Mouse methylome studies SRP526448 Track Settings
 
Epigenetic regulators of clonal hematopoiesis control CD8 T cell stemness during immunotherapy [Bisulfite-Seq] [Spleen]

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Study title: Epigenetic regulators of clonal hematopoiesis control CD8 T cell stemness during immunotherapy [Bisulfite-Seq]
SRA: SRP526448
GEO: not found
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX25704498 Spleen 0.695 29.5 45127 1068.5 581 1005.7 1370 10290.4 0.977 title: GSM8459561 Rosa D30 rep1, WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "Spleen", "tissue": "Spleen", "cell_type": "CD8 T cells", "genotype": "CD8+CD90.1+", "genetic_mod": "Rosa KO", "geo_loc_name": "missing", "collection_date": "missing"}
SRX25704499 Spleen 0.698 13.0 38781 1207.4 420 1030.8 963 12682.9 0.979 title: GSM8459562 Rosa D30 rep2, WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "Spleen", "tissue": "Spleen", "cell_type": "CD8 T cells", "genotype": "CD8+CD90.1+", "genetic_mod": "Rosa KO", "geo_loc_name": "missing", "collection_date": "missing"}
SRX25704500 Spleen 0.654 14.5 44618 1215.4 743 1038.0 1319 13387.7 0.981 title: GSM8459563 Dnmt3a D30 rep1, WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "Spleen", "tissue": "Spleen", "cell_type": "CD8 T cells", "genotype": "CD8+CD90.1+", "genetic_mod": "Dnmt3a KO", "geo_loc_name": "missing", "collection_date": "missing"}
SRX25704501 Spleen 0.688 29.0 51315 1109.4 616 950.5 2693 9267.4 0.979 title: GSM8459564 Dnmt3a D30 rep2, WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "Spleen", "tissue": "Spleen", "cell_type": "CD8 T cells", "genotype": "CD8+CD90.1+", "genetic_mod": "Dnmt3a KO", "geo_loc_name": "missing", "collection_date": "missing"}
SRX25704502 Spleen 0.689 31.1 40456 1132.9 534 972.5 909 12215.5 0.979 title: GSM8459565 Tet2 D30 rep1, WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "Spleen", "tissue": "Spleen", "cell_type": "CD8 T cells", "genotype": "CD8+CD90.1+", "genetic_mod": "Tet2 KO", "geo_loc_name": "missing", "collection_date": "missing"}
SRX25704503 Spleen 0.689 30.1 40381 1112.4 538 999.4 1160 10685.3 0.979 title: GSM8459566 Tet2 D30 rep2, WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "Spleen", "tissue": "Spleen", "cell_type": "CD8 T cells", "genotype": "CD8+CD90.1+", "genetic_mod": "Tet2 KO", "geo_loc_name": "missing", "collection_date": "missing"}
SRX25704504 Spleen 0.693 26.7 44661 1083.4 494 990.9 1754 9030.8 0.980 title: GSM8459567 Asxl1 D30 rep1, WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "Spleen", "tissue": "Spleen", "cell_type": "CD8 T cells", "genotype": "CD8+CD90.1+", "genetic_mod": "Asxl1 KO", "geo_loc_name": "missing", "collection_date": "missing"}
SRX25704505 Spleen 0.693 26.9 44382 1093.5 506 993.2 1705 9284.0 0.976 title: GSM8459568 Asxl1 D30 rep2, WGBS, Mus musculus, Bisulfite-Seq; {"source_name": "Spleen", "tissue": "Spleen", "cell_type": "CD8 T cells", "genotype": "CD8+CD90.1+", "genetic_mod": "Asxl1 KO", "geo_loc_name": "missing", "collection_date": "missing"}

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.