Mouse methylome studies SRP125320 Track Settings
 
Effector CD8 T cells dedifferentiate into long-lived memory cells [Acute GP33-specific CD8 T Cells, Acute Memory Precursor P14 Cells, Acute Terminal Effector P14 Cells, P14 CD8 T Cells]

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 SRX3409975  HMR  P14 CD8 T Cells / SRX3409975 (HMR)   Data format 
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 SRX3409975  CpG methylation  P14 CD8 T Cells / SRX3409975 (CpG methylation)   Data format 
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 SRX3409976  HMR  Acute Terminal Effector P14 Cells / SRX3409976 (HMR)   Data format 
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 SRX3409976  CpG methylation  Acute Terminal Effector P14 Cells / SRX3409976 (CpG methylation)   Data format 
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 SRX3409977  HMR  Acute Terminal Effector P14 Cells / SRX3409977 (HMR)   Data format 
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 SRX3409977  CpG methylation  Acute Terminal Effector P14 Cells / SRX3409977 (CpG methylation)   Data format 
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 SRX3409978  HMR  Acute Memory Precursor P14 Cells / SRX3409978 (HMR)   Data format 
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 SRX3409978  CpG methylation  Acute Memory Precursor P14 Cells / SRX3409978 (CpG methylation)   Data format 
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 SRX3409979  HMR  Acute Memory Precursor P14 Cells / SRX3409979 (HMR)   Data format 
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 SRX3409979  CpG methylation  Acute Memory Precursor P14 Cells / SRX3409979 (CpG methylation)   Data format 
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 SRX3409980  HMR  Acute GP33-specific CD8 T Cells / SRX3409980 (HMR)   Data format 
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 SRX3409980  CpG methylation  Acute GP33-specific CD8 T Cells / SRX3409980 (CpG methylation)   Data format 
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 SRX3409981  HMR  Acute GP33-specific CD8 T Cells / SRX3409981 (HMR)   Data format 
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 SRX3409981  CpG methylation  Acute GP33-specific CD8 T Cells / SRX3409981 (CpG methylation)   Data format 
    
Assembly: Mouse Jun. 2020 (GRCm39/mm39)

Study title: Effector CD8 T cells dedifferentiate into long-lived memory cells
SRA: SRP125320
GEO: GSE107150
Pubmed: 29236683

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX3409975 P14 CD8 T Cells 0.793 12.3 55605 972.5 591 1028.7 1024 18770.9 0.983 GSM2861720: Naïve_P14; Mus musculus; Bisulfite-Seq
SRX3409976 Acute Terminal Effector P14 Cells 0.715 6.0 35020 1335.6 344 1085.2 464 27919.0 0.986 GSM2861721: D4.5 TE_P14; Mus musculus; Bisulfite-Seq
SRX3409977 Acute Terminal Effector P14 Cells 0.698 12.8 39816 1167.7 678 1073.0 592 18024.2 0.986 GSM2861722: D8 TE_P14; Mus musculus; Bisulfite-Seq
SRX3409978 Acute Memory Precursor P14 Cells 0.721 12.2 41535 1132.1 644 1089.8 611 18576.1 0.986 GSM2861723: D8 MP_P14; Mus musculus; Bisulfite-Seq
SRX3409979 Acute Memory Precursor P14 Cells 0.723 10.4 41712 1156.2 613 1044.1 713 19001.3 0.987 GSM2861724: D4.5 MP_P14; Mus musculus; Bisulfite-Seq
SRX3409980 Acute GP33-specific CD8 T Cells 0.699 16.6 43592 1084.2 684 1063.7 1378 11307.2 0.986 GSM2861725: D8_WT_Acute; Mus musculus; Bisulfite-Seq
SRX3409981 Acute GP33-specific CD8 T Cells 0.694 12.4 44267 1192.9 600 1043.8 1255 15711.7 0.986 GSM2861726: D8_Dnmt3a cKO_Acute; 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.