Rat methylome studies ERP002215 Track Settings
 
Genetic analysis of the cardiac methylome at single nucleotide resolution in the Spontaneously Hypertensive Rat and the Brown Norway Rat [Heart]

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 ERX202457  HMR  Heart / ERX202457 (HMR)   Schema 
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 ERX202457  CpG methylation  Heart / ERX202457 (CpG methylation)   Schema 
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 ERX202458  HMR  Heart / ERX202458 (HMR)   Schema 
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 ERX202458  CpG methylation  Heart / ERX202458 (CpG methylation)   Schema 
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 ERX202464  HMR  Heart / ERX202464 (HMR)   Schema 
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 ERX202464  CpG methylation  Heart / ERX202464 (CpG methylation)   Schema 
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 ERX202465  HMR  Heart / ERX202465 (HMR)   Schema 
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 ERX202465  CpG methylation  Heart / ERX202465 (CpG methylation)   Schema 
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 ERX202466  HMR  Heart / ERX202466 (HMR)   Schema 
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 ERX202466  CpG methylation  Heart / ERX202466 (CpG methylation)   Schema 
    

Study title: Genetic analysis of the cardiac methylome at single nucleotide resolution in the Spontaneously Hypertensive Rat and the Brown Norway Rat
SRA: ERP002215
GEO: not found
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
ERX202454 Heart 0.630 8.2 34566 1122.6 1395 852.8 264 46197.8 0.922 title: ERX202454; {"ENA-FIRST-PUBLIC": "2013-12-31", "ENA-LAST-UPDATE": "2013-02-07", "ENA-SUBMISSION-TOOL": "SRA-Webin", "External_Id": "SAMEA2047053", "INSDC_center_alias": "ICL-PGM", "INSDC_center_name": "Physiological Genomics null, Imperial College London, UK", "INSDC_first_public": "2013-12-31T17:02:34Z", "INSDC_last_update": "2013-02-07T16:43:15Z", "INSDC_status": "public", "Submitter_Id": "SHRXBN1", "bio_material": "left ventricle of the heart", "common_name": "Norway rat", "sample_name": "SHRXBN1"}
ERX202455 Heart 0.638 9.4 35271 1102.5 1663 846.0 335 41594.3 0.924 title: ERX202455; {"ENA-FIRST-PUBLIC": "2013-12-31", "ENA-LAST-UPDATE": "2013-02-07", "ENA-SUBMISSION-TOOL": "SRA-Webin", "External_Id": "SAMEA2047053", "INSDC_center_alias": "ICL-PGM", "INSDC_center_name": "Physiological Genomics null, Imperial College London, UK", "INSDC_first_public": "2013-12-31T17:02:34Z", "INSDC_last_update": "2013-02-07T16:43:15Z", "INSDC_status": "public", "Submitter_Id": "SHRXBN1", "bio_material": "left ventricle of the heart", "common_name": "Norway rat", "sample_name": "SHRXBN1"}
ERX202456 Heart 0.654 11.3 36111 1072.1 1889 859.5 402 37202.8 0.922 title: ERX202456; {"ENA-FIRST-PUBLIC": "2013-12-31", "ENA-LAST-UPDATE": "2013-02-07", "ENA-SUBMISSION-TOOL": "SRA-Webin", "External_Id": "SAMEA2047053", "INSDC_center_alias": "ICL-PGM", "INSDC_center_name": "Physiological Genomics null, Imperial College London, UK", "INSDC_first_public": "2013-12-31T17:02:34Z", "INSDC_last_update": "2013-02-07T16:43:15Z", "INSDC_status": "public", "Submitter_Id": "SHRXBN1", "bio_material": "left ventricle of the heart", "common_name": "Norway rat", "sample_name": "SHRXBN1"}
ERX202457 Heart 0.621 7.6 34304 1100.5 1514 845.4 224 55105.8 0.901 title: ERX202457; {"ENA-FIRST-PUBLIC": "2013-12-31", "ENA-LAST-UPDATE": "2013-02-07", "ENA-SUBMISSION-TOOL": "SRA-Webin", "External_Id": "SAMEA2047054", "INSDC_center_alias": "ICL-PGM", "INSDC_center_name": "Physiological Genomics null, Imperial College London, UK", "INSDC_first_public": "2013-12-31T17:02:34Z", "INSDC_last_update": "2013-02-07T16:43:15Z", "INSDC_status": "public", "Submitter_Id": "SHRXBN2", "bio_material": "left ventricle of the heart", "common_name": "Norway rat", "sample_name": "SHRXBN2"}
ERX202458 Heart 0.626 8.9 35417 1077.8 1705 866.1 305 48515.5 0.901 title: ERX202458; {"ENA-FIRST-PUBLIC": "2013-12-31", "ENA-LAST-UPDATE": "2013-02-07", "ENA-SUBMISSION-TOOL": "SRA-Webin", "External_Id": "SAMEA2047054", "INSDC_center_alias": "ICL-PGM", "INSDC_center_name": "Physiological Genomics null, Imperial College London, UK", "INSDC_first_public": "2013-12-31T17:02:34Z", "INSDC_last_update": "2013-02-07T16:43:15Z", "INSDC_status": "public", "Submitter_Id": "SHRXBN2", "bio_material": "left ventricle of the heart", "common_name": "Norway rat", "sample_name": "SHRXBN2"}
ERX202464 Heart 0.622 8.0 34198 1102.9 1541 845.0 319 48080.2 0.906 title: ERX202464; {"ENA-FIRST-PUBLIC": "2013-12-31", "ENA-LAST-UPDATE": "2013-02-07", "ENA-SUBMISSION-TOOL": "SRA-Webin", "External_Id": "SAMEA2047056", "INSDC_center_alias": "ICL-PGM", "INSDC_center_name": "Physiological Genomics null, Imperial College London, UK", "INSDC_first_public": "2013-12-31T17:02:34Z", "INSDC_last_update": "2013-02-07T16:43:15Z", "INSDC_status": "public", "Submitter_Id": "SHRXBN4", "bio_material": "left ventricle of the heart", "common_name": "Norway rat", "sample_name": "SHRXBN4"}
ERX202465 Heart 0.624 8.9 34708 1106.1 1731 854.0 375 41513.6 0.913 title: ERX202465; {"ENA-FIRST-PUBLIC": "2013-12-31", "ENA-LAST-UPDATE": "2013-02-07", "ENA-SUBMISSION-TOOL": "SRA-Webin", "External_Id": "SAMEA2047056", "INSDC_center_alias": "ICL-PGM", "INSDC_center_name": "Physiological Genomics null, Imperial College London, UK", "INSDC_first_public": "2013-12-31T17:02:34Z", "INSDC_last_update": "2013-02-07T16:43:15Z", "INSDC_status": "public", "Submitter_Id": "SHRXBN4", "bio_material": "left ventricle of the heart", "common_name": "Norway rat", "sample_name": "SHRXBN4"}
ERX202466 Heart 0.637 11.2 35725 1072.6 2077 868.4 518 33146.2 0.913 title: ERX202466; {"ENA-FIRST-PUBLIC": "2013-12-31", "ENA-LAST-UPDATE": "2013-02-07", "ENA-SUBMISSION-TOOL": "SRA-Webin", "External_Id": "SAMEA2047056", "INSDC_center_alias": "ICL-PGM", "INSDC_center_name": "Physiological Genomics null, Imperial College London, UK", "INSDC_first_public": "2013-12-31T17:02:34Z", "INSDC_last_update": "2013-02-07T16:43:15Z", "INSDC_status": "public", "Submitter_Id": "SHRXBN4", "bio_material": "left ventricle of the heart", "common_name": "Norway rat", "sample_name": "SHRXBN4"}

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.