Mouse methylome studies SRP188205 Track Settings
 
MeCP2 represses the rate of transcriptional initiation of highly methylated long genes (BS-Seq and OxBS-Seq) [Forebrain]

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Study title: MeCP2 represses the rate of transcriptional initiation of highly methylated long genes (BS-Seq and OxBS-Seq)
SRA: SRP188205
GEO: GSE128172
Pubmed: 31784358

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX5508338 Forebrain 0.755 2.1 25207 1814.9 4 999.2 379 44244.8 0.986 title: GSM3666121 MeCP2_WT_BS_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "WT", "library_type": "BS-seq"}
SRX5508339 Forebrain 0.756 2.0 25160 1866.4 5 1164.2 304 48282.7 0.985 title: GSM3666122 MeCP2_WT_BS_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "WT", "library_type": "BS-seq"}
SRX5508340 Forebrain 0.755 2.0 26958 1786.3 5 1183.8 482 41525.7 0.986 title: GSM3666123 MeCP2_WT_BS_rep3, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "WT", "library_type": "BS-seq"}
SRX5508341 Forebrain 0.755 2.5 26068 1763.6 1 861.0 479 36170.3 0.987 title: GSM3666124 MeCP2_KO_BS_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "MeCP2 KO", "library_type": "BS-seq"}
SRX5508342 Forebrain 0.755 2.2 24748 1844.0 3 980.3 437 43367.6 0.985 title: GSM3666125 MeCP2_KO_BS_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "MeCP2 KO", "library_type": "BS-seq"}
SRX5508343 Forebrain 0.759 2.4 27150 1755.7 5 938.8 497 37836.7 0.986 title: GSM3666126 MeCP2_KO_BS_rep3, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "MeCP2 KO", "library_type": "BS-seq"}
SRX5508344 Forebrain 0.622 1.7 22947 3623.8 4 813.8 556 127964.2 0.987 title: GSM3666127 MeCP2_WT_OX_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "WT", "library_type": "oxBS-seq"}
SRX5508345 Forebrain 0.623 1.9 23647 3637.9 8 1043.9 713 122598.2 0.986 title: GSM3666128 MeCP2_WT_OX_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "WT", "library_type": "oxBS-seq"}
SRX5508346 Forebrain 0.627 1.9 22578 3540.5 7 1812.3 870 91596.4 0.987 title: GSM3666129 MeCP2_WT_OX_rep3, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "WT", "library_type": "oxBS-seq"}
SRX5508347 Forebrain 0.628 2.2 24687 3309.2 2 802.0 785 84764.6 0.988 title: GSM3666130 MeCP2_KO_OX_rep1, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "MeCP2 KO", "library_type": "oxBS-seq"}
SRX5508348 Forebrain 0.618 2.0 23693 3639.3 4 1126.8 805 116502.6 0.986 title: GSM3666131 MeCP2_KO_OX_rep2, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "MeCP2 KO", "library_type": "oxBS-seq"}
SRX5508349 Forebrain 0.627 2.4 25077 3281.7 3 1251.0 976 82177.8 0.987 title: GSM3666132 MeCP2_KO_OX_rep3, Mus musculus, Bisulfite-Seq; {"source_name": "Forebrain tissue", "strain": "C57BL/6", "age": "8 weeks", "sex": "male", "genotype": "MeCP2 KO", "library_type": "oxBS-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.