]. This suggests that the presence of the CGCGC motif in a CGI independently confers additional protection from DNA methylation in the oocyte, in particular independent of promoter activity.
Finally, we tested each pair of factors for significant interaction, that is, a significant increase in the predictive value of the model upon the addition of an interaction term to the model composed of the two factors (Figure 7.B). The most significant interaction with by far the largest effect size occurs between promoter-association and R-loop formation potential [see Additional file 1: Table S5], consistent with promoter activity being required to realise actual R-loop formation at CGIs that have the potential to do so, and consequently, significant extra protection from DNA methylation above and beyond the effect of R-loop potential or promoter-association alone. The effect sizes of all other significant pair-wise interactions between factors were relatively small. That includes the only significant interaction between the CGCGC motif and another factor, namely, Cfp1 binding.
The CGCGC motif matches the recognition site of E2f1 and E2f2, DNA-binding proteins involved in chromatin remodelling
We searched the Jaspar and Uniprobe motif databases for matches of the CGCGC motif to previously reported recognition sites of DNA-binding proteins. The yeast proteins RSC3 and RSC30, and the mammalian proteins E2f1, E2f2, E2f3 and Zfp161, have significantly (FDR <20%) matching database entries [see Additional file 2: Figure S11]. The match to E2f1 is supported further by an E2F1 ChIP-seq experiment in human MCF7 cells that identified CGCGC as the consensus binding sequence . Transcriptionally, E2f1 and E2f2 are highly expressed specifically in oocytes (>15x of the median expression level across tissues), in contrast to E2f3 and Zfp161 (aka Zbtb14) ([see Additional file 2: Figure S12],  and also ).
We re-analysed the E2F1 ChIP-seq data from , identifying 30,467 sites of significant E2F1 enrichment (over input) and [GG]CGCGC as the most significantly enriched motif see Additional file 6: Mini-website with GEM results]. Almost 2/3 of the E2F1 binding sites overlap a CGI from . Using the transcripts annotated by UCSC Known Genes (see Methods for details), we determined that E2F1 is >55x more likely (lower bound of odds ratio (OR) 95% confidence interval) to bind a CGI promoter than a non-CGI promoter (Fisher’s exact test; P <10-15); similarly for the comprehensive set of Gencode v19 transcripts (OR >72; P <10-15). However, only between 8,517 (UCSC) and 8,814 (Gencode) promoters are expressed in MCF7 cells (FANTOM5 CAGE: >1 tags per million mapped tags (TPM)). Still, even among only the expressed promoters, E2F1 has a strong preference for CGI promoters (OR >9.4 (UCSC), >14.2 (Gencode); P <10-15).
E2f1-3 are considered ‘activators’ since they induce H3 and H4 acetylation at target promoters . DNA binding of E2f1 is required in particular for H4 acetylation, and E2f1 directly interacts with the Kat5 (aka Tip60)  histone acetyltransferase (HAT) complex whose preferred targets include K5, K8, K12 and K16 of H4 . This was observed in non-dividing cells, that is, the results are relevant for oocytes where Kat5 also is highly expressed .
The E2f family are also known to interact with the Swi/Snf chromatin remodelling complex via Arid1a and Arid1b . Genes encoding Swi/Snf components (Smarca2, Smarcb1, Smarcc2, Smarce1, Actl6a), and Arid1a as well as Arid1b are highly expressed in mouse oocytes [33, 34, 38, 39]. Like E2f1 and E2f2, Arid1b is highly expressed specifically in oocytes [see Additional file 2: Figure S12] . E2f1 and Kat5 specifically interact with Arid1b, and in proliferating cells, Arid1b is required for the binding of Swi/Snf to the promoters of cell-cycle-specific genes . The Swi/Snf complex can move nucleosomes along DNA, and its recruitment to nucleosomes is enhanced by histone acetylation .
Nucleosome-bound DNA is the preferred substrate of the Dnmt3a/l complex, consistent with features of its structure and the generation of strand-asymmetric pairs of 5mC by its two active sites that are approximately 9 bp apart [18, 23]. In addition, Dnmt3a has particularly high affinity for H3K36me3-marked nucleosomes [41, 42]. H3K36me3 follows transcriptional elongation , and while overall being associated with deacetylation , there is complex interplay with H4K16 acetylation  along transcribed genes.
In this wider context, our findings support a model (Figure 7.C) of E2f1 and/or E2f2 contributing to sequence-specific protection of CGIs from de novo DNA methylation in the oocyte via the recruitment of Kat5 and Swi/Snf, the latter removing nucleosomes from the CGI and thus inhibiting Dnmt3a/l activity on the CGI sequence, even if transcription proceeds through the CGI, which would normally lead to DNA methylation. Our analysis results for human MCF7 cells indicate that E2f1 may play a role in the regulation of DNA methylation at CGIs in somatic cell types too.
Definitive proof of such a role for E2f1/2 will require the genome-wide assessment of DNA methylation in (oocyte-conditional) E2f1/2 knock-outs, direct observation of E2f1/2 binding in oocytes, and/or DNA methylation studies of specific loci with engineered deletions or insertions of E2f1/2 recognition sites. Homozygous triple knock-out mice for E2f1, E2f2 and a non-canonical isoform of E2f3 survive to adulthood, the only described phenotype being a lower body weight . While these are unconditional knock-outs, they may provide an opportunity to study the effects of E2f1 and E2f2 deficiency on DNA methylation in oocytes and during embryogenesis. Transcription factor ChIP-seq in oocytes remains a technical challenge, but given the known E2f1 recognition motif, other, less demanding methods may prove effective at identifying bona fide E2f1 binding sites in oocytes .
Our results support a sequence-independent and transcription elongation-driven model of de novo CGI methylation during oogenesis. However, the vast majority of CGIs resist de novo DNA methylation during oogenesis, and we show that this resistance is associated with the CGCGC DNA sequence motif. The motif is the recognition site consensus for two DNA-binding proteins, E2f1 and E2f2, involved in chromatin remodelling via Arid1b and the Swi/Snf complex. E2f1, E2f2 and Arid1b are highly expressed specifically in oocytes. On the basis of our results in this context, we propose that sequence-specific E2f1 and/or E2f2 binding to CGIs in the oocyte confers protection against de novo DNA methylation via nucleosome depletion by recruited Swi/Snf.
CpG island, gDMR and oocyte promoter coordinates
CGI coordinates, relative to mouse genome NCBI build 37, were taken from . Permanent maternal gDMR coordinates were taken from . Promoter regions of oocyte-expressed transcripts were defined as +/- 1 kbp around the transcription start site (the start of the first exon) of a Cufflinks-reconstructed transcript for which Cufflinks was able to determine the strand of origin and hence, the direction of transcription. Promoter regions also had to overlap a CGI and a region of H3K4me3-enrichment in growing oocyte .
Motif finding analyses
Motif analyses were conducted using modules of the MEME suite  (version 4.9.0). DREME  was used for ab initio motif search, FIMO  was used for searching DNA sequences for motif occurrences, and TOMTOM  was used for finding matches between motifs and known recognition sites of DNA binding proteins.
High throughput sequencing analyses
Percent methylation values of CGIs were taken from [12, 13] for mouse GV and MII stage oocytes, and MeDIP-seq-derived log-transformed methylation fold-change values for mouse E8.5 embryos derived from Dnmt3L-deficient oocytes were taken from . The complete annotation of CGIs with methylation data is part of the Additional file 4 and Additional file 5 (‘CGIs_methylation_Annotation’ spreadsheet).
The oocyte transcriptome was generated from analysing mRNA-seq data for growing (d10, ) and fully grown (d35, ; 7 to 8 week wild type and 7 to 15 week Dnmt3L-deficient ) oocytes using the Tuxedo protocol , including alignment with Tophat  (Bowtie-1 ), per-sample transcript reconstruction with Cufflinks  (v.2.0.1), and merging of per-sample reconstructed transcripts with Cuffmerge  (v.2.0.1). The complete annotation of CGIs with oocyte transcriptome data is part of the Additional file 4 and Additional file 5 (‘CGIs_transcripts_Annotation’ spreadsheet).
H3K4me3 ChIP-seq data for growing oocyte (d15, ) were reanalysed. ChIP-seq reads over CGIs and promoters of oocyte-expressed transcripts were counted using HTSeq (http://www-huber.embl.de/users/anders/HTSeq/). DESeq was used to normalise read counts for differences in sequencing depth between samples, to robustly estimate the variance of read counts between samples, and to variance-stabilise and log-transform the read count data . Subsequently, regions enriched for H3K4me3 in the two IP samples compared to the inputs were identified from a linear model fitted with limma .
Cfp1 ChIP-seq data  were reanalysed using USeq  and MACS  with the default parameters. Since input DNA data were not available, we used the input samples for a CTCF ChIP-seq experiment in the same tissue (whole mouse brain) instead . The sequence reads for the input samples were trimmed to be equal in length to the immunoprecipitated (IP) samples.
CpG island classification
CGIs were classified into distinct classes related to their location relative to oocyte expressed transcripts for which the strand of origin could be determined by Cufflinks. Promoter-associated CGIs (PA) overlap a 1 kbp region (enriched in H3K4me3) around the TSS by at least 1 bp. Intragenic CGIs are located within a transcript, at least 1 kbp distant from the TSS. Distal intragenic CGIs overlap the 1 kbp region downstream from the end of the transcript. End-associated CGIs overlap the 1 kbp region around either the start or the end of a transcript that lacks strand information.
The empirical distribution of the expected number of CpG pairs at distances from 0 to 1,000 bp was generated for each CGI from 1,000 independent permutations of its nucleotides while maintaining the original frequencies of all dinucleotides (dinucleotide frequency-invariant DNA sequence shuffling ). The CpG positions in each shuffled version of the sequence were recorded. From these positions, a pair-wise distance matrix was created. For each distance D from 0 to 1,000 or, if smaller, the length of the island L less two (the maximum distance between two CpGs in a sequence of length L is L-2), the number of CpG pairs was counted. For each distance D, the 1,000 counts generated from the 1,000 permutations of a sequence S form the empirical, expected distribution of the number of CpG pairs at distance D in S. Using this empirical distribution for S, the rank and corresponding empirical P value of the actually observed number of CpG pairs at distance D in S was determined. To test the significance of the number of CpG pairs at distances between 8 and 10 bp, the counts for these distances were added for each of the 1,000 permutations of S as well as for the original sequence S. The empirical p-value for this range of distances was then determined as above (the significance threshold was 0.05).
From the permutation-generated expected distributions, for each S and D, the expected number of CpG pairs in sequence S at distance D was derived by averaging the counts obtained from the 1,000 permutations of S. The expected number for S and D was used to normalise the number of actually observed CpG pairs at distance D in S, that is, observed over expected ratios (obs/exp) were generated. Finally, the obs/exp ratios for each distance D were averaged over all sequences in a CGI category with L - 2 ≥ D, where L is the length of the sequence, that is, excluding sequences that are too short to contain CpG pairs at distance D.
Re-analysis of E2F1 ChIP-seq in MCF7 cells
SRA files with the E2F1 ChIP-seq (SRR167632-3) and input (SRR167638-40) reads were downloaded from the NCBI Short Read Archive, converted to FASTQ with fastq-dump from the SRA toolkit v2.3.5, and aligned to the GRCh37 (hg19) human reference genome using Novoalign v3.02.07. Calling of regions significantly enriched in the E2F1 ChIP-seq samples over input and identification of enriched DNA sequence motifs within those regions was performed using GEM v2.4.1 .
Promoter regions were derived from the transcripts annotated by UCSC Known Genes and, alternatively, by Gencode (comprehensive transcript set v19) as the regions from -1,500 bp to +500 bp of a TSS using Bash and Perl scripts and Bedops v2.4.2 . Overlapping and abutting regions were merged. Each promoter region was then annotated with the maximum number of FANTOM5 CAGE tags per million mapped tags (TPM) that was observed for a CAGE tag cluster in MCF7 cells overlapping the promoter region .
Per CpG DNA methylation data for MCF7 cells generated by RRBS-seq were downloaded (GEO GSM683787 and GSM683793). Correlation between the two replicate samples was high (r2 = 97.97%) so that they were merged. CpGs with <10x coverage were discarded. Bedops was used to annotate each CGI from  with the number of assayed CpGs and their median percent methylation value.
The coordinates of occurrences of the CGCGC motif in the GRCh37 (hg19) genome were determined using dreg from the EMBOSS toolkit v6.6.0.
The above data set [see Additional file 7: BED format files, some with extra columns containing annotation] was the basis for overlap queries following filtering performed with bedops, followed by Fisher’s exact tests or logistic regression modelling in R. For example, TPM-annotated promoters were filtered by TPM >1 to generate the subset of expressed promoters. Similarly, CGIs were filtered by number of assayed CpGs >5, prior to determining their methylation state and testing or linear regression modelling.
embryonic stem cells
imprinting control regions
tags per million mapped tags
This work was supported by a PhD studentship to H.S. funded by the Division of Genetics and Molecular Medicine and the Division of Immunology, Infection & Inflammatory Disease (Professor Michael Malim), King’s College London. Funding for open access charge: Division of Genetics and Molecular Medicine, King’s College London. We would like to thank Michael Weale and Anne Segonds-Pichon for advice on logistic regression analysis. We would like to thank Michael Cowley, Adam Prickett, Nikolaos Barkas, Sebastien Smallwood, Simon Andrews, Gavin Kelsey and Rebecca Oakey for critical reading of the manuscript.
- Blomen VA, Boonstra J: Stable transmission of reversible modifications: maintenance of epigenetic information through the cell cycle. Cell Mol Life Sci. 2011, 68: 27-44. 10.1007/s00018-010-0505-5.PubMed CentralView ArticlePubMedGoogle Scholar
- Morgan HD, Santos F, Green K, Dean W, Reik W: Epigenetic reprogramming in mammals. Hum Mol Genet. 2005, 14 (Spec No 1): R47-R58.View ArticlePubMedGoogle Scholar
- Surani MA, Hajkova P: Epigenetic reprogramming of mouse germ cells toward totipotency. Cold Spring Harb Symp Quant Biol. 2010, 75: 211-218. 10.1101/sqb.2010.75.010.View ArticlePubMedGoogle Scholar
- Wossidlo M, Nakamura T, Lepikhov K, Marques CJ, Zakhartchenko V, Boiani M, Arand J, Nakano T, Reik W, Walter J: 5-Hydroxymethylcytosine in the mammalian zygote is linked with epigenetic reprogramming. Nat Commun. 2011, 2: 241.View ArticlePubMedGoogle Scholar
- Reik W, Dean W, Walter J: Epigenetic reprogramming in mammalian development. Science. 2001, 293: 1089-1093. 10.1126/science.1063443.View ArticlePubMedGoogle Scholar
- Seisenberger S, Andrews S, Krueger F, Arand J, Walter J, Santos F, Popp C, Thienpont B, Dean W, Reik W: The dynamics of genome-wide DNA methylation reprogramming in mouse primordial germ cells. Mol Cell. 2012, 48: 849-862. 10.1016/j.molcel.2012.11.001.PubMed CentralView ArticlePubMedGoogle Scholar
- Hackett JA, Sengupta R, Zylicz JJ, Murakami K, Lee C, Down TA, Surani MA: Germline DNA demethylation dynamics and imprint erasure through 5-hydroxymethylcytosine. Science. 2013, 6: 448-452.View ArticleGoogle Scholar
- Bourc’his D, Proudhon C: Sexual dimorphism in parental imprint ontogeny and contribution to embryonic development. Mol Cell Endocrinol. 2008, 282: 87-94. 10.1016/j.mce.2007.11.025.View ArticlePubMedGoogle Scholar
- Bird A: DNA methylation patterns and epigenetic memory. Genes Dev. 2002, 16: 6-21. 10.1101/gad.947102.View ArticlePubMedGoogle Scholar
- Illingworth R, Gruenwald-Schneider U, Webb S, Kerr A, James KD, Turner DJ, Smith C, Harrison DJ, Andrews R, Bird A: Orphan CpG islands identify numerous conserved promoters in the mammalian genome. PLoS Genet. 2010, 6: e1001134-10.1371/journal.pgen.1001134.PubMed CentralView ArticlePubMedGoogle Scholar
- Deaton AM, Bird A: CpG islands and the regulation of transcription. Genes Dev. 2011, 25: 1010-1022. 10.1101/gad.2037511.PubMed CentralView ArticlePubMedGoogle Scholar
- Kobayashi H, Sakurai T, Imai M, Takahashi N, Fukuda A, Yayoi O, Sato S, Nakabayashi K, Hata K, Sotomaru Y, Suzuki Y, Kono T: Contribution of intragenic DNA methylation in mouse gametic DNA methylomes to establish oocyte-specific heritable marks. PLoS Genet. 2012, 8: e1002440-10.1371/journal.pgen.1002440.PubMed CentralView ArticlePubMedGoogle Scholar
- Smallwood SA, Tomizawa S, Krueger F, Ruf N, Carli N, Segonds-Pichon A, Sato S, Hata K, Andrews S, Kelsey G: Dynamic CpG island methylation landscape in oocytes and preimplantation embryos. Nat Genet. 2011, 43: 811-814. 10.1038/ng.864.PubMed CentralView ArticlePubMedGoogle Scholar
- Proudhon C, Duffie R, Ajjan S, Cowley M, Iranzo J, Carbajosa G, Saadeh H, Holland ML, Oakey RJ, Rakyan VK, Schulz R, Bourc’his D: Protection against de novo methylation is instrumental in maintaining parent-of-origin methylation inherited from the gametes. Mol Cell. 2012, 47: 909-920. 10.1016/j.molcel.2012.07.010.PubMed CentralView ArticlePubMedGoogle Scholar
- Lienert F, Wirbelauer C, Som I, Dean A, Mohn F, Schübeler D: Identification of genetic elements that autonomously determine DNA methylation states. Nat Genet. 2011, 43: 1091-1097. 10.1038/ng.946.View ArticlePubMedGoogle Scholar
- Quenneville S, Verde G, Corsinotti A, Kapopoulou A, Jakobsson J, Offner S, Baglivo I, Pedone PV, Grimaldi G, Riccio A, Trono D: In embryonic stem cells, ZFP57/KAP1 recognize a methylated hexanucleotide to affect chromatin and DNA methylation of imprinting control regions. Mol Cell. 2011, 44: 361-372. 10.1016/j.molcel.2011.08.032.PubMed CentralView ArticlePubMedGoogle Scholar
- Bailey TL: DREME: motif discovery in transcription factor ChIP-seq data. Bioinformatics. 2011, 27: 1653-1659. 10.1093/bioinformatics/btr261.PubMed CentralView ArticlePubMedGoogle Scholar
- Jia D, Jurkowska RZ, Zhang X, Jeltsch A, Cheng X: Structure of Dnmt3a bound to Dnmt3L suggests a model for de novo DNA methylation. Nature. 2007, 449: 248-251. 10.1038/nature06146.PubMed CentralView ArticlePubMedGoogle Scholar
- Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, Gabo K, Rongione M, Webster M, Ji H, Potash JB, Sabunciyan S, Feinberg AP: The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat Genet. 2009, 41: 178-186. 10.1038/ng.298.PubMed CentralView ArticlePubMedGoogle Scholar
- Hess ST, Blake JD, Blake RD: Wide variations in neighbour-dependent substitution rates. J Mol Biol. 1994, 236: 1022-1033. 10.1016/0022-2836(94)90009-4.View ArticlePubMedGoogle Scholar
- Chotalia M, Smallwood SA, Ruf N, Dawson C, Lucifero D, Frontera M, James K, Dean W, Kelsey G: Transcription is required for establishment of germline methylation marks at imprinted genes. Genes Dev. 2009, 23: 105-117. 10.1101/gad.495809.PubMed CentralView ArticlePubMedGoogle Scholar
- Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L: Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012, 7: 562-578. 10.1038/nprot.2012.016.PubMed CentralView ArticlePubMedGoogle Scholar
- Jurkowska RZ, Anspach N, Urbanke C, Jia D, Reinhardt R, Nellen W, Cheng X, Jeltsch A: Formation of nucleoprotein filaments by mammalian DNA methyltransferase Dnmt3a in complex with regulator Dnmt3L. Nucleic Acids Res. 2008, 36: 6656-6663. 10.1093/nar/gkn747.PubMed CentralView ArticlePubMedGoogle Scholar
- Glass JL, Fazzari MJ, Ferguson-Smith AC, Greally JM: CG dinucleotide periodicities recognized by the Dnmt3a-Dnmt3L complex are distinctive at retroelements and imprinted domains. Mamm Genome. 2009, 20: 633-643. 10.1007/s00335-009-9232-3.View ArticlePubMedGoogle Scholar
- Altschul SF, Erickson BW: Significance of nucleotide sequence alignments: a method for random sequence permutation that preserves dinucleotide and codon usage. Mol Biol Evol. 1985, 2: 526-538.PubMedGoogle Scholar
- Jones PA: Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat Rev Genet. 2012, 13: 484-492. 10.1038/nrg3230.View ArticlePubMedGoogle Scholar
- Ginno PA, Lott PL, Christensen HC, Korf I, Chédin F: R-loop formation is a distinctive characteristic of unmethylated human CpG island promoters. Mol Cell. 2012, 45: 814-825. 10.1016/j.molcel.2012.01.017.PubMed CentralView ArticlePubMedGoogle Scholar
- Clouaire T, Webb S, Skene P, Illingworth R, Kerr A, Andrews R, Lee JH, Skalnik D, Bird A: Cfp1 integrates both CpG content and gene activity for accurate H3K4me3 deposition in embryonic stem cells. Genes Dev. 2012, 26: 1714-1728. 10.1101/gad.194209.112.PubMed CentralView ArticlePubMedGoogle Scholar
- Thomson JP, Skene PJ, Selfridge J, Clouaire T, Guy J, Webb S, Kerr AR, Deaton A, Andrews R, James KD, Turner DJ, Illingworth R, Bird A: CpG islands influence chromatin structure via the CpG-binding protein Cfp1. Nature. 2010, 464: 1082-1086. 10.1038/nature08924.PubMed CentralView ArticlePubMedGoogle Scholar
- Ooi SK, Qiu C, Bernstein E, Li K, Jia D, Yang Z, Erdjument-Bromage H, Tempst P, Lin SP, Allis CD, Cheng X, Bestor TH: DNMT3L connects unmethylated lysine 4 of histone H3 to de novo methylation of DNA. Nature. 2007, 45: 714-717.View ArticleGoogle Scholar
- Zhang Y, Jurkowska R, Soeroes S, Rajavelu A, Dhayalan A, Bock I, Rathert P, Brandt O, Reinhardt R, Fischle W, Jeltsch A: Chromatin methylation activity of Dnmt3a and Dnmt3a/3 L is guided by interaction of the ADD domain with the histone H3 tail. Nucleic Acids Res. 2010, 38: 4246-4253. 10.1093/nar/gkq147.PubMed CentralView ArticlePubMedGoogle Scholar
- Cao AR, Rabinovich R, Xu M, Xu X, Jin VX, Farnham PJ: Genome-wide analysis of transcription factor E2F1 mutant proteins reveals that N- and C-terminal protein interaction domains do not participate in targeting E2F1 to the human genome. J Biol Chem. 2011, 286: 11985-11996. 10.1074/jbc.M110.217158.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu C, Orozco C, Boyer J, Leglise M, Goodale J, Batalov S, Hodge CL, Haase J, Janes J: Huss JW3rd. Su AI: BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 2009, 10: R130.PubMedGoogle Scholar
- Schulz R, Woodfine K, Menheniott TR, Bourc'his D, Bestor T, Oakey RJ: WAMIDEX: a web atlas of murine genomic imprinting and differential expression. Epigenetics. 2008, 3: 89-96. 10.4161/epi.3.2.5900.PubMed CentralView ArticlePubMedGoogle Scholar
- Taubert S, Gorrini C, Frank SR, Parisi T, Fuchs M, Chan HM, Livingston DM, Amati B: E2F-dependent histone acetylation and recruitment of the Tip60 acetyltransferase complex to chromatin in late G1. Mol Cell Biol. 2004, 24: 4546-4556. 10.1128/MCB.24.10.4546-4556.2004.PubMed CentralView ArticlePubMedGoogle Scholar
- Kimura A, Horikoshi M: Tip60 acetylates six lysines of a specific class in core histones in vitro. Genes Cells. 1998, 3: 789-800. 10.1046/j.1365-2443.1998.00229.x.View ArticlePubMedGoogle Scholar
- Nagl NGJ, Wang X, Patsialou A, Van Scoy M, Moran E: Distinct mammalian SWI/SNF chromatin remodeling complexes with opposing roles in cell-cycle control. Embo J. 2007, 26: 752-763. 10.1038/sj.emboj.7601541.PubMed CentralView ArticlePubMedGoogle Scholar
- Oliveri RS, Kalisz M, Schjerling CK, Andersen CY, Borup R, Byskov AG: Evaluation in mammalian oocytes of gene transcripts linked to epigenetic reprogramming. Reproduction. 2007, 134: 549-558. 10.1530/REP-06-0315.View ArticlePubMedGoogle Scholar
- Pan H, O'Brien MJ, Wigglesworth K, Eppig JJ, Schultz RM: Transcript profiling during mouse oocyte development and the effect of gonadotropin priming and development in vitro. Dev Biol. 2005, 286: 493-506. 10.1016/j.ydbio.2005.08.023.View ArticlePubMedGoogle Scholar
- Belandia B, Parker MG: Nuclear receptors: a rendezvous for chromatin remodeling factors. Cell. 2003, 114: 277-280. 10.1016/S0092-8674(03)00599-3.View ArticlePubMedGoogle Scholar
- Dhayalan A, Rajavelu A, Rathert P, Tamas R, Jurkowska RZ, Ragozin S, Jeltsch A: The Dnmt3a PWWP domain reads histone 3 lysine 36 trimethylation and guides DNA methylation. J Biol Chem. 2010, 285: 26114-26120. 10.1074/jbc.M109.089433.PubMed CentralView ArticlePubMedGoogle Scholar
- Vezzoli A, Bonadies N, Allen MD, Freund SM, Santiveri CM, Kvinlaug BT, Huntly BJ, Gottgens B, Bycroft M: Molecular basis of histone H3K36me3 recognition by the PWWP domain of Brpf1. Nat Struct Mol Biol. 2010, 17: 617-619. 10.1038/nsmb.1797.View ArticlePubMedGoogle Scholar
- Smolle M, Workman JL: Transcription-associated histone modifications and cryptic transcription. Biochim Biophys Acta. 2013, 1829: 84-97. 10.1016/j.bbagrm.2012.08.008.View ArticlePubMedGoogle Scholar
- Venkatesh S, Workman JL: Set2 mediated H3 lysine 36 methylation: regulation of transcription elongation and implications in organismal development. Wiley Interdiscip Rev Dev Biol. 2013, 2: 685-700. 10.1002/wdev.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Bell O, Wirbelauer C, Hild M, Scharf AN, Schwaiger M, MacAlpine DM, Zilbermann F, van Leeuwen F, Bell SP, Imhof A, Garza D, Peters AH, Schübeler D: Localized H3K36 methylation states define histone H4K16 acetylation during transcriptional elongation in Drosophila. EMBO J. 2007, 26: 4974-4984. 10.1038/sj.emboj.7601926.PubMed CentralView ArticlePubMedGoogle Scholar
- Tsai SY, Opavsky R, Sharma N, Wu L, Naidu S, Nolan E, Feria-Arias E, Timmers C, Opavska J, De Bruin A, Chong JL, Trikha P, Fernandez SA, Stromberg P, Rosol TJ, Leone G: Mouse development with a single E2F activator. Nature. 2008, 454: 1137-1141. 10.1038/nature07066.PubMed CentralView ArticlePubMedGoogle Scholar
- Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ: Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Meth. 2013, 10: 1213-1218. 10.1038/nmeth.2688.View ArticleGoogle Scholar
- Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS: MEME Suite: Tools for motif discovery and searching. Nucleic Acids Res. 2009, 37: W202-W208. 10.1093/nar/gkp335.PubMed CentralView ArticlePubMedGoogle Scholar
- Grant CE, Bailey TL, Noble WS: FIMO: Scanning for occurrences of a given motif. Bioinformatics. 2011, 27: 1017-1018. 10.1093/bioinformatics/btr064.PubMed CentralView ArticlePubMedGoogle Scholar
- Gupta S, Stamatoyannopolous JA, Bailey T, Noble WS: Quantifying similarity between motifs. Genome Biol. 2007, 8: R24-10.1186/gb-2007-8-2-r24.PubMed CentralView ArticlePubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009, 25: 1105-1111. 10.1093/bioinformatics/btp120.PubMed CentralView ArticlePubMedGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009, 10: R25-10.1186/gb-2009-10-3-r25.PubMed CentralView ArticlePubMedGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, Salzberg SL, Wold BJ, Pachter L: Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010, 28: 511-515. 10.1038/nbt.1621.PubMed CentralView ArticlePubMedGoogle Scholar
- Anders S, Huber W: Differential expression analysis for sequence count data. Genome Biol. 2010, 11: R106-10.1186/gb-2010-11-10-r106.PubMed CentralView ArticlePubMedGoogle Scholar
- Smyth GK: Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor. 2005, New York: Springer: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W, 397-420.View ArticleGoogle Scholar
- Nix DA, Courdy SJ, Boucher KM: Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks. BMC Bioinformatics. 2008, 1: 523.View ArticleGoogle Scholar
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS: Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008, 9: R137-10.1186/gb-2008-9-9-r137.PubMed CentralView ArticlePubMedGoogle Scholar
- Prickett AR, Barkas N, McCole RB, Hughes S, Amante SM, Schulz R, Oakey RJ: Genome-wide and parental allele-specific analysis of CTCF and cohesin DNA binding in mouse brain reveals a tissue-specific binding pattern and an association with imprinted differentially methylated regions. Genome Res. 2013, 23: 7.View ArticleGoogle Scholar
- Guo Y, Mahony S, Gifford DK: High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints. PLoS Comput Biol. 2012, 8: e1002638-10.1371/journal.pcbi.1002638.PubMed CentralView ArticlePubMedGoogle Scholar
- Neph S, Kuehn MS, Reynolds AP, Haugen E, Thurman RE, Johnson AK, Rynes E, Maurano MT, Vierstra J, Thomas S, Sandstrom R, Humbert R, Stamatoyannopoulos JA: BEDOPS: high-performance genomic feature operations. Bioinformatics. 2012, 28: 1919-1920. 10.1093/bioinformatics/bts277.PubMed CentralView ArticlePubMedGoogle Scholar
- FANTOM Consortium and the RIKEN PMI and CLST (DGT), Forrest AR, Kawaji H, Rehli M, Baillie JK, de Hoon MJ, Lassmann T, Itoh M, Summers KM, Suzuki H, Daub CO, Kawai J, Heutink P, Hide W, Freeman TC, Lenhard B, Bajic VB, Taylor MS, Makeev VJ, Sandelin A, Hume DA, Carninci P, Hayashizaki Y: A promoter-level mammalian expression atlas. Nature. 2014, 507: 462-470. 10.1038/nature13182.View ArticleGoogle Scholar
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