Human methylome studies SRP299802 Track Settings
 
Acute lymphoblastic leukemia displays a distinct highly methylated genome [ALL-SIL, DND41, Jurkat, LOUCY, MHH-CALL-2, MHH-CALL-4, MOLT-16, MUTZ5, NALM-16, NALM-6, PEER, PER-117, RPMI-8402, TALL-1]

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 SRX13811031  HMR  ALL-SIL / SRX13811031 (HMR)   Data format 
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 SRX13811031  CpG methylation  ALL-SIL / SRX13811031 (CpG methylation)   Data format 
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 SRX13811032  HMR  LOUCY / SRX13811032 (HMR)   Data format 
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 SRX13811032  CpG methylation  LOUCY / SRX13811032 (CpG methylation)   Data format 
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 SRX13811033  HMR  MHH-CALL-2 / SRX13811033 (HMR)   Data format 
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 SRX13811033  CpG methylation  MHH-CALL-2 / SRX13811033 (CpG methylation)   Data format 
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 SRX13811034  HMR  MHH-CALL-4 / SRX13811034 (HMR)   Data format 
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 SRX13811034  CpG methylation  MHH-CALL-4 / SRX13811034 (CpG methylation)   Data format 
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 SRX13811035  HMR  MOLT-16 / SRX13811035 (HMR)   Data format 
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 SRX13811035  CpG methylation  MOLT-16 / SRX13811035 (CpG methylation)   Data format 
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 SRX13811036  HMR  MUTZ5 / SRX13811036 (HMR)   Data format 
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 SRX13811036  CpG methylation  MUTZ5 / SRX13811036 (CpG methylation)   Data format 
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 SRX13811037  HMR  NALM-6 / SRX13811037 (HMR)   Data format 
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 SRX13811037  CpG methylation  NALM-6 / SRX13811037 (CpG methylation)   Data format 
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 SRX13811038  HMR  NALM-6 / SRX13811038 (HMR)   Data format 
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 SRX13811038  CpG methylation  NALM-6 / SRX13811038 (CpG methylation)   Data format 
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 SRX13811039  HMR  NALM-16 / SRX13811039 (HMR)   Data format 
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 SRX13811039  CpG methylation  NALM-16 / SRX13811039 (CpG methylation)   Data format 
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 SRX13811040  HMR  PEER / SRX13811040 (HMR)   Data format 
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 SRX13811040  CpG methylation  PEER / SRX13811040 (CpG methylation)   Data format 
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 SRX13811041  HMR  PER-117 / SRX13811041 (HMR)   Data format 
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 SRX13811041  CpG methylation  PER-117 / SRX13811041 (CpG methylation)   Data format 
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 SRX13811042  HMR  RPMI-8402 / SRX13811042 (HMR)   Data format 
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 SRX13811042  CpG methylation  RPMI-8402 / SRX13811042 (CpG methylation)   Data format 
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 SRX13811043  HMR  TALL-1 / SRX13811043 (HMR)   Data format 
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 SRX13811043  CpG methylation  TALL-1 / SRX13811043 (CpG methylation)   Data format 
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 SRX9756753  HMR  DND41 / SRX9756753 (HMR)   Data format 
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 SRX9756753  CpG methylation  DND41 / SRX9756753 (CpG methylation)   Data format 
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 SRX9756754  CpG methylation  Jurkat / SRX9756754 (CpG methylation)   Data format 
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 SRX9756755  CpG methylation  Jurkat / SRX9756755 (CpG methylation)   Data format 
    
Assembly: Human Dec. 2013 (GRCh38/hg38)

Study title: Acute lymphoblastic leukemia displays a distinct highly methylated genome
SRA: SRP299802
GEO: GSE164040
Pubmed: 35590059

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Title
SRX13811031 ALL-SIL 0.862 34.6 33721 937.2 1433 978.1 759 12175.2 0.979 GSM5821052: WGBS_ALL-SIL; Homo sapiens; Bisulfite-Seq
SRX13811032 LOUCY 0.817 35.6 81235 3810.4 1491 1154.1 1688 575923.2 0.980 GSM5821053: WGBS_LOUCY; Homo sapiens; Bisulfite-Seq
SRX13811033 MHH-CALL-2 0.766 37.1 80304 996.0 819 1313.0 4048 34701.6 0.980 GSM5821054: WGBS_MHH-CALL-2; Homo sapiens; Bisulfite-Seq
SRX13811034 MHH-CALL-4 0.801 32.6 52687 1435.0 1214 914.7 4706 27699.8 0.982 GSM5821055: WGBS_MHH-CALL-4; Homo sapiens; Bisulfite-Seq
SRX13811035 MOLT-16 0.748 39.6 63556 3323.0 2458 1108.7 1165 1157544.2 0.980 GSM5821056: WGBS_MOLT-16; Homo sapiens; Bisulfite-Seq
SRX13811036 MUTZ5 0.836 26.8 58848 1070.6 1372 960.0 1991 12100.3 0.977 GSM5821057: WGBS_MUTZ5; Homo sapiens; Bisulfite-Seq
SRX13811037 NALM-6 0.786 10.9 32958 1346.2 998 898.9 322 28548.6 0.980 GSM5821058: WGBS_NALM-6_Rep1; Homo sapiens; Bisulfite-Seq
SRX13811038 NALM-6 0.793 29.8 44259 1500.7 1781 943.2 2338 17535.5 0.984 GSM5821059: WGBS_NALM-6_Rep2; Homo sapiens; Bisulfite-Seq
SRX13811039 NALM-16 0.801 35.9 76987 849.8 302 1007.1 3264 21498.2 0.982 GSM5821060: WGBS_NALM-16; Homo sapiens; Bisulfite-Seq
SRX13811040 PEER 0.781 18.1 54714 2545.9 1160 957.9 1393 833845.5 0.980 GSM5821061: WGBS_PEER; Homo sapiens; Bisulfite-Seq
SRX13811041 PER-117 0.778 39.8 57592 2374.3 1327 965.2 1108 1220101.2 0.980 GSM5821062: WGBS_PER-117; Homo sapiens; Bisulfite-Seq
SRX13811042 RPMI-8402 0.876 34.4 61061 1984.8 2019 1055.6 1920 126595.4 0.981 GSM5821063: WGBS_RPMI-8402; Homo sapiens; Bisulfite-Seq
SRX13811043 TALL-1 0.825 34.3 59585 3069.7 1454 1011.9 1318 902938.4 0.978 GSM5821064: WGBS_TALL-1; Homo sapiens; Bisulfite-Seq
SRX9756753 DND41 0.760 23.9 44483 2603.4 1028 979.8 1157 1124177.3 0.985 GSM4995532: WGBS_DND41_WT; Homo sapiens; Bisulfite-Seq
SRX9756754 Jurkat 0.715 28.3 108552 4452.4 2971 1026.3 2016 487620.1 0.984 GSM4995533: WGBS_Jurkat_TET2KO; Homo sapiens; Bisulfite-Seq
SRX9756755 Jurkat 0.657 23.7 88036 6903.4 1555 971.6 2076 535086.9 0.986 GSM4995534: WGBS_Jurkat_WT; 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.