Mouse methylome studies SRP106356 Track Settings
 
Activation of Lineage-Regulators and Transposable Elements Across a Pluripotent Spectrum [2i N2B27 BL6, 2i/LIF GMEM/10% KSR BL6, 2i/LIF GMEM/10% serum 129, 2i/LIF N2B27 BL6, Ch/LIF N2B27 BL6, Pd/LIF N2B27 BL6, Serum/LIF Feeders GMEM BL6, Serum/LIF GMEM 129]

Track collection: Mouse methylome studies

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 SRX2781612  CpG methylation  2i N2B27 BL6 / SRX2781612 (CpG methylation)   Schema 
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 SRX2781622  HMR  Pd/LIF N2B27 BL6 / SRX2781622 (HMR)   Schema 
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 SRX2781614  CpG methylation  2i/LIF GMEM/10% KSR BL6 / SRX2781614 (CpG methylation)   Schema 
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 SRX2781624  HMR  Serum/LIF Feeders GMEM BL6 / SRX2781624 (HMR)   Schema 
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 SRX2781615  CpG methylation  2i/LIF GMEM/10% serum 129 / SRX2781615 (CpG methylation)   Schema 
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 SRX2781627  HMR  Serum/LIF GMEM 129 / SRX2781627 (HMR)   Schema 
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 SRX2781618  CpG methylation  2i/LIF N2B27 BL6 / SRX2781618 (CpG methylation)   Schema 
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 SRX2781620  CpG methylation  Ch/LIF N2B27 BL6 / SRX2781620 (CpG methylation)   Schema 
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 SRX2781622  CpG methylation  Pd/LIF N2B27 BL6 / SRX2781622 (CpG methylation)   Schema 
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 SRX2781624  CpG methylation  Serum/LIF Feeders GMEM BL6 / SRX2781624 (CpG methylation)   Schema 
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 SRX2781627  CpG methylation  Serum/LIF GMEM 129 / SRX2781627 (CpG methylation)   Schema 
    

Study title: Activation of Lineage-Regulators and Transposable Elements Across a Pluripotent Spectrum
SRA: SRP106356
GEO: GSE98517
Pubmed: 28591649

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX2781612 2i N2B27 BL6 0.267 1.7 1 915240.0 3 1004.3 0 0.0 0.996 title: GSM2598128 2i – N2B27_BL6_MethylC-seq, Mus musculus, Bisulfite-Seq; {"source_name": "2i N2B27_BL6", "strain": "C57BL6/J", "passages": "13"}
SRX2781614 2i/LIF GMEM/10% KSR BL6 0.332 1.6 2 835635.0 0 0.0 0 0.0 0.994 title: GSM2598130 2i/LIF – GMEM/10% KSR_BL6_MethylC-seq, Mus musculus, Bisulfite-Seq; {"source_name": "2i/LIF GMEM/10% KSR_BL6", "strain": "C57BL6/J", "passages": "13"}
SRX2781615 2i/LIF GMEM/10% serum 129 0.461 1.9 2 415352.0 1 925.0 1 19087263.0 0.981 title: GSM2598131 2i/LIF – GMEM/10% serum_129_MethylC-seq, Mus musculus, Bisulfite-Seq; {"source_name": "2i/LIF GMEM/10% serum_129", "strain": "129X1/SvJ", "passages": "16"}
SRX2781618 2i/LIF N2B27 BL6 0.173 2.0 1 1049750.0 1 1855.0 2 105295771.0 0.996 title: GSM2598134 2i/LIF – N2B27_BL6_MethylC-seq, Mus musculus, Bisulfite-Seq; {"source_name": "2i/LIF N2B27_BL6", "strain": "C57BL6/J", "passages": "13"}
SRX2781620 Ch/LIF N2B27 BL6 0.663 1.6 28227 3100.6 2 901.5 657 177494.6 0.994 title: GSM2598136 Ch/LIF – N2B27_BL6_MethylC-seq, Mus musculus, Bisulfite-Seq; {"source_name": "Ch/LIF N2B27_BL6", "strain": "C57BL6/J", "passages": "13"}
SRX2781622 Pd/LIF N2B27 BL6 0.515 1.6 33691 849.4 7 1074.3 178 139332.9 0.993 title: GSM2598138 Pd/LIF – N2B27_BL6_MethylC-seq, Mus musculus, Bisulfite-Seq; {"source_name": "Pd/LIF N2B27_BL6", "strain": "C57BL6/J", "passages": "13"}
SRX2781624 Serum/LIF Feeders GMEM BL6 0.640 1.7 25100 2735.2 9 1303.1 442 109058.6 0.994 title: GSM2598140 Serum/LIF – Feeders - GMEM_BL6_MethylC-seq, Mus musculus, Bisulfite-Seq; {"source_name": "Serum/LIF Feeders GMEM_BL6", "strain": "C57BL6/J", "passages": "13"}
SRX2781627 Serum/LIF GMEM 129 0.801 1.8 21446 1634.7 2 1027.0 308 149097.7 0.974 title: GSM2598143 Serum/LIF -GMEM_129_MethylC-seq, Mus musculus, Bisulfite-Seq; {"source_name": "Serum/LIF GMEM_129", "strain": "129X1/SvJ", "passages": "16"}

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.