GEO series

rG4-seq reveals widespread formation of G-quadruplex structures in the human transcriptome

GSE77282 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2016/08/10 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 12 samples

What it is: RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism: Homo sapiens (human)
Samples: 12 samples: 4 × polyA-Li vs 4 × polyA-K vs 4 × polyA-KPDSGroups 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)

RNA structures are of crucial importance for biological function and gene regulation. Guanine (G)-rich sequences in RNA can assemble to form RNA G-quadruplex (rG4) structures; specific rG4s have been shown to regulate gene expression, and are associated with human diseases. However, no transcriptome-wide method has been reported to map rG4s, limiting our understanding of the rG4 structure and its relationship with function and regulation.

Read the full summary ↓

Here we introduce a transcriptome-wide rG4 profiling method, rG4-Seq, in which the rG4-mediated reverse transcriptase stalling is read out by next-generation sequencing at nucleotide resolution. We apply this method to human RNA and report the first global map of rG4 structures for any organism. Our analysis identifies over 3,000 rG4s, and increases to over 11,000 upon addition of a rG4 stabilizing ligand Pyridostatin (PDS). Notably, we discover that besides canonical rG4s, many rG4s are non-canonical with novel structural features such as long-loops, bulges, and 2-quartet. We also find that rG4 formation is associated with its stability, Cytosine (C)-content, and the stability of alternative RNA structures. Our data reveals that rG4s can be found in messenger RNAs (mRNAs) and long intergenic noncoding RNAs (lincRNAs). In mRNAs, rG4s are enriched in untranslated regions, and are significantly correlated with microRNA target sites and polyadenylation signals. Remarkably, we found that majority of our in vitro identified rG4s can change the in silico predicted RNA structure to uncover alternative RNA conformation.. Futhermore, the rG4s that have analogues in many ortholog genes across different species show a preferential association with RNA processing and stability. rG4-seq is a broadly applicable method that enables the RNA G4 structurome and its biological roles to be interrogated and can be readily applied in other organisms on a transcriptome-wide scale.

This dataset

Open GSE77282 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 GSE77282_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA309998 and SRA study SRP068969. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 27571552) ↗

Study design

4 × polyA-Li vs 4 × polyA-K vs 4 × polyA-KPDS

Supports a between-group comparison across 12 samples.

How we read this: 3 replicated groups read from 12 sample titles; they account for 12 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

GSM2047243 polyA-Li.rep1 polyA-Li
GSM2047244 polyA-Li.rep2 polyA-Li
GSM2047245 polyA-Li.rep3 polyA-Li
GSM2047246 polyA-Li.rep4 polyA-Li
GSM2047247 polyA-K.rep1 polyA-K
GSM2047248 polyA-K.rep2 polyA-K
GSM2047249 polyA-K.rep3 polyA-K
GSM2047250 polyA-K.rep4 polyA-K
GSM2047251 polyA-KPDS.rep1 polyA-KPDS
GSM2047252 polyA-KPDS.rep2 polyA-KPDS
GSM2047253 polyA-KPDS.rep3 polyA-KPDS
GSM2047254 polyA-KPDS.rep4 polyA-KPDS

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