Mouse methylome studies SRP162035 Track Settings
 
Activin A and BMP4 Signaling Expands Potency of Mouse Embryonic Stem Cells in Serum Free Media [ESC, Epiblast Stem Cell]

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 SRX4703407  CpG methylation  ESC / SRX4703407 (CpG methylation)   Schema 
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 SRX4703408  CpG methylation  ESC / SRX4703408 (CpG methylation)   Schema 
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 SRX4703412  HMR  Epiblast Stem Cell / SRX4703412 (HMR)   Schema 
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 SRX4703411  CpG methylation  ESC / SRX4703411 (CpG methylation)   Schema 
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 SRX7130623  HMR  ESC / SRX7130623 (HMR)   Schema 
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 SRX4703412  CpG methylation  Epiblast Stem Cell / SRX4703412 (CpG methylation)   Schema 
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 SRX4703413  CpG methylation  Epiblast Stem Cell / SRX4703413 (CpG methylation)   Schema 
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 SRX4703414  CpG methylation  Epiblast Stem Cell / SRX4703414 (CpG methylation)   Schema 
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 SRX7130628  CpG methylation  ESC / SRX7130628 (CpG methylation)   Schema 
    

Study title: Activin A and BMP4 Signaling Expands Potency of Mouse Embryonic Stem Cells in Serum Free Media
SRA: SRP162035
GEO: GSE119985
Pubmed: 32032551

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX4703403 ESC 0.454 3.4 15248 8134.5 10 1153.4 749 125313.3 0.998 title: GSM3389851 2iL.ESCs.WGBS.r1, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "13"}
SRX4703404 ESC 0.444 3.8 17193 7507.3 17 945.2 863 117153.2 0.997 title: GSM3389852 2iL.ESCs.WGBS.r2, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "13"}
SRX4703405 ESC 0.434 4.3 21397 6335.7 15 1043.7 951 119943.5 0.998 title: GSM3389853 2iL.ESCs.WGBS.r3, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "13"}
SRX4703406 ESC 0.639 3.8 39858 1966.2 34 1010.0 1235 56273.8 0.995 title: GSM3389854 esASCs.WGBS.r1, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "12"}
SRX4703407 ESC 0.642 3.9 38302 2003.1 19 1086.7 1273 47739.2 0.995 title: GSM3389855 esASCs.WGBS.r2, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "12"}
SRX4703408 ESC 0.637 3.9 40233 1931.4 24 983.0 1401 45581.9 0.995 title: GSM3389856 esASCs.WGBS.r3, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "12"}
SRX4703409 ESC 0.630 3.6 39187 2239.3 13 1130.1 1287 55079.6 0.996 title: GSM3389857 epiASCs.WGBS.r1, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "13"}
SRX4703410 ESC 0.615 4.1 41254 2056.1 25 1045.9 1475 42935.0 0.996 title: GSM3389858 epiASCs.WGBS.r2, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "13"}
SRX4703411 ESC 0.638 4.4 43335 1980.3 23 1822.7 1698 34760.7 0.996 title: GSM3389859 epiASCs.WGBS.r3, Mus musculus, Bisulfite-Seq; {"source_name": "Embryonic stem cell", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "13"}
SRX4703412 Epiblast Stem Cell 0.763 4.3 23627 1422.6 57 1492.5 695 25023.7 0.995 title: GSM3389860 EpiSCs.WGBS.r1, Mus musculus, Bisulfite-Seq; {"source_name": "Epiblast stem cell", "strain": "129/Sv", "cell_type": "Epiblast stem cell", "passages": "13"}
SRX4703413 Epiblast Stem Cell 0.751 4.1 23949 1405.9 64 1116.7 519 30056.8 0.994 title: GSM3389861 EpiSCs.WGBS.r2, Mus musculus, Bisulfite-Seq; {"source_name": "Epiblast stem cell", "strain": "129/Sv", "cell_type": "Epiblast stem cell", "passages": "13"}
SRX4703414 Epiblast Stem Cell 0.750 3.8 23456 1423.7 56 1041.6 474 29344.6 0.994 title: GSM3389862 EpiSCs.WGBS.r3, Mus musculus, Bisulfite-Seq; {"source_name": "Epiblast stem cell", "strain": "129/Sv", "cell_type": "Epiblast stem cell", "passages": "13"}
SRX7130623 ESC 0.553 5.4 35231 2726.5 59 1084.6 1634 28011.1 0.996 title: GSM4157930 blASCs.WGBS.r1, Mus musculus, Bisulfite-Seq; {"source_name": "blASCs.WGBS", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "22", "cell_subtype": "blASCs"}
SRX7130624 ESC 0.545 7.1 46394 1932.3 326 818.6 2039 27184.3 0.987 title: GSM4157931 blASCs.WGBS.r2, Mus musculus, Bisulfite-Seq; {"source_name": "blASCs.WGBS", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "22", "cell_subtype": "blASCs"}
SRX7130625 ESC 0.573 5.1 33335 2666.4 55 1213.6 1240 40689.8 0.982 title: GSM4157932 blASCs.WGBS.r3, Mus musculus, Bisulfite-Seq; {"source_name": "blASCs.WGBS", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "24", "cell_subtype": "blASCs"}
SRX7130626 ESC 0.283 3.5 3 937677.3 10 939.6 0 0.0 0.997 title: GSM4157933 asESCs.WGBS.r1, Mus musculus, Bisulfite-Seq; {"source_name": "asESCs.WGBS", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "15", "cell_subtype": "asESCs"}
SRX7130627 ESC 0.282 3.5 1 1261085.0 11 963.4 2 15077797.0 0.997 title: GSM4157934 asESCs.WGBS.r2, Mus musculus, Bisulfite-Seq; {"source_name": "asESCs.WGBS", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "15", "cell_subtype": "asESCs"}
SRX7130628 ESC 0.283 3.2 2 938662.5 14 908.2 0 0.0 0.997 title: GSM4157935 asESCs.WGBS.r3, Mus musculus, Bisulfite-Seq; {"source_name": "asESCs.WGBS", "strain": "129/Sv", "cell_type": "Embryonic stem cell", "passages": "15", "cell_subtype": "asESCs"}

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.