Mouse methylome studies SRP288034 Track Settings
 
DNA methylomes of murine B and T cells from the BLUEPRINT Epigenome project [CD4+CD62L+ T cells, CD43-B cells]

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 SRX9333782  CpG methylation  CD43-B cells / SRX9333782 (CpG methylation)   Schema 
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 SRX9333783  CpG methylation  CD43-B cells / SRX9333783 (CpG methylation)   Schema 
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 SRX9333784  CpG methylation  CD43-B cells / SRX9333784 (CpG methylation)   Schema 
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 SRX9333785  CpG methylation  CD43-B cells / SRX9333785 (CpG methylation)   Schema 
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 SRX9333786  CpG methylation  CD4+CD62L+ T cells / SRX9333786 (CpG methylation)   Schema 
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 SRX9333787  CpG methylation  CD4+CD62L+ T cells / SRX9333787 (CpG methylation)   Schema 
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 SRX9333788  CpG methylation  CD4+CD62L+ T cells / SRX9333788 (CpG methylation)   Schema 
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 SRX9333789  CpG methylation  CD4+CD62L+ T cells / SRX9333789 (CpG methylation)   Schema 
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 SRX9333790  CpG methylation  CD43-B cells / SRX9333790 (CpG methylation)   Schema 
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 SRX9333791  CpG methylation  CD43-B cells / SRX9333791 (CpG methylation)   Schema 
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 SRX9333792  CpG methylation  CD43-B cells / SRX9333792 (CpG methylation)   Schema 
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 SRX9333792  HMR  CD43-B cells / SRX9333792 (HMR)   Schema 
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 SRX9333793  CpG methylation  CD43-B cells / SRX9333793 (CpG methylation)   Schema 
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 SRX9333794  CpG methylation  CD4+CD62L+ T cells / SRX9333794 (CpG methylation)   Schema 
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 SRX9333795  CpG methylation  CD4+CD62L+ T cells / SRX9333795 (CpG methylation)   Schema 
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 SRX9333796  CpG methylation  CD4+CD62L+ T cells / SRX9333796 (CpG methylation)   Schema 
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 SRX9333797  CpG methylation  CD4+CD62L+ T cells / SRX9333797 (CpG methylation)   Schema 
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Study title: DNA methylomes of murine B and T cells from the BLUEPRINT Epigenome project
SRA: SRP288034
GEO: GSE159790
Pubmed: 33612119

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX9333782 CD43-B cells 0.788 7.8 47121 1046.0 352 1085.2 1246 14723.4 0.994 title: GSM4844987 B6_F_B_10 [BS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD43-B cells", "strain": "C57BL/6J", "sex": "female", "cell_type": "CD43-B cells"}
SRX9333783 CD43-B cells 0.786 6.5 46758 1088.8 203 1079.8 914 22212.0 0.996 title: GSM4844988 B6_F_B_12 [BS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD43-B cells", "strain": "C57BL/6J", "sex": "female", "cell_type": "CD43-B cells"}
SRX9333784 CD43-B cells 0.780 8.4 49260 1055.3 399 1096.9 1459 15767.7 0.997 title: GSM4844989 B6_F_B_11 [OxBS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD43-B cells", "strain": "C57BL/6J", "sex": "female", "cell_type": "CD43-B cells"}
SRX9333785 CD43-B cells 0.787 9.0 48731 1014.7 455 1064.5 1179 15313.4 0.996 title: GSM4844990 B6_F_B_9 [OxBS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD43-B cells", "strain": "C57BL/6J", "sex": "female", "cell_type": "CD43-B cells"}
SRX9333786 CD4+CD62L+ T cells 0.780 8.9 50067 1041.7 471 1058.9 1364 16299.7 0.998 title: GSM4844991 B6_F_T_13 [BS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD4+CD62L+ T cells", "strain": "C57BL/6J", "sex": "female", "cell_type": "CD4+CD62L+ T cells"}
SRX9333787 CD4+CD62L+ T cells 0.781 8.3 49507 1043.9 454 1044.3 1185 17031.0 0.998 title: GSM4844992 B6_F_T_15 [BS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD4+CD62L+ T cells", "strain": "C57BL/6J", "sex": "female", "cell_type": "CD4+CD62L+ T cells"}
SRX9333788 CD4+CD62L+ T cells 0.771 8.6 51677 1134.5 446 1063.7 1912 19473.4 0.999 title: GSM4844993 B6_F_T_14 [OxBS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD4+CD62L+ T cells", "strain": "C57BL/6J", "sex": "female", "cell_type": "CD4+CD62L+ T cells"}
SRX9333789 CD4+CD62L+ T cells 0.772 8.5 51421 1132.0 461 1082.2 2057 18805.5 0.999 title: GSM4844994 B6_F_T_16 [OxBS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD4+CD62L+ T cells", "strain": "C57BL/6J", "sex": "female", "cell_type": "CD4+CD62L+ T cells"}
SRX9333790 CD43-B cells 0.779 7.8 47489 1025.4 204 1076.5 1317 14082.5 0.996 title: GSM4844995 B6_M_B_1 [BS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD43-B cells", "strain": "C57BL/6J", "sex": "male", "cell_type": "CD43-B cells"}
SRX9333791 CD43-B cells 0.776 8.5 49754 1031.1 255 1018.9 1402 15480.6 0.998 title: GSM4844996 B6_M_B_4 [BS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD43-B cells", "strain": "C57BL/6J", "sex": "male", "cell_type": "CD43-B cells"}
SRX9333792 CD43-B cells 0.787 6.8 45843 1067.9 185 1046.9 800 20994.7 0.985 title: GSM4844997 B6_M_B_2 [OxBS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD43-B cells", "strain": "C57BL/6J", "sex": "male", "cell_type": "CD43-B cells"}
SRX9333793 CD43-B cells 0.796 7.1 47714 1078.3 119 1130.2 1309 15929.0 0.996 title: GSM4844998 B6_M_B_3 [OxBS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD43-B cells", "strain": "C57BL/6J", "sex": "male", "cell_type": "CD43-B cells"}
SRX9333794 CD4+CD62L+ T cells 0.781 8.3 49296 1037.2 273 967.0 1279 16279.9 0.997 title: GSM4844999 B6_M_T_5 [BS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD4+CD62L+ T cells", "strain": "C57BL/6J", "sex": "male", "cell_type": "CD4+CD62L+ T cells"}
SRX9333795 CD4+CD62L+ T cells 0.781 8.5 49949 1025.5 245 979.9 1280 16384.0 0.998 title: GSM4845000 B6_M_T_7 [BS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD4+CD62L+ T cells", "strain": "C57BL/6J", "sex": "male", "cell_type": "CD4+CD62L+ T cells"}
SRX9333796 CD4+CD62L+ T cells 0.772 8.2 51122 1126.4 226 1044.6 1908 19010.8 0.998 title: GSM4845001 B6_M_T_6 [OxBS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD4+CD62L+ T cells", "strain": "C57BL/6J", "sex": "male", "cell_type": "CD4+CD62L+ T cells"}
SRX9333797 CD4+CD62L+ T cells 0.774 8.6 52037 1116.0 219 1022.1 1919 19271.0 0.999 title: GSM4845002 B6_M_T_8 [OxBS-seq], Mus musculus, Bisulfite-Seq; {"source_name": "CD4+CD62L+ T cells", "strain": "C57BL/6J", "sex": "male", "cell_type": "CD4+CD62L+ T cells"}

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.