Mouse methylome studies SRP417057 Track Settings
 
Whole-genome bisulfite sequencing analysis for intestinal budding organoids derived from induced or tissue-derived intestinal stem cells, and for mouse embryonic fibroblasts [Embryonic Fibroblast (MEF), Intestinal Stem Cell (ISC)]

Track collection: Mouse methylome studies

+  All tracks in this collection (559)

Maximum display mode:       Reset to defaults   
Select views (Help):
PMD       CpG methylation ▾       CpG reads ▾       AMR       HMR      
Select subtracks by views and experiment:
 All views PMD  CpG methylation  CpG reads  AMR  HMR 
experiment
SRX19005207 
SRX19005208 
SRX19005209 
SRX19005210 
SRX19005211 
SRX19005212 
SRX19005213 
SRX19005214 
List subtracks: only selected/visible    all    ()
  experiment↓1 views↓2   Track Name↓3  
hide
 SRX19005207  HMR  Embryonic Fibroblast (MEF) / SRX19005207 (HMR)   Data format 
hide
 Configure
 SRX19005207  CpG methylation  Embryonic Fibroblast (MEF) / SRX19005207 (CpG methylation)   Data format 
hide
 SRX19005208  HMR  Embryonic Fibroblast (MEF) / SRX19005208 (HMR)   Data format 
hide
 Configure
 SRX19005208  CpG methylation  Embryonic Fibroblast (MEF) / SRX19005208 (CpG methylation)   Data format 
hide
 SRX19005209  HMR  Embryonic Fibroblast (MEF) / SRX19005209 (HMR)   Data format 
hide
 Configure
 SRX19005209  CpG methylation  Embryonic Fibroblast (MEF) / SRX19005209 (CpG methylation)   Data format 
hide
 SRX19005210  HMR  Embryonic Fibroblast (MEF) / SRX19005210 (HMR)   Data format 
hide
 Configure
 SRX19005210  CpG methylation  Embryonic Fibroblast (MEF) / SRX19005210 (CpG methylation)   Data format 
hide
 Configure
 SRX19005211  CpG methylation  Embryonic Fibroblast (MEF) / SRX19005211 (CpG methylation)   Data format 
hide
 Configure
 SRX19005212  CpG methylation  Embryonic Fibroblast (MEF) / SRX19005212 (CpG methylation)   Data format 
hide
 SRX19005213  HMR  Intestinal Stem Cell (ISC) / SRX19005213 (HMR)   Data format 
hide
 Configure
 SRX19005213  CpG methylation  Intestinal Stem Cell (ISC) / SRX19005213 (CpG methylation)   Data format 
hide
 SRX19005214  HMR  Intestinal Stem Cell (ISC) / SRX19005214 (HMR)   Data format 
hide
 Configure
 SRX19005214  CpG methylation  Intestinal Stem Cell (ISC) / SRX19005214 (CpG methylation)   Data format 
    
Assembly: Mouse Jun. 2020 (GRCm39/mm39)

Study title: Whole-genome bisulfite sequencing analysis for intestinal budding organoids derived from induced or tissue-derived intestinal stem cells, and for mouse embryonic fibroblasts
SRA: SRP417057
GEO: not found
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX19005207 Embryonic Fibroblast (MEF) 0.702 6.9 34331 1202.9 93 1276.2 701 16046.5 0.980 GSM6928645: PBAT sample for MEF, replicate 1; Mus musculus; Bisulfite-Seq
SRX19005208 Embryonic Fibroblast (MEF) 0.705 6.1 33607 1235.9 59 1287.4 532 20284.2 0.984 GSM6928646: PBAT sample for MEF, replicate 2; Mus musculus; Bisulfite-Seq
SRX19005209 Embryonic Fibroblast (MEF) 0.705 6.2 33778 1218.4 81 1382.1 566 19734.3 0.981 GSM6928647: PBAT sample for MEF, replicate 3; Mus musculus; Bisulfite-Seq
SRX19005210 Embryonic Fibroblast (MEF) 0.642 5.8 26614 2758.7 161 1166.9 385 2482490.7 0.979 GSM6928648: PBAT sample for iISC-BO, replicate 1; Mus musculus; Bisulfite-Seq
SRX19005211 Embryonic Fibroblast (MEF) 0.616 5.3 24784 5370.2 95 1266.3 408 2158288.6 0.974 GSM6928649: PBAT sample for iISC-BO, replicate 2; Mus musculus; Bisulfite-Seq
SRX19005212 Embryonic Fibroblast (MEF) 0.619 5.3 23067 4353.0 95 1222.8 337 2543341.5 0.972 GSM6928650: PBAT sample for iISC-BO, replicate 3; Mus musculus; Bisulfite-Seq
SRX19005213 Intestinal Stem Cell (ISC) 0.646 5.1 26133 2480.2 91 1278.3 126 32539.1 0.975 GSM6928651: PBAT sample for ISC-BO, replicate 1; Mus musculus; Bisulfite-Seq
SRX19005214 Intestinal Stem Cell (ISC) 0.668 8.0 39378 1353.7 152 1303.0 478 16666.1 0.984 GSM6928652: PBAT sample for ISC-BO, replicate 2; Mus musculus; Bisulfite-Seq

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.