Mouse methylome studies SRP285138 Track Settings
 
Dnmt1 has global de novo methylation activity and is specifically targeted to transposable elements [E3.5 Blastocyst, E6.5 Epiblast, ESC]

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 SRX11531297  CpG methylation  ESC / SRX11531297 (CpG methylation)   Schema 
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 SRX11531298  CpG methylation  ESC / SRX11531298 (CpG methylation)   Schema 
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 SRX11531300  CpG methylation  ESC / SRX11531300 (CpG methylation)   Schema 
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 SRX11531336  CpG methylation  ESC / SRX11531336 (CpG methylation)   Schema 
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 SRX9179158  CpG methylation  ESC / SRX9179158 (CpG methylation)   Schema 
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 SRX9179159  CpG methylation  ESC / SRX9179159 (CpG methylation)   Schema 
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 SRX9179160  CpG methylation  ESC / SRX9179160 (CpG methylation)   Schema 
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 SRX9179161  CpG methylation  ESC / SRX9179161 (CpG methylation)   Schema 
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 SRX9179163  CpG methylation  ESC / SRX9179163 (CpG methylation)   Schema 
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 SRX9179164  CpG methylation  ESC / SRX9179164 (CpG methylation)   Schema 
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 SRX9179165  CpG methylation  ESC / SRX9179165 (CpG methylation)   Schema 
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 SRX9179166  CpG methylation  ESC / SRX9179166 (CpG methylation)   Schema 
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 SRX9179167  CpG methylation  E3.5 Blastocyst / SRX9179167 (CpG methylation)   Schema 
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 SRX9179168  CpG methylation  E3.5 Blastocyst / SRX9179168 (CpG methylation)   Schema 
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 SRX9179169  CpG methylation  E3.5 Blastocyst / SRX9179169 (CpG methylation)   Schema 
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 SRX9179170  CpG methylation  E3.5 Blastocyst / SRX9179170 (CpG methylation)   Schema 
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 SRX9179171  CpG methylation  E3.5 Blastocyst / SRX9179171 (CpG methylation)   Schema 
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 SRX9179172  CpG methylation  E3.5 Blastocyst / SRX9179172 (CpG methylation)   Schema 
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 SRX9179173  CpG methylation  E3.5 Blastocyst / SRX9179173 (CpG methylation)   Schema 
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 SRX9179174  CpG methylation  E6.5 Epiblast / SRX9179174 (CpG methylation)   Schema 
    

Study title: Dnmt1 has global de novo methylation activity and is specifically targeted to transposable elements
SRA: SRP285138
GEO: GSE158460
Pubmed: 34140676

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX11531297 ESC 0.014 18.3 3 1247407.0 0 0.0 18 8596909.6 0.984 title: GSM5468440 TKOL_P10, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO + Dnmt1 shRNA"}
SRX11531298 ESC 0.072 26.6 6 923752.8 62 881.9 5 12956356.2 0.987 title: GSM5468441 DKO0_P25, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO"}
SRX11531299 ESC 0.016 25.8 28 822594.2 0 0.0 22 8119152.8 0.984 title: GSM5468442 DKO0_Uhrf1_A10_P15, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b + Uhrf1 KO"}
SRX11531300 ESC 0.015 28.9 10 1010439.3 0 0.0 15 8155286.2 0.984 title: GSM5468443 DKO0_Uhrf1_C5_P15, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b + Uhrf1 KO"}
SRX11531336 ESC 0.735 34.9 48701 1328.4 785 985.7 4712 12042.5 0.978 title: GSM5468479 WT_WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "V65 mESC", "cell_type": "mESC", "strain": "V65", "genotype": "WT"}
SRX9179158 ESC 0.012 26.9 15 901816.2 0 0.0 50 6244987.7 0.985 title: GSM4801046 TKOL, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO + Dnmt1 shRNA"}
SRX9179159 ESC 0.012 26.9 5 1192093.6 0 0.0 23 7179184.3 0.987 title: GSM4801047 DKO0_P1, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO"}
SRX9179160 ESC 0.015 28.7 8 937218.8 0 0.0 3 12065552.3 0.990 title: GSM4801048 DKO0_P5, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO"}
SRX9179161 ESC 0.059 26.2 3 1127231.3 42 879.7 16 11486329.1 0.984 title: GSM4801049 DKO0_P15, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO"}
SRX9179163 ESC 0.010 6.8 6 1063781.0 0 0.0 18 14261448.7 0.986 title: GSM4801051 DKO0_Uhrf1_C5, Mus musculus, Bisulfite-Seq; {"source_name": "J1 mESC", "cell_type": "mESC", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b + Uhrf1 KO"}
SRX9179164 ESC 0.017 20.1 3 1293114.0 0 0.0 8 10953549.0 0.984 title: GSM4801052 TKO, Mus musculus, Bisulfite-Seq; {"source_name": "KH2 mESC", "cell_type": "mESC", "strain": "KH2", "genotype": "Dnmt3a + Dnmt3b + Dnmt1 KO"}
SRX9179165 ESC 0.059 17.8 3 903413.0 12 614.8 1 15638251.0 0.984 title: GSM4801053 TKO_Dnmt1_rescue, Mus musculus, Bisulfite-Seq; {"source_name": "KH2 mESC", "cell_type": "mESC", "strain": "KH2", "genotype": "Dnmt3a + Dnmt3b + Dnmt1 KO + ectopically Dnmt1"}
SRX9179166 ESC 0.023 21.1 3 1218074.0 1 566.0 3 12777599.0 0.984 title: GSM4801054 TKO_Dnmt1_inactive, Mus musculus, Bisulfite-Seq; {"source_name": "KH2 mESC", "cell_type": "mESC", "strain": "KH2", "genotype": "Dnmt3a + Dnmt3b + Dnmt1 KO + ectopically catalytically inactive Dnmt1"}
SRX9179167 E3.5 Blastocyst 0.132 17.4 1452 85439.8 2661 975.6 0 0.0 0.983 title: GSM4801055 WT_E3.5_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E3.5 Blastocyst", "cell_type": "E3.5 Blastocyst", "strain": "J1", "genotype": "WT"}
SRX9179168 E3.5 Blastocyst 0.145 15.7 613 101993.3 2150 963.4 0 0.0 0.983 title: GSM4801056 WT_E3.5_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E3.5 Blastocyst", "cell_type": "E3.5 Blastocyst", "strain": "J1", "genotype": "WT"}
SRX9179169 E3.5 Blastocyst 0.180 13.5 16010 27780.1 2795 938.2 100 580461.4 0.983 title: GSM4801057 Dnmt3ab_DKO_E3.5_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E3.5 Blastocyst", "cell_type": "E3.5 Blastocyst", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO"}
SRX9179170 E3.5 Blastocyst 0.154 12.9 7381 45138.9 2252 968.9 0 0.0 0.983 title: GSM4801058 Dnmt3ab_DKO_E3.5_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E3.5 Blastocyst", "cell_type": "E3.5 Blastocyst", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO"}
SRX9179171 E3.5 Blastocyst 0.147 15.5 9831 41420.4 2990 931.2 198 496001.4 0.982 title: GSM4801059 Dnmt1_KO_E3.5, Mus musculus, Bisulfite-Seq; {"source_name": "E3.5 Blastocyst", "cell_type": "E3.5 Blastocyst", "strain": "J1", "genotype": "Dnmt1 KO"}
SRX9179172 E3.5 Blastocyst 0.121 19.4 7143 47866.1 4586 991.0 1235 231011.5 0.984 title: GSM4801060 DKO_DMSO_E35, Mus musculus, Bisulfite-Seq; {"source_name": "E3.5 Blastocyst", "cell_type": "E3.5 Blastocyst", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO + DMSO"}
SRX9179173 E3.5 Blastocyst 0.113 20.7 14269 33419.3 2879 986.1 59 617999.6 0.983 title: GSM4801061 DKO_0.35uM_1i_E35, Mus musculus, Bisulfite-Seq; {"source_name": "E3.5 Blastocyst", "cell_type": "E3.5 Blastocyst", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO + Dnmt1 inhibitor"}
SRX9179174 E6.5 Epiblast 0.145 17.9 0 0.0 314 845.6 2 3260261.5 0.983 title: GSM4801062 DKO_0.35uM_1i_Epi, Mus musculus, Bisulfite-Seq; {"source_name": "E6.5 Epiblast", "cell_type": "E6.5 Epiblast", "strain": "J1", "genotype": "Dnmt3a + Dnmt3b KO + Dnmt1 inhibitor"}

Methods

All analysis was done using a bisulfite sequnecing data analysis pipeline DNMTools developed in the Smith lab at USC.

Mapping reads from bisulfite sequencing: Bisulfite treated reads are mapped to the genomes with the abismal program. Input reads are filtered by their quality, and adapter sequences in the 3' end of reads are trimmed. This is done with cutadapt. Uniquely mapped reads with mismatches/indels below given threshold are retained. For pair-end reads, if the two mates overlap, the overlapping part of the mate with lower quality is discarded. After mapping, we use the format command in dnmtools to merge mates for paired-end reads. We use the dnmtools uniq command to randomly select one from multiple reads mapped exactly to the same location. Without random oligos as UMIs, this is our best indication of PCR duplicates.

Estimating methylation levels: After reads are mapped and filtered, the dnmtools counts command is used to obtain read coverage and estimate methylation levels at individual cytosine sites. We count the number of methylated reads (those containing a C) and the number of unmethylated reads (those containing a T) at each nucleotide in a mapped read that corresponds to a cytosine in the reference genome. The methylation level of that cytosine is estimated as the ratio of methylated to total reads covering that cytosine. For cytosines in the symmetric CpG sequence context, reads from the both strands are collapsed to give a single estimate. Very rarely do the levels differ between strands (typically only if there has been a substitution, as in a somatic mutation), and this approach gives a better estimate.

Bisulfite conversion rate: The bisulfite conversion rate for an experiment is estimated with the dnmtools bsrate command, which computes the fraction of successfully converted nucleotides in reads (those read out as Ts) among all nucleotides in the reads mapped that map over cytosines in the reference genome. This is done either using a spike-in (e.g., lambda), the mitochondrial DNA, or the nuclear genome. In the latter case, only non-CpG sites are used. While this latter approach can be impacted by non-CpG cytosine methylation, in practice it never amounts to much.

Identifying hypomethylated regions (HMRs): In most mammalian cells, the majority of the genome has high methylation, and regions of low methylation are typically the interesting features. (This seems to be true for essentially all healthy differentiated cell types, but not cells of very early embryogenesis, various germ cells and precursors, and placental lineage cells.) These are valleys of low methylation are called hypomethylated regions (HMR) for historical reasons. To identify the HMRs, we use the dnmtools hmr command, which uses a statistical model that accounts for both the methylation level fluctations and the varying amounts of data available at each CpG site.

Partially methylated domains: Partially methylated domains are large genomic regions showing partial methylation observed in immortalized cell lines and cancerous cells. The pmd program is used to identify PMDs.

Allele-specific methylation: Allele-Specific methylated regions refers to regions where the parental allele is differentially methylated compared to the maternal allele. The program allelic is used to compute allele-specific methylation score can be computed for each CpG site by testing the linkage between methylation status of adjacent reads, and the program amrfinder is used to identify regions with allele-specific methylation.

For more detailed description of the methods of each step, please refer to the DNMTools documentation.