Mouse methylome studies SRP300442 Track Settings
 
Disordered N-Terminal DNMT3A Domain Is Required for Mouse Postnatal Development [Cerebral Cortex, Cortical Neuron Nuclei]

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 SRX13085668  HMR  Cortical Neuron Nuclei / SRX13085668 (HMR)   Data format 
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 SRX13085668  CpG methylation  Cortical Neuron Nuclei / SRX13085668 (CpG methylation)   Data format 
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 SRX13085669  CpG methylation  Cortical Neuron Nuclei / SRX13085669 (CpG methylation)   Data format 
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 SRX13085670  HMR  Cortical Neuron Nuclei / SRX13085670 (HMR)   Data format 
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 SRX13085670  CpG methylation  Cortical Neuron Nuclei / SRX13085670 (CpG methylation)   Data format 
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 SRX13085671  HMR  Cortical Neuron Nuclei / SRX13085671 (HMR)   Data format 
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 SRX13085671  CpG methylation  Cortical Neuron Nuclei / SRX13085671 (CpG methylation)   Data format 
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 SRX13085672  HMR  Cortical Neuron Nuclei / SRX13085672 (HMR)   Data format 
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 SRX13085672  CpG methylation  Cortical Neuron Nuclei / SRX13085672 (CpG methylation)   Data format 
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 SRX13085673  HMR  Cortical Neuron Nuclei / SRX13085673 (HMR)   Data format 
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 SRX13085673  CpG methylation  Cortical Neuron Nuclei / SRX13085673 (CpG methylation)   Data format 
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 SRX13085674  HMR  Cortical Neuron Nuclei / SRX13085674 (HMR)   Data format 
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 SRX13085674  CpG methylation  Cortical Neuron Nuclei / SRX13085674 (CpG methylation)   Data format 
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 SRX13085675  HMR  Cortical Neuron Nuclei / SRX13085675 (HMR)   Data format 
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 SRX13085675  CpG methylation  Cortical Neuron Nuclei / SRX13085675 (CpG methylation)   Data format 
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 SRX9784404  HMR  Cerebral Cortex / SRX9784404 (HMR)   Data format 
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 SRX9784404  CpG methylation  Cerebral Cortex / SRX9784404 (CpG methylation)   Data format 
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 SRX9784405  CpG methylation  Cerebral Cortex / SRX9784405 (CpG methylation)   Data format 
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 SRX9784406  HMR  Cerebral Cortex / SRX9784406 (HMR)   Data format 
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 SRX9784406  CpG methylation  Cerebral Cortex / SRX9784406 (CpG methylation)   Data format 
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 SRX9784407  HMR  Cerebral Cortex / SRX9784407 (HMR)   Data format 
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 SRX9784407  CpG methylation  Cerebral Cortex / SRX9784407 (CpG methylation)   Data format 
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 SRX9784408  HMR  Cerebral Cortex / SRX9784408 (HMR)   Data format 
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 SRX9784408  CpG methylation  Cerebral Cortex / SRX9784408 (CpG methylation)   Data format 
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 SRX9784409  HMR  Cerebral Cortex / SRX9784409 (HMR)   Data format 
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 SRX9784409  CpG methylation  Cerebral Cortex / SRX9784409 (CpG methylation)   Data format 
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 SRX9784410  HMR  Cerebral Cortex / SRX9784410 (HMR)   Data format 
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 SRX9784410  CpG methylation  Cerebral Cortex / SRX9784410 (CpG methylation)   Data format 
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 SRX9784411  HMR  Cerebral Cortex / SRX9784411 (HMR)   Data format 
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 SRX9784411  CpG methylation  Cerebral Cortex / SRX9784411 (CpG methylation)   Data format 
    
Assembly: Mouse Jun. 2020 (GRCm39/mm39)

Study title: Disordered N-Terminal DNMT3A Domain Is Required for Mouse Postnatal Development
SRA: SRP300442
GEO: GSE164265
Pubmed: 35534561

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX13085668 Cortical Neuron Nuclei 0.653 11.1 38869 1200.8 80 1110.8 1786 12337.1 0.985 GSM5683981: WT_Neuron nuclei_Me_1; Mus musculus; Bisulfite-Seq
SRX13085669 Cortical Neuron Nuclei 0.654 9.1 36795 1261.2 30 973.2 1864 12342.8 0.985 GSM5683982: WT_Neuron nuclei_Me_2; Mus musculus; Bisulfite-Seq
SRX13085670 Cortical Neuron Nuclei 0.580 9.5 63893 1367.3 48 1175.3 1932 16534.6 0.992 GSM5683983: 3a1KO_Neuron nuclei_Me_1; Mus musculus; Bisulfite-Seq
SRX13085671 Cortical Neuron Nuclei 0.583 9.8 65188 1322.2 57 1128.3 1826 18483.2 0.992 GSM5683984: 3a1KO_Neuron nuclei_Me_2; Mus musculus; Bisulfite-Seq
SRX13085672 Cortical Neuron Nuclei 0.657 10.3 38638 1235.7 76 1112.5 1877 12210.5 0.984 GSM5683985: 3a2KO_Neuron nuclei_Me_1; Mus musculus; Bisulfite-Seq
SRX13085673 Cortical Neuron Nuclei 0.654 8.8 38799 1258.2 40 1084.5 1878 12762.4 0.986 GSM5683986: 3a2KO_Neuron nuclei_Me_2; Mus musculus; Bisulfite-Seq
SRX13085674 Cortical Neuron Nuclei 0.634 9.4 43753 1239.8 59 1247.5 1699 12083.6 0.990 GSM5683987: 3a1dN_Neuron nuclei_Me_1; Mus musculus; Bisulfite-Seq
SRX13085675 Cortical Neuron Nuclei 0.631 8.3 40640 1290.9 49 947.8 1764 11999.9 0.989 GSM5683988: 3a1dN_Neuron nuclei_Me_2; Mus musculus; Bisulfite-Seq
SRX9784404 Cerebral Cortex 0.748 6.2 34740 1299.5 324 1008.0 1333 15015.6 0.988 GSM5005415: WT_Cortex_BS_1; Mus musculus; Bisulfite-Seq
SRX9784405 Cerebral Cortex 0.748 12.2 41470 1185.5 2029 879.3 3421 8349.3 0.988 GSM5005416: WT_Cortex_BS_2; Mus musculus; Bisulfite-Seq
SRX9784406 Cerebral Cortex 0.685 15.3 68671 1329.5 2133 842.4 3746 11843.1 0.997 GSM5005417: 3a1KO_Cortex_BS_1; Mus musculus; Bisulfite-Seq
SRX9784407 Cerebral Cortex 0.686 14.6 67986 1337.8 2282 885.8 3730 12044.8 0.997 GSM5005418: 3a1KO_Cortex_BS_2; Mus musculus; Bisulfite-Seq
SRX9784408 Cerebral Cortex 0.741 18.0 44842 1182.3 2353 826.5 3702 8977.6 0.988 GSM5005419: 3a2KO_Cortex_BS_1; Mus musculus; Bisulfite-Seq
SRX9784409 Cerebral Cortex 0.741 15.6 43953 1197.5 2475 882.3 3659 8589.3 0.988 GSM5005420: 3a2KO_Cortex_BS_2; Mus musculus; Bisulfite-Seq
SRX9784410 Cerebral Cortex 0.726 19.4 53187 1184.0 2111 824.4 3137 9761.5 0.994 GSM5005421: 3a1dN_Cortex_BS_1; Mus musculus; Bisulfite-Seq
SRX9784411 Cerebral Cortex 0.722 15.7 50998 1206.3 2110 884.8 3356 9057.1 0.995 GSM5005422: 3a1dN_Cortex_BS_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.