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CRISPRi-based genome-scale identification of functional long non-coding RNA loci in human cells

GSE85011 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 112 samples Submitted 2016/12/01 Platform GPL20301
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 112 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
112 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)
The human genome produces thousands of long non-coding RNAs (lncRNAs) – transcripts >200 nucleotides long that do not encode proteins. While critical roles in normal biology and disease have been revealed for a subset of lncRNAs, the function of the vast majority remains untested. Here, we developed a CRISPR interference (CRISPRi) platform targeting 16,401 lncRNA loci in 7 diverse cell lines including 6 transformed cell lines and human induced pluripotent stem cells (iPSCs).
Read the full summary ↓
Large-scale screening identified 499 lncRNA loci required for robust cellular growth, of which 89% showed growth modifying function exclusively in one cell type. We further found that lncRNA knockdown can perturb complex transcriptional networks in a cell type-specific manner. These data underscore the functional importance and cell type-specificity of many lncRNAs.
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 GSE85011_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 PRJNA335860 and SRA study SRP080367. Searching any of these in the dataset finder brings you back here.

Samples in this study

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