GEO series

Landscape and variation of RNA secondary structure across the human transcriptome

GSE50676 Homo sapiens (human) Single-nucleus RNA-seq 18 samples Submitted 2013/12/19 Platform GPL16791Platform GPL11154

At a glance

Single-nucleus RNA-seq · human · 18 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 18 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)

In parallel to the genetic code for protein synthesis, a second layer of information is embedded in all RNA transcripts in the form of RNA structure. The ability of RNA to base pair with itself and other nucleic acids endow RNA with the capacity to form extensive structures, which are known to influence practically every step in the gene expression program1.

Read the full summary ↓

Yet the nature of most RNA structures or effects of sequence variation on structure are not known. Here we report the initial landscape and variation of RNA secondary structures (RSS) in a human family trio, providing a comprehensive RSS map of human coding and noncoding RNAs. We identify unique RSS signatures that demarcate open reading frames, splicing junctions, and define authentic microRNA binding sites. Comparison of native deproteinized RNA isolated from cells versus refolded purified RNA suggests that the majority of the RSS information is encoded within RNA sequence. Over one thousand transcribed single nucleotide variants (~15% of all transcribed SNVs) alter local RNA structure; these “RiboSNitches”2 occur in disease-associated variants. We discover simple sequence and spacing rules that determine the ability of point mutations to impact RSS. Selective depletion of RiboSNitches versus structurally synonymous variants at precise locations suggests selection for specific RNA shapes at thousands of sites, including 3’UTRs, binding sites of miRNAs and RNA binding proteins genome-wide. These results highlight the potentially broad contribution of RNA structure and its variation to gene regulation.

This dataset

Open GSE50676 in finder → Find more human RNA-seq 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 GSE50676_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA218468 and SRA study SRP029656. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 24476892) ↗

Samples in this study

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+ 18 more — browse all 18 samples with per-sample file links →

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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.