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Pancreatic Cancer Patient Survival Correlates with DNA Methylation of Pancreas Development Genes

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Human NormalPancreas1
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Human PancreaticCancer9
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Human PancreaticCancer5
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Human PancreaticCancer7
Human PancreaticCancer6
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Assembly: Human Dec. 2013 (GRCh38/hg38)

Thompson_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_PancreaticCancer9 Pancreatic cancer sample 9 WGBS 0.000 0.618 1.939 0.315 40703 0 0 LowCov; Download
Human_PancreaticCancer8 Pancreatic cancer sample 8 WGBS 0.000 0.603 1.704 0.302 38547 0 0 LowCov; Download
Human_PancreaticCancer5 Pancreatic cancer sample 5 WGBS 0.000 0.574 1.245 0.251 25341 0 14 LowCov; Download
Human_PancreaticCancer4 Pancreatic cancer sample 4 WGBS 0.000 0.588 0.422 0.128 19031 0 13 LowCov; Download
Human_PancreaticCancer7 Pancreatic cancer sample 7 WGBS 0.000 0.562 1.258 0.253 25592 0 0 LowCov; Download
Human_PancreaticCancer6 Pancreatic cancer sample 6 WGBS 0.000 0.542 1.383 0.252 22130 0 0 LowCov; Download
Human_PancreaticCancer1 Pancreatic cancer sample 1 WGBS 0.000 0.565 1.666 0.337 27817 0 131 LowCov; Download
Human_PancreaticCancer3 Pancreatic cancer sample 3 WGBS 0.000 0.595 1.724 0.296 31431 0 0 LowCov; Download
Human_PancreaticCancer2 Pancreatic cancer sample 2 WGBS 0.000 0.602 1.902 0.307 37190 0 0 LowCov; Download
Human_NormalPancreas1 Normal pancreas sample 1 WGBS 0.988 0.553 1.922 0.292 24910 0 0 LowCov; Download
Human_NormalPancreas2 Normal pancreas sample 2 WGBS 0.000 0.565 1.992 0.317 28179 0 0 LowCov; Download
Human_PancreaticCancer11 Pancreatic cancer sample 11 WGBS 0.000 0.593 1.672 0.259 32983 0 0 LowCov; Download
Human_PancreaticCancer10 Pancreatic cancer sample 10 WGBS 0.000 0.593 2.093 0.320 35387 0 0 LowCov; 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 Thompson MJ 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

Thompson MJ, Rubbi L, Dawson DW, Donahue TR, Pellegrini M Pancreatic cancer patient survival correlates with DNA methylation of pancreas development genes. PLoS ONE. 2015 10(6):e0128814