Mouse methylome studies DRP003273 Track Settings
 
Methylome and transcriptome dynamics during mouse germ cell specification/differentiation in vitro

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 DRX029829  CpG methylation  DRS034094 / DRX029829 (CpG methylation)   Schema 
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 DRX029833  CpG methylation  DRS034098 / DRX029833 (CpG methylation)   Schema 
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 DRX029841  HMR  DRS034106 / DRX029841 (HMR)   Schema 
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 DRX029837  CpG methylation  DRS034102 / DRX029837 (CpG methylation)   Schema 
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 DRX029843  HMR  DRS034108 / DRX029843 (HMR)   Schema 
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 DRX029839  CpG methylation  DRS034104 / DRX029839 (CpG methylation)   Schema 
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 DRX029841  CpG methylation  DRS034106 / DRX029841 (CpG methylation)   Schema 
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 DRX029847  HMR  DRS034112 / DRX029847 (HMR)   Schema 
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 DRX029843  CpG methylation  DRS034108 / DRX029843 (CpG methylation)   Schema 
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 DRX029849  CpG methylation  DRS034114 / DRX029849 (CpG methylation)   Schema 
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Study title: Methylome and transcriptome dynamics during mouse germ cell specification/differentiation in vitro
SRA: DRP003273
GEO: not found
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
DRX029817 DRS034088 0.464 17.7 68375 1857.1 620 1162.4 5375 24659.1 0.993 title: Illumina HiSeq 2500 sequencing of SAMD00028585; {}
DRX029819 DRS034090 0.718 15.9 37401 1180.0 737 1073.3 2035 13081.9 0.983 title: Illumina HiSeq 2500 sequencing of SAMD00028587; {}
DRX029821 DRS034092 0.366 15.8 26272 1411.2 744 1015.8 324 1775680.1 0.994 title: Illumina HiSeq 2500 sequencing of SAMD00028589; {}
DRX029829 DRS034094 0.195 15.5 20766 11765.4 16 804.6 30 1337980.5 0.994 title: Illumina HiSeq 2500 sequencing of SAMD00028597; {}
DRX029831 DRS034096 0.593 14.8 33670 1234.5 270 924.1 2633 35848.8 0.988 title: Illumina HiSeq 2500 sequencing of SAMD00028599; {}
DRX029833 DRS034098 0.148 15.8 0 0.0 133 874.5 52 1879578.0 0.994 title: Illumina HiSeq 2500 sequencing of SAMD00028601; {}
DRX029835 DRS034100 0.713 14.0 52258 1313.1 302 963.7 3554 15303.3 0.978 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028603; {}
DRX029837 DRS034102 0.770 14.2 37694 1082.5 275 988.9 1934 10428.8 0.983 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028605; {}
DRX029839 DRS034104 0.774 16.8 36245 1030.1 364 998.1 1637 10701.1 0.981 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028607; {}
DRX029841 DRS034106 0.700 18.3 38242 972.6 302 2461.8 1877 9712.6 0.990 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028609; {}
DRX029843 DRS034108 0.634 28.3 42569 961.0 385 2092.5 3647 7420.1 0.991 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028611; {}
DRX029845 DRS034110 0.539 17.7 70202 1591.4 211 966.8 3401 26737.9 0.991 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028613; {}
DRX029847 DRS034112 0.682 16.4 43307 1248.1 326 965.1 2158 19181.4 0.984 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028615; {}
DRX029849 DRS034114 0.675 17.6 39551 1061.9 267 1001.6 1855 12134.0 0.987 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028617; {}
DRX029851 DRS034116 0.466 26.9 33451 1069.2 545 968.4 1803 7712.1 0.991 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028619; {}
DRX029853 DRS034118 0.349 25.4 29082 1297.9 792 933.3 520 1682152.2 0.991 title: Illumina HiSeq 2500 paired end sequencing of SAMD00028621; {}

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.