Human methylome studies SRP375137 Track Settings
 
DNMT3A-mediated DNA demethylation is required for hypoxia induced EMT of human cancer cells

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 SRX15265809  CpG methylation  SRS12996600 / SRX15265809 (CpG methylation)   Schema 
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 SRX15265810  CpG methylation  SRS12996602 / SRX15265810 (CpG methylation)   Schema 
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 SRX15265811  CpG methylation  SRS12996601 / SRX15265811 (CpG methylation)   Schema 
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 SRX15265812  CpG methylation  SRS12996603 / SRX15265812 (CpG methylation)   Schema 
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 SRX15265813  CpG methylation  SRS12996605 / SRX15265813 (CpG methylation)   Schema 
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 SRX15265814  CpG methylation  SRS12996604 / SRX15265814 (CpG methylation)   Schema 
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 SRX15265816  CpG methylation  SRS12996607 / SRX15265816 (CpG methylation)   Schema 
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 SRX15265817  CpG methylation  SRS12996608 / SRX15265817 (CpG methylation)   Schema 
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Study title: DNMT3A-mediated DNA demethylation is required for hypoxia induced EMT of human cancer cells
SRA: SRP375137
GEO: not found
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX15265803 SRS12996594 0.652 13.0 79771 8945.5 579 992.0 2539 365884.8 0.984 title: GSM6142345 Run1_SW480-Normoxia-1, Homo sapiens, Bisulfite-Seq; {}
SRX15265804 SRS12996596 0.650 13.4 79599 8954.9 598 979.7 2596 357997.2 0.984 title: GSM6142346 Run1_SW480-Normoxia-2, Homo sapiens, Bisulfite-Seq; {}
SRX15265805 SRS12996595 0.657 12.1 78301 8936.7 518 972.2 2591 353537.2 0.984 title: GSM6142347 Run1_SW480-Hypoxia-1, Homo sapiens, Bisulfite-Seq; {}
SRX15265806 SRS12996597 0.656 13.2 79652 8800.6 567 1056.4 2590 354390.6 0.984 title: GSM6142348 Run1_SW480-Hypoxia-2, Homo sapiens, Bisulfite-Seq; {}
SRX15265807 SRS12996598 0.605 12.7 87607 8325.0 552 1071.9 2781 326163.2 0.984 title: GSM6142349 Run1_SW480-DNMT3A-KO-Normoxia-1, Homo sapiens, Bisulfite-Seq; {}
SRX15265808 SRS12996599 0.607 12.2 85603 8514.6 498 987.6 2745 329428.7 0.984 title: GSM6142350 Run1_SW480-DNMT3A-KO-Normoxia-2, Homo sapiens, Bisulfite-Seq; {}
SRX15265809 SRS12996600 0.611 10.7 80620 8921.0 464 968.9 2002 453945.0 0.984 title: GSM6142351 Run1_SW480-DNMT3A-KO-Hypoxia-1, Homo sapiens, Bisulfite-Seq; {}
SRX15265810 SRS12996602 0.610 11.6 83506 8598.8 502 1057.5 2684 334436.3 0.984 title: GSM6142352 Run1_SW480-DNMT3A-KO-Hypoxia-2, Homo sapiens, Bisulfite-Seq; {}
SRX15265811 SRS12996601 0.652 9.3 72103 9779.4 341 1133.1 1926 486357.4 0.982 title: GSM6142353 Run2_SW480-Normoxia-1, Homo sapiens, Bisulfite-Seq; {}
SRX15265812 SRS12996603 0.650 10.3 74071 9548.5 417 1004.8 1978 475696.7 0.982 title: GSM6142354 Run2_SW480-Normoxia-2, Homo sapiens, Bisulfite-Seq; {}
SRX15265813 SRS12996605 0.657 9.7 72754 9563.6 338 996.0 1954 474560.5 0.983 title: GSM6142355 Run2_SW480-Hypoxia-1, Homo sapiens, Bisulfite-Seq; {}
SRX15265814 SRS12996604 0.657 14.4 81676 8623.1 586 993.7 2651 348618.5 0.983 title: GSM6142356 Run2_SW480-Hypoxia-2, Homo sapiens, Bisulfite-Seq; {}
SRX15265815 SRS12996606 0.606 10.2 78855 9222.9 410 978.3 2011 455019.9 0.983 title: GSM6142357 Run2_SW480-DNMT3A-KO-Normoxia-1, Homo sapiens, Bisulfite-Seq; {}
SRX15265816 SRS12996607 0.607 9.9 77021 9427.1 364 975.7 2039 448642.0 0.983 title: GSM6142358 Run2_SW480-DNMT3A-KO-Normoxia-2, Homo sapiens, Bisulfite-Seq; {}
SRX15265817 SRS12996608 0.611 9.1 74091 9679.4 329 969.2 1998 453480.7 0.982 title: GSM6142359 Run2_SW480-DNMT3A-KO-Hypoxia-1, Homo sapiens, Bisulfite-Seq; {}
SRX15265818 SRS12996609 0.611 9.1 74455 9646.2 349 1083.2 2037 446457.0 0.982 title: GSM6142360 Run2_SW480-DNMT3A-KO-Hypoxia-2, 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.