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De novo DNA methylation during monkey pre-implantation embryogenesis.

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Gao_Rhesus_2017

Description

Sample BS rate* Methylation Coverage %CpGs #HMR #AMR #PMD
Rhesus_Embryo.2cell T-WGBS for rhesus monkey 2-cell embryos WGBS 0.637 0.653 3.598 0.756 50278 1145 50278 LowBS; LowCov; Download
Rhesus_ICM T-WGBS for rhesus monkey inner cell mass (ICM) of the blastocysts WGBS 0.930 0.457 0.663 0.235 0 774 0 LowBS; LowCov; Download
Rhesus_Embryo.8cell T-WGBS for rhesus monkey 8-cell embryos WGBS 0.579 0.712 2.060 0.478 9798 1603 0 LowBS; LowCov; Download
Rhesus_Zygote T-WGBS for rhesus monkey zygotes WGBS 0.233 0.816 6.799 0.835 9304 9807 9304 LowBS; Download
Rhesus_Sperm T-WGBS for rhesus monkey sperm WGBS 0.995 0.792 20.379 0.926 72242 62923 5456 Download
Rhesus_Morula T-WGBS for rhesus monkey morula WGBS 0.609 0.508 1.169 0.279 8487 14838 8487 LowBS; LowCov; Download

* see Methods section for how the bisulfite conversion rate is calculated
Sample flag:
LowBS:  sample has low bisulfite conversion rate (<0.95);
LowCov:  sample has low mean coverage (<6.0)

Terms of use: If you use this resource, please cite us! The Smith Lab at USC has developed and is owner of all analyses and associated browser tracks from the MethBase database (e.g. tracks displayed in the "DNA Methylation" trackhub on the UCSC Genome Browser). Any derivative work or use of the MethBase resource that appears in published literature must cite the most recent publication associated with Methbase (see "References" below). Users who wish to copy the contents of MethBase in bulk into a publicly available resource must additionally have explicit permission from the Smith Lab to do so. We hope the MethBase resource can help you!

Display Conventions and Configuration

The various types of tracks associated with a methylome follow the display conventions below. Green intervals represent partially methylated region; Blue intervals represent hypo-methylated regions; Yellow bars represent methylation levels; Black bars represent depth of coverage; Purple intervals represent allele-specific methylated regions; Purple bars represent allele-specific methylation score; and red intervals represent hyper-methylated regions.

Methods

All analysis was done using a bisulfite sequnecing data analysis pipeline MethPipe developed in the Smith lab at USC.

Mapping bisulfite treated reads: Bisulfite treated reads are mapped to the genomes with the rmapbs program (rmapbs-pe for pair-end reads), one of the wildcard based mappers. Input reads are filtered by their quality, and adapter sequences in the 3' end of reads are trimmed. Uniquely mapped reads with mismatches below given threshold are kept. For pair-end reads, if the two mates overlap, the overlapping part of the mate with lower quality is clipped. After mapping, we use the program duplicate-remover to randomly select one from multiple reads mapped exactly to the same location.

Estimating methylation levels: After reads are mapped and filtered, the methcounts program is used to obtain read coverage and estimate methylation levels at individual cytosine sites. We count the number of methylated reads (containing C's) and the number of unmethylated reads (containing T's) at each cytosine site. The methylation level of that cytosine is estimated with the ratio of methylated to total reads covering that cytosine. For cytosines within the symmetric CpG sequence context, reads from the both strands are used to give a single estimate.

Estimating bisulfite conversion rate: Bisulfite conversion rate is estimated with the bsrate program by computing the fraction of successfully converted reads (read out as Ts) among all reads mapped to presumably unmethylated cytosine sites, for example, spike-in lambda DNA, chroloplast DNA or non-CpG cytosines in mammalian genomes.

Identifying hypo-methylated regions: In most mammalian cells, the majority of the genome has high methylation, and regions of low methylation are typically more interesting. These are called hypo-methylated regions (HMR). To identify the HMRs, we use the hmr which implements a hidden Markov model (HMM) approach taking into account both coverage and methylation level information.

Identifying hyper-methylated regions: Hyper-methylated regions (HyperMR) are of interest in plant methylomes, invertebrate methylomes and other methylomes showing "mosaic methylation" pattern. We identify HyperMRs with the hmr_plant program for those samples showing "mosaic methylation" pattern.

Identifying 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.

Identifying allele-specific methylated regions: Allele-Specific methylated regions refers to regions where the parental allele is differentially methylated compared to the maternal allele. The program allelicmeth is used to 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 reference by Song et al. For instructions on how to use MethPipe, you may obtain the MethPipe Manual.

Credits

The raw data were produced by Gao F et al. The data analysis were performed by members of the Smith lab.

Contact: Benjamin Decato and Liz Ji

Terms of Use

If you use this resource, please cite us! The Smith Lab at USC has developed and is owner of all analyses and associated browser tracks from the MethBase database (e.g. tracks displayed in the "DNA Methylation" trackhub on the UCSC Genome Browser). Any derivative work or use of the MethBase resource that appears in published literature must cite the most recent publication associated with Methbase (see "References" below). Users who wish to copy the contents of MethBase in bulk into a publicly available resource must additionally have explicit permission from the Smith Lab to do so. We hope the MethBase resource can help you!

References

MethPipe and MethBase

Song Q, Decato B, Hong E, Zhou M, Fang F, Qu J, Garvin T, Kessler M, Zhou J, Smith AD (2013) A reference methylome database and analysis pipeline to facilitate integrative and comparative epigenomics. PLOS ONE 8(12): e81148

Data sources

Gao F, Niu Y, Sun YE, Lu H, Chen Y, Li S, Kang Y, Luo Y, Si C, Yu J, et al De novo DNA methylation during monkey pre-implantation embryogenesis. Cell Res.. 2017 27(4):526-539