Human methylome studies SRP268755 Track Settings
 
Probing the signaling requirements for naïve human pluripotency by high-throughput chemical screening [WGBS] [Naive hESC, Naïve hESC, Primed hESC]

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 SRX10279984  CpG methylation  Naïve hESC / SRX10279984 (CpG methylation)   Data format 
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 SRX8614755  HMR  Primed hESC / SRX8614755 (HMR)   Data format 
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 SRX8614757  CpG methylation  Naive hESC / SRX8614757 (CpG methylation)   Data format 
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 SRX8614759  CpG methylation  Naive hESC / SRX8614759 (CpG methylation)   Data format 
    
Assembly: Human Dec. 2013 (GRCh38/hg38)

Study title: Probing the signaling requirements for naïve human pluripotency by high-throughput chemical screening [WGBS]
SRA: SRP268755
GEO: GSE153213
Pubmed: 34133938

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX10279978 Naïve hESC 0.334 10.7 57669 9869.2 117 872.1 3357 236491.8 0.982 GSM5145217: WGBS 5i/L/A; Homo sapiens; Bisulfite-Seq
SRX10279979 Naïve hESC 0.403 18.7 70798 7196.4 161 872.9 4803 149526.2 0.980 GSM5145218: WGBS FXGY; Homo sapiens; Bisulfite-Seq
SRX10279980 Naïve hESC 0.488 17.0 68541 5154.0 274 900.8 4468 154292.1 0.980 GSM5145219: WGBS PXGY; Homo sapiens; Bisulfite-Seq
SRX10279981 Naïve hESC 0.471 17.4 61385 4038.8 278 911.0 4443 143958.0 0.978 GSM5145220: WGBS AXGY; Homo sapiens; Bisulfite-Seq
SRX10279982 Naïve hESC 0.507 19.2 72813 6280.6 353 930.4 4469 167424.9 0.980 GSM5145221: WGBS GXGY; Homo sapiens; Bisulfite-Seq
SRX10279983 Naïve hESC 0.356 17.1 67468 10193.9 236 1209.6 4802 177228.5 0.972 GSM5145222: WGBS PXGGY; Homo sapiens; Bisulfite-Seq
SRX10279984 Naïve hESC 0.350 24.6 73997 9569.5 245 918.0 5209 165855.7 0.983 GSM5145223: WGBS PXGGY/A; Homo sapiens; Bisulfite-Seq
SRX8614755 Primed hESC 0.736 3.4 28008 1441.8 407 1102.3 581 53282.1 0.978 GSM4635941: WGBS H9 mTeSR1; Homo sapiens; Bisulfite-Seq
SRX8614756 Naive hESC 0.351 3.9 12 94611.2 855 859.5 1219 580360.8 0.983 GSM4635942: WGBS H9 5i/L/A; Homo sapiens; Bisulfite-Seq
SRX8614757 Naive hESC 0.514 3.6 24011 4354.9 606 868.2 1024 428796.6 0.977 GSM4635943: WGBS H9 a5i/L/A; Homo sapiens; Bisulfite-Seq
SRX8614758 Naive hESC 0.473 5.1 21011 5425.6 887 904.0 1172 345605.0 0.979 GSM4635944: WGBS H9 AXGY; Homo sapiens; Bisulfite-Seq
SRX8614759 Naive hESC 0.453 4.3 17663 6162.2 989 904.5 874 308365.6 0.978 GSM4635945: WGBS H9 AXGYU; Homo sapiens; 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.