Mouse methylome studies SRP045054 Track Settings
 
The developmental potential of iPSCs is greatly influenced by the selection of the reprogramming factors [ESC, Induced Pluripotent Stem Cell]

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Study title: The developmental potential of iPSCs is greatly influenced by the selection of the reprogramming factors
SRA: SRP045054
GEO: GSE59696
Pubmed: 25192464

Experiment Label Methylation Coverage HMRs HMR size AMRs AMR size PMDs PMD size Conversion Details
SRX665150 Induced Pluripotent Stem Cell 0.708 9.1 62428 1523.7 455 992.6 4117 27336.4 0.994 title: GSM1448286 BC_2 OKSM, Mus musculus, Bisulfite-Seq; {"source_name": "BC_2 OKSM", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Induced pluripotent stem cells", "echotype": "pluripotent"}
SRX665151 Induced Pluripotent Stem Cell 0.675 9.3 59410 1488.5 409 1059.5 3839 27832.9 0.994 title: GSM1448287 KH2 OKSM, Mus musculus, Bisulfite-Seq; {"source_name": "KH2 OKSM", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Induced pluripotent stem cells", "echotype": "pluripotent"}
SRX665152 ESC 0.724 11.6 55129 1345.6 337 983.8 4968 12227.6 0.994 title: GSM1448288 KH2 ESC, Mus musculus, Bisulfite-Seq; {"source_name": "KH2 ESC", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Embryonic stem cells", "echotype": "pluripotent"}
SRX665153 Induced Pluripotent Stem Cell 0.560 9.4 58642 1714.7 293 1002.1 3504 31857.2 0.995 title: GSM1448289 Nanog-GFP OKSM#2, Mus musculus, Bisulfite-Seq; {"source_name": "Nanog-GFP OKSM", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Induced pluripotent stem cells", "echotype": "pluripotent"}
SRX665154 ESC 0.739 9.4 51297 1360.7 413 1059.5 3277 18325.6 0.995 title: GSM1448290 Nanog-GFP ESCs, Mus musculus, Bisulfite-Seq; {"source_name": "Nanog-GFP ESCs", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Embryonic stem cells", "echotype": "pluripotent"}
SRX665155 Induced Pluripotent Stem Cell 0.739 9.8 52781 1368.8 396 1017.1 2961 20249.3 0.995 title: GSM1448291 Nanog-GFP SNEL#2, Mus musculus, Bisulfite-Seq; {"source_name": "Nanog-GFP SNEL", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Induced pluripotent stem cells", "echotype": "pluripotent"}
SRX665156 Induced Pluripotent Stem Cell 0.696 9.1 48921 1504.1 366 1011.5 2986 20473.7 0.995 title: GSM1448292 Nanog-GFP SNEL#3, Mus musculus, Bisulfite-Seq; {"source_name": "Nanog-GFP SNEL", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Induced pluripotent stem cells", "echotype": "pluripotent"}
SRX665157 ESC 0.664 9.5 54444 1552.4 396 984.8 3132 23457.5 0.995 title: GSM1448293 Oct4-GFP ESCs, Mus musculus, Bisulfite-Seq; {"source_name": "Oct4-GFP ESCs", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Embryonic stem cells", "echotype": "pluripotent"}
SRX665158 Induced Pluripotent Stem Cell 0.342 9.5 41611 3131.9 170 995.0 1548 149502.0 0.994 title: GSM1448294 Oct4-GFP OKSM#2, Mus musculus, Bisulfite-Seq; {"source_name": "Oct4-GFP OKSM", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Induced pluripotent stem cells", "echotype": "pluripotent"}
SRX665159 Induced Pluripotent Stem Cell 0.659 9.5 53377 1526.1 327 988.1 2986 23280.3 0.994 title: GSM1448295 Oct4-GFP SNEL#1, Mus musculus, Bisulfite-Seq; {"source_name": "Oct4-GFP SNEL", "strain_background": "mixed; 129Sv/Jae and C57/Bl6", "cell_type": "Induced pluripotent stem cells", "echotype": "pluripotent"}

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.