Mouse methylome studies SRP133698 Track Settings
 
Global DNA methylation remodeling during direct reprogramming from fibroblast to neuron [MethylC-seq] [Direct Reprogramming, Embryonic Fibroblast, Neural Progenitor Cells]

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 SRX3752569  HMR  Embryonic Fibroblast / SRX3752569 (HMR)   Data format 
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 SRX3752569  CpG methylation  Embryonic Fibroblast / SRX3752569 (CpG methylation)   Data format 
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 SRX3752571  CpG methylation  Direct Reprogramming / SRX3752571 (CpG methylation)   Data format 
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 SRX3752572  HMR  Direct Reprogramming / SRX3752572 (HMR)   Data format 
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 SRX3752572  CpG methylation  Direct Reprogramming / SRX3752572 (CpG methylation)   Data format 
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 SRX3752573  HMR  Direct Reprogramming / SRX3752573 (HMR)   Data format 
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 SRX3752573  CpG methylation  Direct Reprogramming / SRX3752573 (CpG methylation)   Data format 
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 SRX3752574  HMR  Direct Reprogramming / SRX3752574 (HMR)   Data format 
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 SRX3752574  CpG methylation  Direct Reprogramming / SRX3752574 (CpG methylation)   Data format 
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 SRX3752575  CpG methylation  Direct Reprogramming / SRX3752575 (CpG methylation)   Data format 
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 SRX3752576  HMR  Direct Reprogramming / SRX3752576 (HMR)   Data format 
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 SRX3752576  CpG methylation  Direct Reprogramming / SRX3752576 (CpG methylation)   Data format 
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 SRX3752577  CpG methylation  Direct Reprogramming / SRX3752577 (CpG methylation)   Data format 
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 SRX3752578  HMR  Direct Reprogramming / SRX3752578 (HMR)   Data format 
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 SRX3752578  CpG methylation  Direct Reprogramming / SRX3752578 (CpG methylation)   Data format 
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 SRX3752579  HMR  Direct Reprogramming / SRX3752579 (HMR)   Data format 
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 SRX3752579  CpG methylation  Direct Reprogramming / SRX3752579 (CpG methylation)   Data format 
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 SRX3752580  HMR  Direct Reprogramming / SRX3752580 (HMR)   Data format 
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 SRX3752580  CpG methylation  Direct Reprogramming / SRX3752580 (CpG methylation)   Data format 
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 SRX3752581  HMR  Neural Progenitor Cells / SRX3752581 (HMR)   Data format 
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 SRX3752581  CpG methylation  Neural Progenitor Cells / SRX3752581 (CpG methylation)   Data format 
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 SRX3752582  CpG methylation  Neural Progenitor Cells / SRX3752582 (CpG methylation)   Data format 
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 SRX3752583  HMR  GSM3027845: MethylC-seq_NPC_7d_A; Mus musculus; Bisulfite-Seq (HMR)   Data format 
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 SRX3752583  CpG methylation  GSM3027845: MethylC-seq_NPC_7d_A; Mus musculus; Bisulfite-Seq (CpG methylation)   Data format 
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 SRX3752584  HMR  GSM3027846: MethylC-seq_NPC_7d_B; Mus musculus; Bisulfite-Seq (HMR)   Data format 
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 SRX3752584  CpG methylation  GSM3027846: MethylC-seq_NPC_7d_B; Mus musculus; Bisulfite-Seq (CpG methylation)   Data format 
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 SRX3752585  HMR  GSM3027847: MethylC-seq_NPC_14d; Mus musculus; Bisulfite-Seq (HMR)   Data format 
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 SRX3752585  CpG methylation  GSM3027847: MethylC-seq_NPC_14d; Mus musculus; Bisulfite-Seq (CpG methylation)   Data format 
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 SRX3752586  HMR  GSM3027848: MethylC-seq_NPC_21d; Mus musculus; Bisulfite-Seq (HMR)   Data format 
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 SRX3752586  CpG methylation  GSM3027848: MethylC-seq_NPC_21d; Mus musculus; Bisulfite-Seq (CpG methylation)   Data format 
    
Assembly: Mouse Jun. 2020 (GRCm39/mm39)

Study title: Global DNA methylation remodeling during direct reprogramming from fibroblast to neuron [MethylC-seq]
SRA: SRP133698
GEO: GSE111283
Pubmed: 30644360

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX3752569 Embryonic Fibroblast 0.705 16.8 37449 1127.3 512 1017.9 2551 295000.1 0.995 GSM3027831: MethylC-seq_MEF_A; Mus musculus; Bisulfite-Seq
SRX3752570 Embryonic Fibroblast 0.725 16.0 37006 1079.3 360 1035.5 4314 34170.4 0.995 GSM3027832: MethylC-seq_MEF_B; Mus musculus; Bisulfite-Seq
SRX3752571 Direct Reprogramming 0.713 16.5 37689 1110.9 375 1042.3 4997 64959.3 0.994 GSM3027833: MethylC-seq_Ascl1_2d_A; Mus musculus; Bisulfite-Seq
SRX3752572 Direct Reprogramming 0.764 14.1 40030 1104.4 310 1015.6 1424 10635.8 0.994 GSM3027834: MethylC-seq_Ascl1_2d_B; Mus musculus; Bisulfite-Seq
SRX3752573 Direct Reprogramming 0.753 14.8 36087 1101.8 394 1001.9 2295 16955.5 0.993 GSM3027835: MethylC-seq_Ascl1_5d_A; Mus musculus; Bisulfite-Seq
SRX3752574 Direct Reprogramming 0.737 14.3 35937 1087.7 389 1019.1 2650 18847.1 0.995 GSM3027836: MethylC-seq_Ascl1_5d_B; Mus musculus; Bisulfite-Seq
SRX3752575 Direct Reprogramming 0.781 11.8 29786 1126.8 576 1026.7 3826 88357.8 0.993 GSM3027837: MethylC-seq_Ascl1_22d_A; Mus musculus; Bisulfite-Seq
SRX3752576 Direct Reprogramming 0.771 9.1 28500 1179.7 556 1089.7 2496 153199.8 0.993 GSM3027838: MethylC-seq_Ascl1_22d_B; Mus musculus; Bisulfite-Seq
SRX3752577 Direct Reprogramming 0.738 16.3 33263 1122.9 487 1018.4 3753 122107.3 0.995 GSM3027839: MethylC-seq_BAM_5d_B; Mus musculus; Bisulfite-Seq
SRX3752578 Direct Reprogramming 0.742 33.0 38533 1083.0 472 1034.2 4654 78241.9 0.994 GSM3027840: MethylC-seq_BAM_5d_C; Mus musculus; Bisulfite-Seq
SRX3752579 Direct Reprogramming 0.824 10.7 27361 1085.6 800 1058.0 2410 13266.0 0.988 GSM3027841: MethylC-seq_BAM_22d_A; Mus musculus; Bisulfite-Seq
SRX3752580 Direct Reprogramming 0.791 11.8 28399 1110.4 617 1060.5 2030 14991.6 0.990 GSM3027842: MethylC-seq_BAM_22d_B; Mus musculus; Bisulfite-Seq
SRX3752581 Neural Progenitor Cells 0.761 12.6 40581 1056.7 183 1032.0 2712 16681.0 0.997 GSM3027843: MethylC-seq_NPC_A; Mus musculus; Bisulfite-Seq
SRX3752582 Neural Progenitor Cells 0.761 11.5 40152 1063.4 236 997.3 2545 15289.9 0.997 GSM3027844: MethylC-seq_NPC_B; Mus musculus; Bisulfite-Seq
SRX3752583 None 0.795 14.1 42202 1064.9 289 1044.6 2973 25631.0 0.996 GSM3027845: MethylC-seq_NPC_7d_A; Mus musculus; Bisulfite-Seq
SRX3752584 None 0.795 14.3 42267 1063.0 323 1051.2 3269 29660.2 0.996 GSM3027846: MethylC-seq_NPC_7d_B; Mus musculus; Bisulfite-Seq
SRX3752585 None 0.801 13.2 38519 1089.1 374 1025.6 3255 39821.1 0.995 GSM3027847: MethylC-seq_NPC_14d; Mus musculus; Bisulfite-Seq
SRX3752586 None 0.809 13.2 38585 1096.4 278 1010.7 3382 31199.9 0.993 GSM3027848: MethylC-seq_NPC_21d; 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.