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The long noncoding RNAs NEAT1 and MALAT1 bind active chromatin sites

GSE58444 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 66 samples Submitted 2014/08/12 Platform GPL11154
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 66 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
66 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)
Long noncoding RNAs (lncRNAs) are important regulators of chromatin; however, the mechanistic roles for many lncRNAs are poorly understood in part because their direct interactions with genomic loci and proteins are difficult to assess. We used CHART-seq to map the genomic binding sites for two highly expressed human lncRNAs, NEAT1 and MALAT1, which localize within the nucleus to paraspeckles and nuclear speckles, respectively.
Read the full summary ↓
We show that NEAT1 and MALAT1 localize to hundreds of genomic sites in human cells, primarily over active genes. NEAT1 and MALAT1 exhibit colocalization to many of these loci, but display distinct gene body binding patterns at these sites, suggesting independent but complementary functions for these RNAs. Protein mass spectrometry analysis of CHART-enriched material (CHART-MS) identified numerous proteins enriched by both lncRNAs, supporting complementary binding and function, in addition to unique associated proteins. Transcriptional inhibition or stimulation affects the localization of NEAT1 to active chromatin sites, implying that DNA sequence itself does not target NEAT1 to chromatin and that localization responds to cues involved in the transcription process.
This dataset
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.

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 GSE58444_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA252626 and SRA study SRP043173. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 66 more — browse all 66 samples with per-sample file links →

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