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ASXL1 Mutations Promote Myeloid Transformation Through Loss of PRC2-Mediated Gene Repression

GSE38861 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 9 samples Submitted 2012/08/21 Platform GPL9052
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 9 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Samples
9 samples: 9 samples, each with a distinct title — no replicated groupsGroups 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)
Recurrent somatic ASXL1 mutations occur in patients with myelodysplasia (MDS), myeloproliferative neoplasms (MPN), and acute myeloid leukemia (AML), and are associated with adverse outcome. Despite the genetic and clinical data implicating ASXL1 mutations in myeloid malignancies, the mechanisms of transformation by ASXL1 mutations are not understood.
Read the full summary ↓
Here we identify that ASXL1 mutations result in loss of PRC2-mediated histone H3 lysine 27 (H3K27) tri-methylation. Through integration of microarray data with genome-wide histone modification ChIP-Seq data we identify targets of ASXL1 repression including the posterior HOXA cluster that is known to contribute to myeloid transformation. We demonstrate that ASXL1 associates with the Polycomb repressive complex 2 (PRC2), and that loss of ASXL1 in vivo collaborates with NRASG12D to promote myeloid leukemogenesis.
This dataset
Open GSE38861 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE38861_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 peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA169078 and SRA study SRP013855. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 22897849) ↗
Study design
9 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 9 sample titles: 9 distinct titles with little repetition. 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
Datasets on a similar topic

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