Mouse methylome studies SRP119705 Track Settings
 
DNA hypermethylation encroachment at CpG island borders in cancer is predisposed by H3K4 monomethylation [WGBS_Mm] [Blood (B Cell-Depleted), Spleen Bulk B Cells]

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 SRX4521697  HMR  Spleen Bulk B Cells / SRX4521697 (HMR)   Data format 
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 SRX4521697  CpG methylation  Spleen Bulk B Cells / SRX4521697 (CpG methylation)   Data format 
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 SRX4521700  CpG methylation  Blood (B Cell-Depleted) / SRX4521700 (CpG methylation)   Data format 
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Assembly: Mouse Jun. 2020 (GRCm39/mm39)

Study title: DNA hypermethylation encroachment at CpG island borders in cancer is predisposed by H3K4 monomethylation [WGBS_Mm]
SRA: SRP119705
GEO: GSE104781
Pubmed: 30753827

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX3267832 Spleen Bulk B Cells 0.751 10.7 43989 1052.3 283 976.5 1579 13407.4 0.998 GSM2807805: mBcells_KMT2D_WT_WGBS; Mus musculus; Bisulfite-Seq
SRX3267833 Spleen Bulk B Cells 0.747 14.4 46189 1001.3 441 2003.4 1624 13388.9 0.997 GSM2807806: mBcells_KMT2D_HET_WGBS; Mus musculus; Bisulfite-Seq
SRX3267834 Spleen Bulk B Cells 0.716 11.3 40406 1051.9 300 927.1 1597 12593.2 0.998 GSM2807807: mBcells_KMT2D_HOM_WGBS; Mus musculus; Bisulfite-Seq
SRX4521696 Spleen Bulk B Cells 0.761 24.1 61688 918.6 585 895.3 3493 9615.0 0.996 GSM3324664: mBcells_KMT2D_WT_WGBS_rep2; Mus musculus; Bisulfite-Seq
SRX4521697 Spleen Bulk B Cells 0.779 18.1 57095 937.3 394 896.7 3140 9457.1 0.994 GSM3324665: mBcells_KMT2D_HET_WGBS_rep2; Mus musculus; Bisulfite-Seq
SRX4521698 Spleen Bulk B Cells 0.765 21.6 55484 918.5 646 893.5 3052 8736.9 0.996 GSM3324666: mBcells_KMT2D_HOM_WGBS_rep2; Mus musculus; Bisulfite-Seq
SRX4521699 Blood (B Cell-Depleted) 0.767 9.1 46348 1081.1 242 2722.6 1607 14318.9 0.996 GSM3324667: mBlood_KMT2D_WT_WGBS; Mus musculus; Bisulfite-Seq
SRX4521700 Blood (B Cell-Depleted) 0.763 7.4 43361 1109.8 197 916.9 1210 19025.8 0.997 GSM3324668: mBlood_KMT2D_HET_WGBS; Mus musculus; Bisulfite-Seq
SRX4521701 Blood (B Cell-Depleted) 0.768 10.6 45041 1079.7 328 944.7 1945 15102.0 0.996 GSM3324669: mBlood_KMT2D_HOM_WGBS; Mus musculus; Bisulfite-Seq

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.