Mouse methylome studies SRP353283 Track Settings
 
Cancer stem cells, not bulk tumor cells, determine mechanisms of resistance to SMO inhibitors

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 SRX13580775  CpG methylation  SRS11472555 / SRX13580775 (CpG methylation)   Schema 
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 SRX13580776  CpG methylation  SRS11472556 / SRX13580776 (CpG methylation)   Schema 
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 SRX13580777  CpG methylation  SRS11472557 / SRX13580777 (CpG methylation)   Schema 
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 SRX13580778  CpG methylation  SRS11472558 / SRX13580778 (CpG methylation)   Schema 
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 SRX13580780  CpG methylation  SRS11472560 / SRX13580780 (CpG methylation)   Schema 
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 SRX13580781  HMR  SRS11472561 / SRX13580781 (HMR)   Schema 
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 SRX13580782  HMR  SRS11472562 / SRX13580782 (HMR)   Schema 
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 SRX13580781  CpG methylation  SRS11472561 / SRX13580781 (CpG methylation)   Schema 
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 SRX13580782  CpG methylation  SRS11472562 / SRX13580782 (CpG methylation)   Schema 
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 SRX13580783  HMR  SRS11472563 / SRX13580783 (HMR)   Schema 
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 SRX13580783  CpG methylation  SRS11472563 / SRX13580783 (CpG methylation)   Schema 
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 SRX13580784  CpG methylation  SRS11472564 / SRX13580784 (CpG methylation)   Schema 
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 SRX13580785  CpG methylation  SRS11472565 / SRX13580785 (CpG methylation)   Schema 
    

Study title: Cancer stem cells, not bulk tumor cells, determine mechanisms of resistance to SMO inhibitors
SRA: SRP353283
GEO: not found
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX13580775 SRS11472555 0.715 27.3 78575 2963.5 421 1040.2 3191 180628.7 0.989 title: LPMeseq1; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "none", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq1", "Source": "918 par"}
SRX13580776 SRS11472556 0.722 26.3 78495 2958.9 498 1039.1 3095 188593.5 0.988 title: LPMeseq2; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "DMSO", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq2", "Source": "918 DMSO"}
SRX13580777 SRS11472557 0.719 22.9 69842 2834.4 548 1031.6 3166 174853.5 0.989 title: LPMeseq3; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "LDE", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq3", "Source": "918 LDEr"}
SRX13580778 SRS11472558 0.726 21.0 77431 2960.9 410 1075.3 3015 183499.8 0.989 title: LPMeseq4; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "DMSO", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq4", "Source": "2869 DMSO"}
SRX13580780 SRS11472560 0.718 21.6 65716 4355.3 535 1031.8 2930 215327.8 0.988 title: LPMeseq5; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "LDE", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq5", "Source": "2869 LDEr"}
SRX13580781 SRS11472561 0.769 20.8 49764 2117.0 500 1016.7 2720 257205.3 0.987 title: LPMeseq6; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "none", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq6", "Source": "17282 par"}
SRX13580782 SRS11472562 0.765 23.6 50829 2123.6 596 980.8 2969 234542.7 0.989 title: LPMeseq7; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "DMSO", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq7", "Source": "17282 DMSO"}
SRX13580783 SRS11472563 0.775 21.7 50544 2089.2 584 1710.5 2516 289754.1 0.986 title: LPMeseq8; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "LDE", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq8", "Source": "17282 LDEr"}
SRX13580784 SRS11472564 0.671 27.0 65700 4542.4 849 1163.2 2823 222680.4 0.991 title: LPMeseq9; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "DMSO", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq9", "Source": "9410 DMSO"}
SRX13580785 SRS11472565 0.673 24.6 65013 4615.2 835 1202.0 2710 231798.0 0.987 title: LPMeseq10; {"strain": "not applicable", "age": "not applicable", "dev_stage": "not applicable", "sex": "not applicable", "tissue": "not applicable", "treatment": "LDE", "assay_type": "Methyl-Seq", "sample_number": "LPMeseq10", "Source": "9410 LDEr"}

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.