Human methylome studies SRP265926 Track Settings
 
2-hydroxyglutarate drives whole-genome hypermethylation in kidney cancer cells with inactivated VHL [Caki1 Cell Line, Wildtype VHL, Clone12, Exogenous Wildtype VHL Reintroduced, Clone30, Exogenous Wildtype VHL Reintroduced, Clone44, Exogenous Wildtype VHL Reintroduced, VHL Inactivated, Clone12, VHL Inactivated, Clone30, VHL Inactivated, Clone44]

Track collection: Human methylome studies

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 SRX8468850  CpG methylation  Caki1 Cell Line, Wildtype VHL / SRX8468850 (CpG methylation)   Data format 
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 SRX8468851  CpG methylation  Caki1 Cell Line, Wildtype VHL / SRX8468851 (CpG methylation)   Data format 
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 SRX8468852  CpG methylation  Caki1 Cell Line, Wildtype VHL / SRX8468852 (CpG methylation)   Data format 
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 SRX8468853  CpG methylation  VHL Inactivated, Clone12 / SRX8468853 (CpG methylation)   Data format 
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 SRX8468854  CpG methylation  VHL Inactivated, Clone30 / SRX8468854 (CpG methylation)   Data format 
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 SRX8468855  CpG methylation  VHL Inactivated, Clone44 / SRX8468855 (CpG methylation)   Data format 
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 SRX8468856  CpG methylation  Clone12, Exogenous Wildtype VHL Reintroduced / SRX8468856 (CpG methylation)   Data format 
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 SRX8468857  CpG methylation  Clone30, Exogenous Wildtype VHL Reintroduced / SRX8468857 (CpG methylation)   Data format 
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 SRX8468858  CpG methylation  Clone44, Exogenous Wildtype VHL Reintroduced / SRX8468858 (CpG methylation)   Data format 
    
Assembly: Human Dec. 2013 (GRCh38/hg38)

Study title: 2-hydroxyglutarate drives whole-genome hypermethylation in kidney cancer cells with inactivated VHL
SRA: SRP265926
GEO: GSE151787
Pubmed: 34494499

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX8468850 Caki1 Cell Line, Wildtype VHL 0.633 6.2 61070 11619.2 6284 6404.0 1589 624409.8 0.997 GSM4590940: Caki1_1; Homo sapiens; Bisulfite-Seq
SRX8468851 Caki1 Cell Line, Wildtype VHL 0.633 6.7 63781 11836.4 6587 6421.2 1776 556414.9 0.998 GSM4590941: Caki1_2; Homo sapiens; Bisulfite-Seq
SRX8468852 Caki1 Cell Line, Wildtype VHL 0.633 9.2 68864 11212.0 14253 4112.3 1786 557628.4 0.998 GSM4590942: Caki1_3; Homo sapiens; Bisulfite-Seq
SRX8468853 VHL Inactivated, Clone12 0.609 8.3 69884 10807.6 13574 4439.0 2149 442469.8 0.998 GSM4590943: dVHL_clone12; Homo sapiens; Bisulfite-Seq
SRX8468854 VHL Inactivated, Clone30 0.642 5.9 64299 11397.3 3845 9518.5 1776 525949.6 0.999 GSM4590944: dVHL_clone30; Homo sapiens; Bisulfite-Seq
SRX8468855 VHL Inactivated, Clone44 0.635 8.1 71271 10480.9 9010 5782.2 2195 432142.9 0.998 GSM4590945: dVHL_clone44; Homo sapiens; Bisulfite-Seq
SRX8468856 Clone12, Exogenous Wildtype VHL Reintroduced 0.629 6.2 63537 11674.8 5435 7004.0 1794 523973.0 0.993 GSM4590946: dVHLexo_clone12; Homo sapiens; Bisulfite-Seq
SRX8468857 Clone30, Exogenous Wildtype VHL Reintroduced 0.645 7.1 68917 10829.6 5388 7642.9 2237 412349.4 0.994 GSM4590947: dVHLexo_clone30; Homo sapiens; Bisulfite-Seq
SRX8468858 Clone44, Exogenous Wildtype VHL Reintroduced 0.640 6.9 68926 10798.8 4676 8525.3 2224 412813.5 0.995 GSM4590948: dVHLexo_clone44; Homo sapiens; 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.