Human methylome studies SRP510440 Track Settings
 
Tracking and mitigating imprint erasure during induction of naïve human pluripotency at single-cell resolution [Bisulfite-Seq] [SRS21455179, SRS21455180, SRS21455181, SRS21455182, SRS21455183, SRS21455184, SRS21455185, SRS21455186, SRS21455187, SRS21455188, SRS21455189, SRS21455190, SRS21455191, SRS21455192]

Track collection: Human methylome studies

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 SRX24732801  HMR  SRS21455179 / SRX24732801 (HMR)   Data format 
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 SRX24732801  CpG methylation  SRS21455179 / SRX24732801 (CpG methylation)   Data format 
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 SRX24732802  HMR  SRS21455180 / SRX24732802 (HMR)   Data format 
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 SRX24732802  CpG methylation  SRS21455180 / SRX24732802 (CpG methylation)   Data format 
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 SRX24732803  HMR  SRS21455181 / SRX24732803 (HMR)   Data format 
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 SRX24732803  CpG methylation  SRS21455181 / SRX24732803 (CpG methylation)   Data format 
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 SRX24732804  CpG methylation  SRS21455182 / SRX24732804 (CpG methylation)   Data format 
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 SRX24732805  CpG methylation  SRS21455183 / SRX24732805 (CpG methylation)   Data format 
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 SRX24732806  HMR  SRS21455184 / SRX24732806 (HMR)   Data format 
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 SRX24732806  CpG methylation  SRS21455184 / SRX24732806 (CpG methylation)   Data format 
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 SRX24732807  HMR  SRS21455185 / SRX24732807 (HMR)   Data format 
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 SRX24732807  CpG methylation  SRS21455185 / SRX24732807 (CpG methylation)   Data format 
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 SRX24732808  HMR  SRS21455186 / SRX24732808 (HMR)   Data format 
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 SRX24732808  CpG methylation  SRS21455186 / SRX24732808 (CpG methylation)   Data format 
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 SRX24732809  HMR  SRS21455187 / SRX24732809 (HMR)   Data format 
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 SRX24732809  CpG methylation  SRS21455187 / SRX24732809 (CpG methylation)   Data format 
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 SRX24732810  HMR  SRS21455188 / SRX24732810 (HMR)   Data format 
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 SRX24732810  CpG methylation  SRS21455188 / SRX24732810 (CpG methylation)   Data format 
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 SRX24732811  HMR  SRS21455189 / SRX24732811 (HMR)   Data format 
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 SRX24732811  CpG methylation  SRS21455189 / SRX24732811 (CpG methylation)   Data format 
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 SRX24732812  HMR  SRS21455190 / SRX24732812 (HMR)   Data format 
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 SRX24732812  CpG methylation  SRS21455190 / SRX24732812 (CpG methylation)   Data format 
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 SRX24732813  HMR  SRS21455191 / SRX24732813 (HMR)   Data format 
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 SRX24732813  CpG methylation  SRS21455191 / SRX24732813 (CpG methylation)   Data format 
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 SRX24732814  HMR  SRS21455192 / SRX24732814 (HMR)   Data format 
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 SRX24732814  CpG methylation  SRS21455192 / SRX24732814 (CpG methylation)   Data format 
    
Assembly: Human Dec. 2013 (GRCh38/hg38)

Study title: Tracking and mitigating imprint erasure during induction of naïve human pluripotency at single-cell resolution [Bisulfite-Seq]
SRA: SRP510440
GEO: not found
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX24732801 SRS21455179 0.809 21.5 52236 1259.7 1123 1277.3 3537 50815.2 0.979 GSM8293988: WGBS_H9-SRG_Primed; Homo sapiens; Bisulfite-Seq
SRX24732802 SRS21455180 0.555 19.4 51699 2113.4 1247 929.1 4186 56163.5 0.973 GSM8293989: WGBS_H9-SRG_Naïve_P1_R+G-; Homo sapiens; Bisulfite-Seq
SRX24732803 SRS21455181 0.479 24.9 59591 3451.3 577 912.7 4259 142099.5 0.976 GSM8293990: WGBS_H9-SRG_Naïve_P1_R+G+; Homo sapiens; Bisulfite-Seq
SRX24732804 SRS21455182 0.354 20.0 72122 10074.8 198 875.8 5357 164419.5 0.979 GSM8293991: WGBS_H9-SRG_Naïve_P5_R+G-; Homo sapiens; Bisulfite-Seq
SRX24732805 SRS21455183 0.351 26.4 76064 9806.0 241 859.6 5635 157671.6 0.980 GSM8293992: WGBS_H9-SRG_Naïve_P5_R+G+; Homo sapiens; Bisulfite-Seq
SRX24732806 SRS21455184 0.814 23.1 44204 1118.5 1047 987.3 4319 19104.1 0.977 GSM8293993: WGBS_H9-SRG_Re-primed_R+G-; Homo sapiens; Bisulfite-Seq
SRX24732807 SRS21455185 0.796 22.0 43500 1110.7 1101 1061.3 4317 16881.0 0.977 GSM8293994: WGBS_H9-SRG_Re-primed_R+G+; Homo sapiens; Bisulfite-Seq
SRX24732808 SRS21455186 0.810 21.4 46220 1110.7 1563 1048.1 4554 9798.0 0.974 GSM8293995: WGBS_H9-SRG_1.0MEKi_2.5ERKi_d7_R+G-_Re-primed; Homo sapiens; Bisulfite-Seq
SRX24732809 SRS21455187 0.805 21.6 46774 1098.5 1296 1094.0 4587 10163.9 0.974 GSM8293996: WGBS_H9-SRG_0.5MEKi_0.5ERKi_d17_R+G-_Re-primed; Homo sapiens; Bisulfite-Seq
SRX24732810 SRS21455188 0.807 22.6 47171 1096.9 1304 1105.7 4562 10053.0 0.973 GSM8293997: WGBS_H9-SRG_1.0MEKi_0.1ERKi_d17_R+G-_Re-primed; Homo sapiens; Bisulfite-Seq
SRX24732811 SRS21455189 0.551 18.8 53561 2138.1 1579 931.5 2606 55436.5 0.973 GSM8293998: WGBS_H9-SRG_Naïve_rtTA; Homo sapiens; Bisulfite-Seq
SRX24732812 SRS21455190 0.603 17.9 48927 2027.4 1876 986.8 3036 54460.5 0.971 GSM8293999: WGBS_H9-SRG_Naïve_ZFP57_OE; Homo sapiens; Bisulfite-Seq
SRX24732813 SRS21455191 0.544 19.1 44778 1864.0 2683 7677.6 3538 50269.9 0.974 GSM8294000: WGBS_H9_Naïve_rtTA; Homo sapiens; Bisulfite-Seq
SRX24732814 SRS21455192 0.563 17.6 43131 1588.0 2436 998.1 3031 36296.0 0.975 GSM8294001: WGBS_H9_Naïve_ZFP57_OE; 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.