GEO series

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

GSE79277 Homo sapiens (human) DNA methylation 134 samples Submitted 2017/02/19 Platform GPL15520Platform GPL16791

At a glance

DNA methylation · human · 134 samples

What it is: DNA methylationChemical marks on DNA that help switch genes on or off.
Organism: Homo sapiens (human)
Samples: 134 samples: 75 × NC-P vs 30 × CRC-P vs 29 × LC-PGroups are read from the sample names, not from the paper. Check the list below.
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 GSE79277 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 GSE79277_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 PRJNA315379 and SRA study SRP071841. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 28263317) ↗

Study design

75 × NC-P vs 30 × CRC-P vs 29 × LC-P

Supports a between-group comparison across 134 samples.

How we read this: 3 replicated groups read from 134 sample titles; they account for 134 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

GSM2090507 CRC-P-1 CRC-P
GSM2090508 CRC-P-10 CRC-P
GSM2090509 CRC-P-11 CRC-P
GSM2090510 CRC-P-12 CRC-P
GSM2090511 CRC-P-13 CRC-P
GSM2090512 CRC-P-14 CRC-P
GSM2090513 CRC-P-15 CRC-P
GSM2090514 CRC-P-16 CRC-P
GSM2090515 CRC-P-17 CRC-P
GSM2090516 CRC-P-18 CRC-P
GSM2090517 CRC-P-19 CRC-P
GSM2090518 CRC-P-2 CRC-P
GSM2090519 CRC-P-20 CRC-P
GSM2090520 CRC-P-21 CRC-P
GSM2090521 CRC-P-22 CRC-P
GSM2090522 CRC-P-23 CRC-P
GSM2090523 CRC-P-24 CRC-P
GSM2090524 CRC-P-25 CRC-P
GSM2090525 CRC-P-26 CRC-P
GSM2090526 CRC-P-27 CRC-P
GSM2090527 CRC-P-28 CRC-P
GSM2090528 CRC-P-29 CRC-P
GSM2090529 CRC-P-3 CRC-P
GSM2090530 CRC-P-30 CRC-P
GSM2090531 CRC-P-4 CRC-P
GSM2090532 CRC-P-5 CRC-P
GSM2090533 CRC-P-6 CRC-P
GSM2090534 CRC-P-7 CRC-P
GSM2090535 CRC-P-8 CRC-P
GSM2090536 CRC-P-9 CRC-P
GSM2090537 LC-P-1 LC-P
GSM2090538 LC-P-10 LC-P
GSM2090539 LC-P-11 LC-P
GSM2090540 LC-P-12 LC-P
GSM2090541 LC-P-13 LC-P
GSM2090542 LC-P-14 LC-P
GSM2090543 LC-P-15 LC-P
GSM2090544 LC-P-16 LC-P
GSM2090545 LC-P-17 LC-P
GSM2090546 LC-P-18 LC-P

+ 94 more — browse all 134 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
GSE79215 Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tissue-of-origin mapping from plasma DNA [WGBS] 10 samples shares tissue-of-origin, deconvolution, heterogeneous, methylation, haplotype, mapping, blocks
GSE25247 BI Human Reference Epigenome Mapping Project: Characterization of DNA methylation by RRBS in human subject 55 samples shares rrbs, methylation, mapping
GSE25246 BI Human Reference Epigenome Mapping Project: Characterization of DNA methylation by RRBS 10 samples shares rrbs, methylation, mapping
GSE269977 DNA methylation alterations of driver genes in blood of high-altitude pulmonary edema ( RRBS) 20 samples shares rrbs, 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
GSE264396 The disease modeling of Jacob Syndrome reveals an aberrant genome-wide expression modulation from the Y chromosome [RRBS] 31 samples shares rrbs

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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.