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Global Epigenomic Reconfiguration During Mammalian Brain Development

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Mouse FrontCortexNeuronFemale6Wk
Human DorsPrefrontNonNeuron53Yr
Mouse FrontCortexNonNeuronMale7Wk
Mouse FrontCortexMale22Mo
Human MidFrontGyr25Yr
Mouse FrontCortexMale2Wk
Human DorsPrefrontNeuronMale55Yr
Mouse FrontCortexMale4Wk
Mouse FrontCortexMale6Wk
Mouse FrontCortexNonNeuronFemale6Wk
Human MidFrontGyr2Yr
Mouse FrontCortexNonNeuronFemale12Mo
Mouse FrontCortex8WkTet2Mut
Human MidFrontGyr35Day
Mouse FetalFrontCortex
Human FrontCortexFemale64Yr
Human MidFrontGyr5Yr
Human DorsPrefrontNonNeuronMale55Yr
Human FetalCerebCortex
Mouse FrontCortexNeuronMale7Wk
Human DorsPrefrontNeuron53Yr
Mouse FrontCortexNeuronFemale12Mo
Mouse FrontCortexMale10Wk
Human MidFrontGyr12Yr
Human HUES6
Mouse FrontCortexMale1Wk
Mouse FrontCortexGliaS00bPos
Human MidFrontGyr16Yr
Human DorsPrefrontMale55YrTissue
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 Human DorsPrefrontNonNeuron53Yr  hypomethylated regions  Human_DorsPrefrontNonNeuron53Yr_HMR   Data format 
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 Human DorsPrefrontNonNeuron53Yr  methylation level  Human_DorsPrefrontNonNeuron53Yr_Meth   Data format 
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 Human MidFrontGyr25Yr  hypomethylated regions  Human_MidFrontGyr25Yr_HMR   Data format 
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 Human MidFrontGyr25Yr  methylation level  Human_MidFrontGyr25Yr_Meth   Data format 
    
Assembly: Human Feb. 2009 (GRCh37/hg19)

Lister_Brain_2013
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_DorsPrefrontMale55YrTissue 0.979 0.800 3.006 0.886 40840 839 4753 LowCov; Download
Human_DorsPrefrontNeuron53Yr 0.932 0.843 24.416 0.959 66284 25590 6545 LowBS; Download
Human_DorsPrefrontNeuronMale55Yr 0.939 0.860 13.682 0.958 66508 14209 5547 LowBS; Download
Human_DorsPrefrontNonNeuron53Yr 0.991 0.828 11.872 0.952 67023 4495 4783 Download
Human_DorsPrefrontNonNeuronMale55Yr 0.989 0.832 12.811 0.957 69925 4811 4058 Download
Human_FetalCerebCortex 0.995 0.787 25.042 0.956 86982 4489 3849 Download
Human_FrontCortexFemale64Yr 0.980 0.721 14.763 0.957 50154 31872 5669 Download
Human_HUES6 0.983 0.859 8.850 0.939 37100 1008 4185 LowCov; Download
Human_MidFrontGyr12Yr 0.982 0.827 19.482 0.961 53275 13994 3753 Download
Human_MidFrontGyr16Yr 0.980 0.828 19.550 0.961 52023 16380 3983 Download
Human_MidFrontGyr25Yr 0.951 0.828 9.981 0.961 44312 3055 3645 LowCov; Download
Human_MidFrontGyr2Yr 0.984 0.823 19.603 0.961 50823 13339 3422 Download
Human_MidFrontGyr35Day 0.991 0.815 19.166 0.961 61799 6339 3526 Download
Human_MidFrontGyr5Yr 0.985 0.826 19.495 0.961 54088 11823 3424 Download
Mouse_FetalFrontCortex 0.995 0.781 0.000 0.983 47853 256 3023 Download
Mouse_FrontCortex8WkTet2Mut 0.984 0.738 0.000 0.992 36511 3704 3324 Download
Mouse_FrontCortexGliaS00bPos 0.991 0.667 0.000 0.992 51004 2955 2402 Download
Mouse_FrontCortexMale10Wk 0.982 0.683 0.000 0.991 39496 4521 3314 Download
Mouse_FrontCortexMale1Wk 0.995 0.706 0.000 0.990 55554 818 2155 Download
Mouse_FrontCortexMale22Mo 0.982 0.721 0.000 0.992 38207 4069 3316 Download
Mouse_FrontCortexMale2Wk 0.988 0.692 0.000 0.992 39606 4340 2906 Download
Mouse_FrontCortexMale4Wk 0.976 0.726 0.000 0.992 38299 5819 3351 Download
Mouse_FrontCortexMale6Wk 0.980 0.737 0.000 0.992 37740 5305 3378 Download
Mouse_FrontCortexNeuronFemale12Mo 0.974 0.775 0.000 0.990 55736 2562 3788 Download
Mouse_FrontCortexNeuronFemale6Wk 0.974 0.765 0.000 0.988 53357 1429 3651 Download
Mouse_FrontCortexNeuronMale7Wk 0.973 0.784 0.000 0.987 56424 1568 3853 Download
Mouse_FrontCortexNonNeuronFemale12Mo 0.992 0.742 0.000 0.988 43721 943 2657 Download
Mouse_FrontCortexNonNeuronFemale6Wk 0.992 0.730 0.000 0.970 31218 302 2486 Download
Mouse_FrontCortexNonNeuronMale7Wk 0.991 0.745 0.000 0.991 35986 2948 2602 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 (<10.0)

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 . The data analysis were performed by members of the Smith lab.

Contact: Liz Ji and Benjamin Decato

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