Mouse methylome studies SRP414189 Track Settings
 
TET activity propagates transcriptional memory through embryonic dormancy [WGBS] [ESC]

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Study title: TET activity propagates transcriptional memory through embryonic dormancy [WGBS]
SRA: SRP414189
GEO: GSE221469
Pubmed: 38783076

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX18798028 ESC 0.376 26.1 43494 1920.0 1384 927.7 3057 25537.8 0.983 title: GSM6873765 WBGS_WT_N_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E14 wild-type, 0 hours", "tissue": "embryonic stem cell", "cell_line": "E14 wild-type", "treatment": "control"}
SRX18798029 ESC 0.402 26.6 41075 1897.2 665 870.6 3210 25557.5 0.982 title: GSM6873766 WBGS_WT_N_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E14 wild-type, 0 hours", "tissue": "embryonic stem cell", "cell_line": "E14 wild-type", "treatment": "control"}
SRX18798030 ESC 0.371 23.3 40755 1924.7 891 904.5 2413 31175.4 0.983 title: GSM6873767 WBGS_WT_24h_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E14 wild-type, 24 hours", "tissue": "embryonic stem cell", "cell_line": "E14 wild-type", "treatment": "mTORi"}
SRX18798031 ESC 0.382 28.5 39601 2348.9 187 891.7 4331 52343.7 0.981 title: GSM6873768 WBGS_WT_24h_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E14 wild-type, 24 hours", "tissue": "embryonic stem cell", "cell_line": "E14 wild-type", "treatment": "mTORi"}
SRX18798032 ESC 0.518 23.6 47952 1697.7 303 1016.0 3724 20770.1 0.981 title: GSM6873769 WBGS_WT_72h_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E14 wild-type, 72 hours", "tissue": "embryonic stem cell", "cell_line": "E14 wild-type", "treatment": "mTORi"}
SRX18798033 ESC 0.510 24.7 45310 1818.9 152 876.5 4997 29011.2 0.981 title: GSM6873770 WBGS_WT_72h_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E14 wild-type, 72 hours", "tissue": "embryonic stem cell", "cell_line": "E14 wild-type", "treatment": "mTORi"}
SRX18798034 ESC 0.653 23.0 55180 1532.0 392 998.5 4453 14648.4 0.979 title: GSM6873771 WBGS_WT_144h_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E14 wild-type, 144 hours", "tissue": "embryonic stem cell", "cell_line": "E14 wild-type", "treatment": "mTORi"}
SRX18798035 ESC 0.653 22.7 52848 1539.8 302 981.5 4462 14837.2 0.980 title: GSM6873772 WBGS_WT_144h_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E14 wild-type, 144 hours", "tissue": "embryonic stem cell", "cell_line": "E14 wild-type", "treatment": "mTORi"}
SRX18798036 ESC 0.264 29.4 35469 6891.6 223 857.2 59 1003490.6 0.983 title: GSM6873773 WBGS_KO_N_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E14 TET1/2 DKO, 0 hours", "tissue": "embryonic stem cell", "cell_line": "E14 TET1/2 DKO", "treatment": "control"}
SRX18798037 ESC 0.354 27.9 42390 4166.0 135 809.8 4481 86382.1 0.983 title: GSM6873774 WBGS_KO_N_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E14 TET1/2 DKO, 0 hours", "tissue": "embryonic stem cell", "cell_line": "E14 TET1/2 DKO", "treatment": "control"}
SRX18798038 ESC 0.265 23.6 33274 7263.0 147 869.8 1 1866709.0 0.983 title: GSM6873775 WBGS_KO_24h_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E14 TET1/2 DKO, 24 hours", "tissue": "embryonic stem cell", "cell_line": "E14 TET1/2 DKO", "treatment": "mTORi"}
SRX18798039 ESC 0.338 20.7 40288 5647.6 79 812.7 4488 100260.5 0.983 title: GSM6873776 WBGS_KO_24h_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E14 TET1/2 DKO, 24 hours", "tissue": "embryonic stem cell", "cell_line": "E14 TET1/2 DKO", "treatment": "mTORi"}
SRX18798040 ESC 0.414 22.4 43468 4316.3 143 856.0 4831 84336.3 0.982 title: GSM6873777 WBGS_KO_72h_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "E14 TET1/2 DKO, 72 hours", "tissue": "embryonic stem cell", "cell_line": "E14 TET1/2 DKO", "treatment": "mTORi"}
SRX18798041 ESC 0.479 25.3 45550 3791.2 98 868.7 4595 92368.5 0.983 title: GSM6873778 WBGS_KO_72h_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "E14 TET1/2 DKO, 72 hours", "tissue": "embryonic stem cell", "cell_line": "E14 TET1/2 DKO", "treatment": "mTORi"}

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.