Mouse methylome studies SRP099007 Track Settings
 
Microglia isolated from juvenile offspring of dams with allergic asthma exhibit methylation and transcriptional alterations to autism risk genes [WGBS] [Acutely Isolated Microglia, P35 Brain]

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 SRX2540541  CpG methylation  Acutely Isolated Microglia, P35 Brain / SRX2540541 (CpG methylation)   Data format 
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 SRX2540544  CpG methylation  Acutely Isolated Microglia, P35 Brain / SRX2540544 (CpG methylation)   Data format 
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 SRX2540545  CpG methylation  Acutely Isolated Microglia, P35 Brain / SRX2540545 (CpG methylation)   Data format 
    
Assembly: Mouse Jun. 2020 (GRCm39/mm39)

Study title: Microglia isolated from juvenile offspring of dams with allergic asthma exhibit methylation and transcriptional alterations to autism risk genes [WGBS]
SRA: SRP099007
GEO: GSE94568
Pubmed: 29134693

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX2540538 Acutely Isolated Microglia, P35 Brain 0.714 3.7 27407 1446.8 132 1056.4 243 39327.4 0.981 GSM2478425: MAA1 WGBS; Mus musculus; Bisulfite-Seq
SRX2540539 Acutely Isolated Microglia, P35 Brain 0.711 2.6 26822 1580.8 58 1138.5 131 55879.1 0.982 GSM2478426: MAA2 WGBS; Mus musculus; Bisulfite-Seq
SRX2540540 Acutely Isolated Microglia, P35 Brain 0.711 3.1 27438 1530.5 71 1100.4 99 50578.1 0.982 GSM2478427: MAA3 WGBS; Mus musculus; Bisulfite-Seq
SRX2540541 Acutely Isolated Microglia, P35 Brain 0.710 3.0 29739 1507.6 62 1065.8 223 45267.7 0.983 GSM2478428: MAA4 WGBS; Mus musculus; Bisulfite-Seq
SRX2540542 Acutely Isolated Microglia, P35 Brain 0.716 3.1 28481 1522.3 73 1077.5 87 57878.2 0.983 GSM2478429: PBS1 WGBS; Mus musculus; Bisulfite-Seq
SRX2540543 Acutely Isolated Microglia, P35 Brain 0.715 2.4 26705 1634.8 57 1048.2 99 63145.5 0.982 GSM2478430: PBS2 WGBS; Mus musculus; Bisulfite-Seq
SRX2540544 Acutely Isolated Microglia, P35 Brain 0.711 2.9 28261 1563.5 89 1086.5 135 49789.5 0.982 GSM2478431: PBS3 WGBS; Mus musculus; Bisulfite-Seq
SRX2540545 Acutely Isolated Microglia, P35 Brain 0.709 3.6 28369 1466.9 149 1048.1 228 38278.8 0.981 GSM2478432: PBS4 WGBS; 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.