GEO series

Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tissue-of-origin mapping from plasma DNA [WGBS]

GSE79215 Homo sapiens (human) DNA methylation 10 samples Submitted 2017/02/19 Platform GPL11154

At a glance

DNA methylation · human · 10 samples

What it is: DNA methylationChemical marks on DNA that help switch genes on or off.
Organism: Homo sapiens (human)
Samples: 10 samples
Data you can get: A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.

Summary (written by the submitters)

Adjacent CpG sites in mammalian genomes tend to be co-methylated due to the processivity of enzymes responsible for adding or removing the methyl group. Yet discordant methylation patterns have also be observed, and found to be related to stochastic or uncoordinated molecular processes. Here we focused on a systematic search and investigation of regions in the human genome that exhibit highly coordinated methylation.

Read the full summary ↓

By examining the co-methylation patterns of multiple adjacent CpG sites, termed methylation haplotypes, in single bisulfite sequencing reads, we applied a greedy-searching strategy to defined blocks of tightly coupled CpG sites, called Methylation Haplotype Blocks (MHBs), based on 53 sets of whole genome bisulfite sequencing (WGBS) data, including 43 published sets from human adult tissues, ESC and in vitro differentiated cell lines, as well as 10 sets from human adult tissues generated in this study. The MHBs were then further validated with 101 sets of RRBS ENCODE data, and 637 sets of Illumina 450k methylation array data from TCGA tumor and normal samples. Globally, MHBs are enriched in but only partially overlap with several well-known genomic features, including CpG islands, promoters, enhancers and VMRs. To perform quantitative analysis of the MHBs, we defined a metric called Methylation Haplotype Load (MHL), which is covered both average methylation level and methylation complexity and therefore more informative than average methylation level or Shannon entropy. Using a feature selection strategy, we identified a set of tissue-specific MHBs that cluster by developmental germ-layers. Interestingly, examination of these MHBs revealed two distinct mechanisms for fate commitment during development: epigenetic silencing of pluripotent genes, such as NANOG, for mesoderm induction; and epigenetic induction (or de-suppression) of lineage-specific factors for ectoderm commitment.

This dataset

Open GSE79215 in finder → Find more human methylation datasets →

Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE79215_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA315194 and SRA study SRP071771. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28263317) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 10 more — browse all 10 samples with per-sample file links →

Datasets on a similar topic

GSE79279 Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tissue-of-origin mapping from plasma DNA 159 samples shares tissue-of-origin, deconvolution, heterogeneous, methylation, haplotype, mapping, blocks
GSE79277 Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tissue-of-origin mapping from plasma DNA [Plasma RRBS] 134 samples shares tissue-of-origin, deconvolution, heterogeneous, methylation, haplotype, mapping, blocks
GSE319444 DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML [PrimaryAML_WGBS] 20 samples shares wgbs, methylation
GSE319448 DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML [CellCulture_WGBS] 12 samples shares wgbs, methylation
GSE86832 Critical evaluation of the Illumina MethylationEPIC BeadChip microarray for whole-genome DNA methylation profiling [WGBS] 9 samples shares wgbs, methylation
GSE110554 FlowSorted.Blood.EPIC: An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the Illumina HumanMethylationEPIC BeadArray (II) 49 samples shares deconvolution, methylation
GSE307587 Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens [snRNA-seq] 20 samples shares heterogeneous, mapping
GSE237279 Integrative Analysis of Genome-Wide Epigenetic and Transcriptomic Alterations Reveals Molecular Markers for Diagnosing Pediatric Obstructive Sleep Apnea in Black Females [WGBS] 18 samples shares wgbs

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.