Human methylome studies SRP199025 Track Settings
 
Molecular mechanism of KCNJ5 gene hotspot mutation in adrenal aldosteronoma [Adrenal Gland]

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 SRX11946827  CpG methylation  Adrenal Gland / SRX11946827 (CpG methylation)   Data format 
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 SRX11946828  CpG methylation  Adrenal Gland / SRX11946828 (CpG methylation)   Data format 
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 SRX11946829  CpG methylation  Adrenal Gland / SRX11946829 (CpG methylation)   Data format 
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 SRX11946830  CpG methylation  Adrenal Gland / SRX11946830 (CpG methylation)   Data format 
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 SRX11946831  CpG methylation  Adrenal Gland / SRX11946831 (CpG methylation)   Data format 
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 SRX11946832  CpG methylation  Adrenal Gland / SRX11946832 (CpG methylation)   Data format 
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 SRX11946833  CpG methylation  Adrenal Gland / SRX11946833 (CpG methylation)   Data format 
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 SRX11946834  CpG methylation  Adrenal Gland / SRX11946834 (CpG methylation)   Data format 
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 SRX11946835  CpG methylation  Adrenal Gland / SRX11946835 (CpG methylation)   Data format 
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 SRX11946837  CpG methylation  Adrenal Gland / SRX11946837 (CpG methylation)   Data format 
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 SRX11946838  CpG methylation  Adrenal Gland / SRX11946838 (CpG methylation)   Data format 
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 SRX11946839  CpG methylation  Adrenal Gland / SRX11946839 (CpG methylation)   Data format 
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 SRX11946840  CpG methylation  Adrenal Gland / SRX11946840 (CpG methylation)   Data format 
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 SRX11946841  CpG methylation  Adrenal Gland / SRX11946841 (CpG methylation)   Data format 
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 SRX11946842  CpG methylation  Adrenal Gland / SRX11946842 (CpG methylation)   Data format 
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 SRX11946843  CpG methylation  Adrenal Gland / SRX11946843 (CpG methylation)   Data format 
    
Assembly: Human Dec. 2013 (GRCh38/hg38)

Study title: Molecular mechanism of KCNJ5 gene hotspot mutation in adrenal aldosteronoma
SRA: SRP199025
GEO: not found
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX11946827 Adrenal Gland 0.509 7.2 53391 19151.0 7425 6127.6 1929 700775.2 0.984 WGBS of wild SW-13: library 1
SRX11946828 Adrenal Gland 0.509 7.2 53391 19175.8 7225 6171.5 1929 700537.0 0.984 WGBS of wild SW-13: library 2
SRX11946829 Adrenal Gland 0.508 7.5 55085 18766.6 8532 5676.0 1928 699367.8 0.986 WGBS of wild SW-13: library 3
SRX11946830 Adrenal Gland 0.508 7.4 55107 18772.5 8303 5766.9 1927 700864.7 0.986 WGBS of wild SW-13: library 4
SRX11946831 Adrenal Gland 0.546 6.3 45571 19775.5 7366 5726.6 1637 839239.9 0.988 WGBS of D3
SRX11946832 Adrenal Gland 0.543 5.7 41983 20451.0 4847 7555.3 1601 850176.6 0.989 WGBS of D3
SRX11946833 Adrenal Gland 0.545 6.3 45540 19797.4 7475 5650.7 1637 836682.1 0.988 WGBS of D3
SRX11946834 Adrenal Gland 0.545 6.4 45864 19721.5 7667 5620.3 1624 844742.0 0.988 WGBS of D3
SRX11946835 Adrenal Gland 0.545 6.4 45985 19718.6 7608 5628.3 1622 845121.3 0.988 WGBS of D3
SRX11946837 Adrenal Gland 0.545 6.3 45652 19829.0 7252 5799.8 1626 842902.6 0.989 WGBS of D3
SRX11946838 Adrenal Gland 0.460 5.7 49405 22285.0 3033 11010.1 2200 662051.4 0.988 WGBS of B7
SRX11946839 Adrenal Gland 0.457 5.2 41815 24179.8 1907 16337.2 2031 716432.8 0.989 WGBS of B7
SRX11946840 Adrenal Gland 0.459 5.8 49639 22185.7 3065 10934.3 2237 651831.0 0.988 WGBS of B7
SRX11946841 Adrenal Gland 0.459 5.8 49525 22258.6 3154 10790.5 2187 665189.6 0.988 WGBS of B7
SRX11946842 Adrenal Gland 0.459 5.8 49801 22199.1 3100 10817.5 2238 650846.4 0.988 WGBS of B7
SRX11946843 Adrenal Gland 0.458 5.8 49882 22168.5 3027 11101.2 2223 654779.0 0.989 WGBS of B7

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.