Mouse methylome studies SRP109758 Track Settings
 
Cytosine methylation dynamics during spermatogenesis and post-testicular sperm maturation [Primary, Sperm]

Track collection: Mouse methylome studies

+  All tracks in this collection (604)

Maximum display mode:       Reset to defaults   
Select views (Help):
HMR       CpG reads ▾       CpG methylation ▾       PMD       AMR      
Select subtracks by views and experiment:
 All views HMR  CpG reads  CpG methylation  PMD  AMR 
experiment
SRX2934498 
SRX2934499 
SRX2934500 
SRX2934501 
SRX2934502 
SRX2934503 
SRX2934504 
SRX2934505 
SRX2934506 
SRX2934507 
SRX2934508 
SRX2934509 
SRX2934510 
SRX2934511 
List subtracks: only selected/visible    all    ()
  experiment↓1 views↓2   Track Name↓3  
hide
 SRX2934498  HMR  Sperm / SRX2934498 (HMR)   Schema 
hide
 Configure
 SRX2934498  CpG methylation  Sperm / SRX2934498 (CpG methylation)   Schema 
hide
 SRX2934499  HMR  Sperm / SRX2934499 (HMR)   Schema 
hide
 Configure
 SRX2934499  CpG methylation  Sperm / SRX2934499 (CpG methylation)   Schema 
hide
 SRX2934500  HMR  Sperm / SRX2934500 (HMR)   Schema 
hide
 Configure
 SRX2934500  CpG methylation  Sperm / SRX2934500 (CpG methylation)   Schema 
hide
 SRX2934501  HMR  Sperm / SRX2934501 (HMR)   Schema 
hide
 Configure
 SRX2934501  CpG methylation  Sperm / SRX2934501 (CpG methylation)   Schema 
hide
 SRX2934502  HMR  Sperm / SRX2934502 (HMR)   Schema 
hide
 Configure
 SRX2934502  CpG methylation  Sperm / SRX2934502 (CpG methylation)   Schema 
hide
 SRX2934503  HMR  Primary / SRX2934503 (HMR)   Schema 
hide
 Configure
 SRX2934503  CpG methylation  Primary / SRX2934503 (CpG methylation)   Schema 
hide
 SRX2934504  HMR  Sperm / SRX2934504 (HMR)   Schema 
hide
 Configure
 SRX2934504  CpG methylation  Sperm / SRX2934504 (CpG methylation)   Schema 
hide
 SRX2934505  HMR  Sperm / SRX2934505 (HMR)   Schema 
hide
 Configure
 SRX2934505  CpG methylation  Sperm / SRX2934505 (CpG methylation)   Schema 
hide
 SRX2934506  HMR  Sperm / SRX2934506 (HMR)   Schema 
hide
 Configure
 SRX2934506  CpG methylation  Sperm / SRX2934506 (CpG methylation)   Schema 
hide
 SRX2934507  HMR  Sperm / SRX2934507 (HMR)   Schema 
hide
 Configure
 SRX2934507  CpG methylation  Sperm / SRX2934507 (CpG methylation)   Schema 
hide
 SRX2934508  HMR  Sperm / SRX2934508 (HMR)   Schema 
hide
 Configure
 SRX2934508  CpG methylation  Sperm / SRX2934508 (CpG methylation)   Schema 
hide
 SRX2934509  HMR  Sperm / SRX2934509 (HMR)   Schema 
hide
 Configure
 SRX2934509  CpG methylation  Sperm / SRX2934509 (CpG methylation)   Schema 
hide
 SRX2934510  HMR  Primary / SRX2934510 (HMR)   Schema 
hide
 Configure
 SRX2934510  CpG methylation  Primary / SRX2934510 (CpG methylation)   Schema 
hide
 SRX2934511  HMR  Sperm / SRX2934511 (HMR)   Schema 
hide
 Configure
 SRX2934511  CpG methylation  Sperm / SRX2934511 (CpG methylation)   Schema 
    

Study title: Cytosine methylation dynamics during spermatogenesis and post-testicular sperm maturation
SRA: SRP109758
GEO: GSE100220
Pubmed: not found

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX2934498 Sperm 0.743 9.8 38028 1528.4 951 813.3 2237 42513.4 0.983 title: GSM2674819 Caput sperm 1, Mus musculus, Bisulfite-Seq; {"source_name": "Caput sperm", "strain": "FVB"}
SRX2934499 Sperm 0.773 13.7 65412 1881.1 241 842.0 4921 39830.8 0.982 title: GSM2674820 Cauda sperm 1, Mus musculus, Bisulfite-Seq; {"source_name": "Cauda sperm", "strain": "FVB"}
SRX2934500 Sperm 0.765 10.2 58620 1733.3 139 884.2 2949 63485.9 0.982 title: GSM2674821 Corpus sperm 1, Mus musculus, Bisulfite-Seq; {"source_name": "Corpus sperm", "strain": "FVB"}
SRX2934501 Sperm 0.745 9.7 62585 1631.8 90 877.7 2843 63695.9 0.983 title: GSM2674822 Later round spermatids 1, Mus musculus, Bisulfite-Seq; {"source_name": "Later round spermatids", "strain": "FVB"}
SRX2934502 Sperm 0.751 9.1 63067 1707.8 82 941.2 2934 66498.1 0.982 title: GSM2674823 Early round spermatids 1, Mus musculus, Bisulfite-Seq; {"source_name": "Early round spermatids", "strain": "FVB"}
SRX2934503 Primary 0.753 15.0 71501 1819.2 158 810.7 4873 41820.3 0.983 title: GSM2674824 Primary spermatocytes 1, Mus musculus, Bisulfite-Seq; {"source_name": "Primary spermatocytes", "strain": "FVB"}
SRX2934504 Sperm 0.752 9.3 48095 1680.7 551 846.4 2553 55566.5 0.983 title: GSM2674825 Vas deferens sperm 1, Mus musculus, Bisulfite-Seq; {"source_name": "Vas deferens sperm", "strain": "FVB"}
SRX2934505 Sperm 0.682 11.6 27521 1328.6 1630 884.4 1052 40080.3 0.986 title: GSM2674826 Caput sperm 2, Mus musculus, Bisulfite-Seq; {"source_name": "Caput sperm", "strain": "FVB"}
SRX2934506 Sperm 0.755 8.0 38784 1472.8 1035 876.4 1197 74953.8 0.986 title: GSM2674827 Cauda sperm 2, Mus musculus, Bisulfite-Seq; {"source_name": "Cauda sperm", "strain": "FVB"}
SRX2934507 Sperm 0.741 10.7 43344 1472.6 1662 868.4 1267 72737.7 0.987 title: GSM2674828 Corpus sperm 2, Mus musculus, Bisulfite-Seq; {"source_name": "Corpus sperm", "strain": "FVB"}
SRX2934508 Sperm 0.716 13.4 56212 1415.0 352 888.3 1853 65935.8 0.987 title: GSM2674829 Later round spermatids 2, Mus musculus, Bisulfite-Seq; {"source_name": "Later round spermatids", "strain": "FVB"}
SRX2934509 Sperm 0.722 15.2 58056 1419.1 359 835.1 2164 70547.7 0.987 title: GSM2674830 Early round spermatids 2, Mus musculus, Bisulfite-Seq; {"source_name": "Early round spermatids", "strain": "FVB"}
SRX2934510 Primary 0.736 8.1 51902 1469.1 240 889.1 1174 111327.4 0.986 title: GSM2674831 Primary spermatocytes 2, Mus musculus, Bisulfite-Seq; {"source_name": "Primary spermatocytes", "strain": "FVB"}
SRX2934511 Sperm 0.692 14.0 26760 1312.0 1428 887.0 1238 29757.3 0.987 title: GSM2674832 Vas deferens sperm 2, Mus musculus, Bisulfite-Seq; {"source_name": "Vas deferens sperm", "strain": "FVB"}

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.