Mouse methylome studies SRP251304 Track Settings
 
Control of Foxp3 induction and maintenance by sequential histone acetylation and DNA demethylation [WGBS] [In vitro induced wild-type regulatory T cells (ascorbic acid treated), In vitro induced wild-type regulatory T cells (control), Wild-type CD4 effector T cells, Wild-type naive CD4 T cells, Wild-type regulatory T cells]

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 SRX7828317  CpG methylation  Wild-type naive CD4 T cells / SRX7828317 (CpG methylation)   Schema 
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 SRX7828318  CpG methylation  Wild-type naive CD4 T cells / SRX7828318 (CpG methylation)   Schema 
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 SRX7828319  CpG methylation  Wild-type regulatory T cells / SRX7828319 (CpG methylation)   Schema 
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 SRX7828320  CpG methylation  Wild-type regulatory T cells / SRX7828320 (CpG methylation)   Schema 
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 SRX7828320  HMR  Wild-type regulatory T cells / SRX7828320 (HMR)   Schema 
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 SRX7828321  CpG methylation  Wild-type CD4 effector T cells / SRX7828321 (CpG methylation)   Schema 
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 SRX7828321  HMR  Wild-type CD4 effector T cells / SRX7828321 (HMR)   Schema 
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 SRX7828322  HMR  Wild-type CD4 effector T cells / SRX7828322 (HMR)   Schema 
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 SRX7828322  CpG methylation  Wild-type CD4 effector T cells / SRX7828322 (CpG methylation)   Schema 
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 SRX7828323  CpG methylation  In vitro induced wild-type regulatory T cells (control) / SRX7828323 (CpG methylation)   Schema 
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 SRX7828323  HMR  In vitro induced wild-type regulatory T cells (control) / SRX7828323 (HMR)   Schema 
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 SRX7828324  CpG methylation  In vitro induced wild-type regulatory T cells (control) / SRX7828324 (CpG methylation)   Schema 
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 SRX7828325  CpG methylation  In vitro induced wild-type regulatory T cells (ascorbic acid treated) / SRX7828325 (CpG methylation)   Schema 
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 SRX7828325  HMR  In vitro induced wild-type regulatory T cells (ascorbic acid treated) / SRX7828325 (HMR)   Schema 
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 SRX7828326  CpG methylation  In vitro induced wild-type regulatory T cells (ascorbic acid treated) / SRX7828326 (CpG methylation)   Schema 
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 SRX7828326  HMR  In vitro induced wild-type regulatory T cells (ascorbic acid treated) / SRX7828326 (HMR)   Schema 
    

Study title: Control of Foxp3 induction and maintenance by sequential histone acetylation and DNA demethylation [WGBS]
SRA: SRP251304
GEO: GSE146248
Pubmed: 34910919

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX7828317 Wild-type naive CD4 T cells 0.789 21.7 60422 918.2 744 1010.5 1810 12690.6 0.980 title: GSM4368917 1119042_Tn1, Mus musculus, Bisulfite-Seq; {"source_name": "Wild-type naive CD4 T cells", "strain": "C57BL/6", "cell_type": "Wild-type naive CD4 T cells", "sex": "male"}
SRX7828318 Wild-type naive CD4 T cells 0.792 22.5 63406 921.7 838 997.9 3874 8085.4 0.975 title: GSM4368918 1163347_Tn2, Mus musculus, Bisulfite-Seq; {"source_name": "Wild-type naive CD4 T cells", "strain": "C57BL/6", "cell_type": "Wild-type naive CD4 T cells", "sex": "male"}
SRX7828319 Wild-type regulatory T cells 0.781 12.8 45731 1089.0 378 1029.0 981 16501.3 0.973 title: GSM4368919 1119041_Treg1, Mus musculus, Bisulfite-Seq; {"source_name": "Wild-type regulatory T cells", "strain": "C57BL/6", "cell_type": "Wild-type regulatory T cells", "sex": "male"}
SRX7828320 Wild-type regulatory T cells 0.793 19.0 55716 976.6 746 1035.4 1714 12674.1 0.972 title: GSM4368920 1163348_Treg2, Mus musculus, Bisulfite-Seq; {"source_name": "Wild-type regulatory T cells", "strain": "C57BL/6", "cell_type": "Wild-type regulatory T cells", "sex": "male"}
SRX7828321 Wild-type CD4 effector T cells 0.762 19.7 46456 1015.4 650 1001.0 1444 11476.4 0.980 title: GSM4368921 1119043_Te1, Mus musculus, Bisulfite-Seq; {"source_name": "Wild-type CD4 effector T cells", "strain": "C57BL/6", "cell_type": "Wild-type CD4 effector T cells", "sex": "male"}
SRX7828322 Wild-type CD4 effector T cells 0.762 18.9 48629 1003.4 800 1034.1 1435 11715.7 0.974 title: GSM4368922 1163349_Te2, Mus musculus, Bisulfite-Seq; {"source_name": "Wild-type CD4 effector T cells", "strain": "C57BL/6", "cell_type": "Wild-type CD4 effector T cells", "sex": "male"}
SRX7828323 In vitro induced wild-type regulatory T cells (control) 0.804 11.6 42925 1098.6 313 1031.5 1109 14651.5 0.980 title: GSM4368923 1119044_iTreg1, Mus musculus, Bisulfite-Seq; {"source_name": "In vitro induced wild-type regulatory T cells (control)", "strain": "C57BL/6", "cell_type": "In vitro induced wild-type regulatory T cells (control)", "sex": "male"}
SRX7828324 In vitro induced wild-type regulatory T cells (control) 0.786 20.4 53820 962.9 881 1053.8 1675 11688.5 0.972 title: GSM4368924 1163350_iTreg2, Mus musculus, Bisulfite-Seq; {"source_name": "In vitro induced wild-type regulatory T cells (control)", "strain": "C57BL/6", "cell_type": "In vitro induced wild-type regulatory T cells (control)", "sex": "male"}
SRX7828325 In vitro induced wild-type regulatory T cells (ascorbic acid treated) 0.784 11.9 45522 1109.4 356 1022.3 1344 15189.4 0.979 title: GSM4368925 1119045_iTregAA2P1, Mus musculus, Bisulfite-Seq; {"source_name": "In vitro induced wild-type regulatory T cells (ascorbic acid treated)", "strain": "C57BL/6", "cell_type": "In vitro induced wild-type regulatory T cells (ascorbic acid treated)", "sex": "male"}
SRX7828326 In vitro induced wild-type regulatory T cells (ascorbic acid treated) 0.766 22.3 57896 955.0 820 1036.0 1605 13139.0 0.975 title: GSM4368926 1163351_iTregAA2P2, Mus musculus, Bisulfite-Seq; {"source_name": "In vitro induced wild-type regulatory T cells (ascorbic acid treated)", "strain": "C57BL/6", "cell_type": "In vitro induced wild-type regulatory T cells (ascorbic acid treated)", "sex": "male"}

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.