Mouse methylome studies SRP115607 Track Settings
 
A role of Polycomb in the maintenance of hypomethylation for DNA methylation valley [ESC]

Track collection: Mouse methylome studies

+  All tracks in this collection (604)

Maximum display mode:       Reset to defaults   
Select views (Help):
AMR       HMR       CpG methylation ▾       PMD       CpG reads ▾      
Select subtracks by views and experiment:
 All views AMR  HMR  CpG methylation  PMD  CpG reads 
experiment
SRX3100179 
SRX3100182 
SRX3100183 
SRX3100184 
List subtracks: only selected/visible    all    ()
  experiment↓1 views↓2   Track Name↓3  
hide
 SRX3100179  AMR  ESC / SRX3100179 (AMR)   Schema 
hide
 SRX3100179  HMR  ESC / SRX3100179 (HMR)   Schema 
hide
 Configure
 SRX3100179  CpG methylation  ESC / SRX3100179 (CpG methylation)   Schema 
hide
 SRX3100179  PMD  ESC / SRX3100179 (PMD)   Schema 
hide
 Configure
 SRX3100179  CpG reads  ESC / SRX3100179 (CpG reads)   Schema 
hide
 SRX3100182  HMR  ESC / SRX3100182 (HMR)   Schema 
hide
 Configure
 SRX3100182  CpG methylation  ESC / SRX3100182 (CpG methylation)   Schema 
hide
 SRX3100182  PMD  ESC / SRX3100182 (PMD)   Schema 
hide
 Configure
 SRX3100182  CpG reads  ESC / SRX3100182 (CpG reads)   Schema 
hide
 SRX3100183  AMR  ESC / SRX3100183 (AMR)   Schema 
hide
 SRX3100183  HMR  ESC / SRX3100183 (HMR)   Schema 
hide
 Configure
 SRX3100183  CpG methylation  ESC / SRX3100183 (CpG methylation)   Schema 
hide
 SRX3100183  PMD  ESC / SRX3100183 (PMD)   Schema 
hide
 Configure
 SRX3100183  CpG reads  ESC / SRX3100183 (CpG reads)   Schema 
hide
 SRX3100184  AMR  ESC / SRX3100184 (AMR)   Schema 
hide
 SRX3100184  HMR  ESC / SRX3100184 (HMR)   Schema 
hide
 Configure
 SRX3100184  CpG methylation  ESC / SRX3100184 (CpG methylation)   Schema 
hide
 SRX3100184  PMD  ESC / SRX3100184 (PMD)   Schema 
hide
 Configure
 SRX3100184  CpG reads  ESC / SRX3100184 (CpG reads)   Schema 
    

Study title: A role of Polycomb in the maintenance of hypomethylation for DNA methylation valley
SRA: SRP115607
GEO: GSE102753
Pubmed: 29422066

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX3100179 ESC 0.695 9.0 42234 1512.4 90 983.4 2220 26662.2 0.989 title: GSM2746084 Met-seq of Eed -/- mESC, Mus musculus, Bisulfite-Seq; {"source_name": "Met-seq of Eed /-mESC", "strain": "l7Rn5-3354SB", "genotype": "Eed /-", "cell_type": "mESCs"}
SRX3100182 ESC 0.672 2.0 28848 2485.3 0 0.0 315 234879.6 0.991 title: GSM2746087 Met-seq of TetWT mESC, Mus musculus, Bisulfite-Seq; {"source_name": "Met-seq of TetWT mESC", "strain": "R1", "genotype": "WT", "cell_type": "mESCs"}
SRX3100183 ESC 0.755 2.4 25018 1529.6 53 1207.4 358 118673.4 0.991 title: GSM2746088 Met-seq of Tet TKO mESC, Mus musculus, Bisulfite-Seq; {"source_name": "Met-seq of Tet TKO mESC", "strain": "R1", "genotype": "Tet1-/-Tet2-/-Tet3-/-", "cell_type": "mESCs"}
SRX3100184 ESC 0.601 3.6 21607 2232.3 251 1081.5 234 64702.2 0.990 title: GSM2746089 Met-seq of Tet/Eed QKO mESC, Mus musculus, Bisulfite-Seq; {"source_name": "Met-seq of Tet/Eed QKO mESC", "strain": "R1", "genotype": "Tet1-/-Tet2-/-Tet3-/-Eed-/-", "cell_type": "mESCs"}

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.