[Pub] Liao-Human-2015 Track Settings
 
Targeted disruption of DNMT1, DNMT3A and DNMT3B in human embryonic stem cells.

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Human HUES64Dnmt3a3bDkoLate
Human HUES64Dnmt3aKoLate
Human HUES64Dnmt3a3bDkoEarly
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Human HUES64Dnmt3aKoCD184
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 Human HUES64Dnmt3a3bDkoLate  partially methylated domain  Human_HUES64Dnmt3a3bDkoLate_PMD   Data format 
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 Human HUES64Dnmt3a3bDkoLate  hypomethylated regions  Human_HUES64Dnmt3a3bDkoLate_HMR   Data format 
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 Human HUES64Dnmt3a3bDkoLate  methylation level  Human_HUES64Dnmt3a3bDkoLate_Meth   Data format 
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 Human HUES64Dnmt3a3bDkoLate  coverage  Human_HUES64Dnmt3a3bDkoLate_Read   Data format 
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 Human HUES64Dnmt3aKoLate  partially methylated domain  Human_HUES64Dnmt3aKoLate_PMD   Data format 
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 Human HUES64Dnmt3aKoLate  hypomethylated regions  Human_HUES64Dnmt3aKoLate_HMR   Data format 
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 Human HUES64Dnmt3aKoLate  methylation level  Human_HUES64Dnmt3aKoLate_Meth   Data format 
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 Human HUES64Dnmt3aKoLate  coverage  Human_HUES64Dnmt3aKoLate_Read   Data format 
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 Human HUES64Dnmt3a3bDkoEarly  partially methylated domain  Human_HUES64Dnmt3a3bDkoEarly_PMD   Data format 
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 Human HUES64Dnmt3a3bDkoEarly  hypomethylated regions  Human_HUES64Dnmt3a3bDkoEarly_HMR   Data format 
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 Human HUES64Dnmt3a3bDkoEarly  methylation level  Human_HUES64Dnmt3a3bDkoEarly_Meth   Data format 
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 Human HUES64Dnmt3a3bDkoEarly  coverage  Human_HUES64Dnmt3a3bDkoEarly_Read   Data format 
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 Human HUES64Dnmt3bKoLate  partially methylated domain  Human_HUES64Dnmt3bKoLate_PMD   Data format 
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 Human HUES64Dnmt3bKoLate  methylation level  Human_HUES64Dnmt3bKoLate_Meth   Data format 
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 Human HUES64Dnmt3bKoLate  coverage  Human_HUES64Dnmt3bKoLate_Read   Data format 
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 Human HUES64Dnmt3aKoCD184  partially methylated domain  Human_HUES64Dnmt3aKoCD184_PMD   Data format 
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 Human HUES64Dnmt3aKoCD184  hypomethylated regions  Human_HUES64Dnmt3aKoCD184_HMR   Data format 
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 Human HUES64Dnmt3aKoCD184  methylation level  Human_HUES64Dnmt3aKoCD184_Meth   Data format 
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 Human HUES64Dnmt3aKoCD184  coverage  Human_HUES64Dnmt3aKoCD184_Read   Data format 
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 Human HUES64Dnmt3aKoEarly  partially methylated domain  Human_HUES64Dnmt3aKoEarly_PMD   Data format 
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 Human HUES64Dnmt3aKoEarly  hypomethylated regions  Human_HUES64Dnmt3aKoEarly_HMR   Data format 
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 Human HUES64Dnmt3aKoEarly  methylation level  Human_HUES64Dnmt3aKoEarly_Meth   Data format 
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 Human HUES64Dnmt3aKoEarly  coverage  Human_HUES64Dnmt3aKoEarly_Read   Data format 
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 Human HUES64Dnmt3bKoEarly  partially methylated domain  Human_HUES64Dnmt3bKoEarly_PMD   Data format 
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 Human HUES64Dnmt3bKoEarly  hypomethylated regions  Human_HUES64Dnmt3bKoEarly_HMR   Data format 
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 Human HUES64Dnmt3bKoEarly  methylation level  Human_HUES64Dnmt3bKoEarly_Meth   Data format 
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 Human HUES64Dnmt3bKoEarly  coverage  Human_HUES64Dnmt3bKoEarly_Read   Data format 
    
Assembly: Human Dec. 2013 (GRCh38/hg38)

Liao_Human_2015
We are planning to introduce the new version of methylone track hubs sometime between February 7 and February 14 2024. The following assemblies will be updated: mm39, gorGor6, canFam6, GCF_000001735.3, rn7, panTro6, hg38.

Description

Sample BS rate* Methylation Coverage %CpGs #HMR #AMR #PMD
Human_HUES64Dnmt3a3bDkoLate HUES64 DNMT3A 3B DKO late WGBS 0.999 0.640 8.198 0.936 100291 0 8054 Download
Human_HUES64Dnmt3bKoLate HUES64 DNMT3B KO late WGBS 0.000 0.000 0.000 0.000 0 0 0 Download
Human_HUES64Dnmt3aKoCD184 DNMT3A KO derived CD184 WGBS 0.000 0.000 0.000 0.000 0 0 0 Download
Human_HUES64Dnmt3aKoLate HUES64 DNMT3A KO late WGBS 0.000 0.000 0.000 0.000 0 0 0 Download
Human_HUES64Dnmt3a3bDkoEarly HUES64 DNMT3A 3B DKO early WGBS 0.985 0.738 4.912 0.937 66064 0 6850 LowCov; Download
Human_HUES64Dnmt3aKoEarly HUES64 DNMT3A KO early WGBS 0.000 0.000 0.000 0.000 0 0 0 Download
Human_HUES64Dnmt3bKoEarly HUES64 DNMT3B KO early WGBS 0.000 0.000 0.000 0.000 0 0 0 Download

* see Methods section for how the bisulfite conversion rate is calculated
Sample flag:
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 Liao J 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

Liao J, Karnik R, Gu H, Ziller MJ, Clement K, Tsankov AM, Akopian V, Gifford CA, Donaghey J, Galonska C, et al Targeted disruption of DNMT1, DNMT3A and DNMT3B in human embryonic stem cells. Nat. Genet.. 2015 47(5):469-78