Mouse methylome studies SRP069154 Track Settings
 
Isoform switch of TET1 regulates DNA demethylation and mouse development [ESC]

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Study title: Isoform switch of TET1 regulates DNA demethylation and mouse development
SRA: SRP069154
GEO: GSE77453
Pubmed: 27916660

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX1556196 ESC 0.595 2.4 22911 2531.8 27 1404.1 59 144105.5 0.988 title: GSM2052292 mESC_Tet2ko_mCG Met-seq, Mus musculus, Bisulfite-Seq; {"source_name": "mESCs", "cell_type": "mESCs", "strain": "R1 129/Sv", "chip_antibody": "none", "genotype": "Tet2-/-"}
SRX1556197 ESC 0.769 2.5 27249 1719.2 30 1087.1 229 117236.1 0.988 title: GSM2052293 mESC_Tetdko_mCG Met-seq, Mus musculus, Bisulfite-Seq; {"source_name": "mESCs", "cell_type": "mESCs", "strain": "R1 129/Sv", "chip_antibody": "none", "genotype": "Tet1-/-Tet2-/-"}
SRX1556198 ESC 0.641 2.5 24961 2009.1 48 1249.2 207 87586.6 0.981 title: GSM2052294 mESC_Tet2ko_Tet1cs_mCG Met-seq, Mus musculus, Bisulfite-Seq; {"source_name": "mESCs", "cell_type": "mESCs", "strain": "R1 129/Sv", "chip_antibody": "none", "genotype": "Tet1cs/cs Tet2-/-"}
SRX2002361 ESC 0.614 3.5 28396 1972.8 203 1089.3 124 123236.6 0.991 title: GSM2262441 mESC_Tet2ko_mCG_rep2 Met-seq, Mus musculus, Bisulfite-Seq; {"source_name": "mESCs", "cell_type": "mESCs", "strain": "R1 129/Sv", "chip_antibody": "none", "genotype": "Tet2-/-"}
SRX2002362 ESC 0.735 2.6 26513 1644.1 169 1085.5 215 100082.7 0.991 title: GSM2262442 mESC_Tetdko_mCG_rep2 Met-seq, Mus musculus, Bisulfite-Seq; {"source_name": "mESCs", "cell_type": "mESCs", "strain": "R1 129/Sv", "chip_antibody": "none", "genotype": "Tet1-/-Tet2-/-"}
SRX2002363 ESC 0.659 4.4 29224 1542.8 206 1176.7 178 70276.1 0.980 title: GSM2262443 mESC_Tet2ko_Tet1cs_mCG_rep2 Met-seq, Mus musculus, Bisulfite-Seq; {"source_name": "mESCs", "cell_type": "mESCs", "strain": "R1 129/Sv", "chip_antibody": "none", "genotype": "Tet1cs/cs Tet2-/-"}

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.