Muscle Track Settings
 
Muscle

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Human Fetal Heart
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 Human Fetal Heart  Roadmap-Human-2015  hypomethylated regions  Roadmap 2015 : Human_Fetal-Heart_HMR   Data format 
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 Human Fetal Heart  Roadmap-Human-2015  methylation level  Roadmap 2015 : Human_Fetal-Heart_Meth   Data format 
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 Human Lung  Roadmap-Human-2015  methylation level  Roadmap 2015 : Human_Lung_Meth   Data format 
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 Human fMuscle leg  Roadmap-Human-2015  hypomethylated regions  Roadmap 2015 : Human_fMuscle-leg_HMR   Data format 
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 Human fMuscle leg  Roadmap-Human-2015  methylation level  Roadmap 2015 : Human_fMuscle-leg_Meth   Data format 
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 Human Fetal Stomach  Roadmap-Human-2015  hypomethylated regions  Roadmap 2015 : Human_Fetal-Stomach_HMR   Data format 
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 Human Fetal Stomach  Roadmap-Human-2015  methylation level  Roadmap 2015 : Human_Fetal-Stomach_Meth   Data format 
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 Human Heart Aorta  Roadmap-Human-2015  hypomethylated regions  Roadmap 2015 : Human_Heart-Aorta_HMR   Data format 
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 Human Heart Aorta  Roadmap-Human-2015  methylation level  Roadmap 2015 : Human_Heart-Aorta_Meth   Data format 
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 Human Bladder  Roadmap-Human-2015  hypomethylated regions  Roadmap 2015 : Human_Bladder_HMR   Data format 
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 Human Bladder  Roadmap-Human-2015  methylation level  Roadmap 2015 : Human_Bladder_Meth   Data format 
    
Assembly: Human Feb. 2009 (GRCh37/hg19)

Muscle

Description

Sample BS rate* Methylation Coverage %CpGs #HMR #AMR #PMD
Human_fMuscle-trunk Fetal Muscle (trunk) WGBS 0.997 0.672 36.171 0.958 69990 0 0 Download
Human_fMuscle-leg Fetal Muscle (leg) WGBS 0.997 0.681 26.774 0.943 68161 0 0 Download
Human_Fetal-Heart Fetal Heart WGBS 0.996 0.755 37.134 0.965 64186 0 0 Download
Human_Heart-Aorta Aortal cell line WGBS 0.995 0.794 27.635 0.940 87762 45096 0 Download
Human_Left-ventrical Left ventrical WGBS 0.995 0.789 110.258 0.974 66565 0 0 Download
Human_Bladder Human bladder cells WGBS 0.995 0.786 55.066 0.957 70512 0 0 Download
Human_Fetal-Stomach Fetal Stomach WGBS 0.997 0.710 35.901 0.961 68618 0 0 Download
Human_HealthyGastric Healthy donor gastric tissue WGBS 0.995 0.800 23.450 0.952 56784 76099 0 Download
Human_Lung Lung WGBS 0.996 0.809 78.184 0.960 57696 0 0 Download
Human_Small-intestine Human small intestine WGBS 0.000 0.000 0.000 0.000 60395 0 0 Download
Human_Fetal-Intestine-Large Fetal Large Intestine WGBS 0.991 0.838 14.899 0.958 59218 0 0 Download

* see Methods section for how the bisulfite conversion rate is calculated

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 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

, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, et al Integrative analysis of 111 reference human epigenomes. Nature. 2015 518(7539):317-30