Mouse methylome studies SRP229373 Track Settings
 
Novel Isoforms of the Epigenetic Regulator TET1 Differentially Control CNS Synaptic Function, Plasticity, and Cognition [Cultured Hippocampal Neurons]

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 SRX7119809  CpG methylation  Cultured Hippocampal Neurons / SRX7119809 (CpG methylation)   Data format 
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 SRX7119810  CpG methylation  Cultured Hippocampal Neurons / SRX7119810 (CpG methylation)   Data format 
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 SRX7119812  CpG methylation  Cultured Hippocampal Neurons / SRX7119812 (CpG methylation)   Data format 
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 SRX7119813  HMR  Cultured Hippocampal Neurons / SRX7119813 (HMR)   Data format 
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 SRX7119813  CpG methylation  Cultured Hippocampal Neurons / SRX7119813 (CpG methylation)   Data format 
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Assembly: Mouse Jun. 2020 (GRCm39/mm39)

Study title: Novel Isoforms of the Epigenetic Regulator TET1 Differentially Control CNS Synaptic Function, Plasticity, and Cognition
SRA: SRP229373
GEO: GSE140174
Pubmed: 33262245

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX7119806 Cultured Hippocampal Neurons 0.821 13.2 49165 1205.2 286 1198.8 2695 9518.8 0.982 GSM4155779: DNAMe_NFD_TIL_Rep1; Mus musculus; Bisulfite-Seq
SRX7119807 Cultured Hippocampal Neurons 0.814 11.2 48560 1208.6 261 1226.4 1475 16036.7 0.982 GSM4155780: DNAMe_NFD_TIL_Rep2; Mus musculus; Bisulfite-Seq
SRX7119808 Cultured Hippocampal Neurons 0.814 20.3 58542 1146.3 453 1136.5 2782 10398.0 0.982 GSM4155781: DNAMe_VP64_TIL_Rep1; Mus musculus; Bisulfite-Seq
SRX7119809 Cultured Hippocampal Neurons 0.825 21.1 56225 1167.8 422 1165.2 3165 10004.4 0.981 GSM4155782: DNAMe_VP64_TIL_Rep2; Mus musculus; Bisulfite-Seq
SRX7119810 Cultured Hippocampal Neurons 0.812 14.3 54735 1193.5 401 1142.7 2621 10532.4 0.981 GSM4155783: DNAMe_SID4X_TIL_Rep1; Mus musculus; Bisulfite-Seq
SRX7119811 Cultured Hippocampal Neurons 0.808 16.7 58940 1178.8 467 1112.5 2965 10250.5 0.981 GSM4155784: DNAMe_SID4X_TIL_Rep2; Mus musculus; Bisulfite-Seq
SRX7119812 Cultured Hippocampal Neurons 0.816 11.3 48785 1208.3 245 2913.7 1650 15484.8 0.982 GSM4155785: DNAMe_NFD_TIS_Rep1; Mus musculus; Bisulfite-Seq
SRX7119813 Cultured Hippocampal Neurons 0.820 21.0 56967 1145.5 413 1183.5 3009 10177.7 0.983 GSM4155786: DNAMe_NFD_TIS_Rep2; Mus musculus; Bisulfite-Seq
SRX7119814 Cultured Hippocampal Neurons 0.815 15.9 53062 1182.3 382 2258.3 2845 10216.4 0.982 GSM4155787: DNAMe_VP64_TIS_Rep1; Mus musculus; Bisulfite-Seq
SRX7119815 Cultured Hippocampal Neurons 0.822 17.1 53114 1183.2 335 1168.0 3282 9644.2 0.982 GSM4155788: DNAMe_VP64_TIS_Rep2; Mus musculus; Bisulfite-Seq
SRX7119816 Cultured Hippocampal Neurons 0.822 14.8 52765 1211.4 323 1174.0 2947 10255.1 0.981 GSM4155789: DNAMe_SID4X_TIS_Rep1; Mus musculus; Bisulfite-Seq
SRX7119817 Cultured Hippocampal Neurons 0.815 11.7 50780 1225.3 272 1159.3 1567 16223.5 0.981 GSM4155790: DNAMe_SID4X_TIS_Rep2; 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.