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Widespread N6-methyladenosine-dependent RNA Structural Switches Regulate RNA-Protein Interactions

GSE56010 Homo sapiens (human) Non-coding RNA profiling 18 samples Submitted 2015/02/27 Platform GPL11154
At a glance

Non-coding RNA profiling · human · 18 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Homo sapiens (human)
Samples
18 samples: 9 conditions, mostly in duplicateGroups 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)
We show that N6-methyladenosine (m6A), the most abundant internal modification in mRNA/lncRNA with still poorly characterized function, alters RNA structure to facilitate the access of RBM for heterogeneous nuclear ribonucleoprotein C (hnRNP C). We term this mechanism m6A-switch. Through combining PAR-CLIP with Me-RIP, we identify 39,060 m6A-switches among hnRNP C binding sites transcriptome-wide.
Read the full summary ↓
We show that m6A-methyltransferases METTL3 or METTL14 knockdown decreases hnRNP C binding at 16,582 m6A-switches. Taken together, 2,798 m6A-switches of high confidence are identified to mediate RNA-hnRNP C interactions and affect diverse biological processes including cell cycle regulation. These findings reveal the biological importance of m6A and provide insights into the sophisticated regulation of RNA-RBP interactions through m6A-induced RNA structural remodeling.
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 GSE56010_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.

Also filed as BioProject PRJNA242201 and SRA study SRP040278. Searching any of these in the dataset finder brings you back here.

Study design
9 conditions, mostly in duplicate
PARCLIP-MeRIP-Input ×2 PARCLIP-MeRIP-IP ×2 hnRNPC-PAR-CLIP-Control ×2 hnRNPC-PAR-CLIP-METTL3KD ×2 hnRNPC-PAR-CLIP-METTL14KD ×2 RNA-seq-control ×2 RNA-seq-HNRNPCKD ×2 RNA-seq-METTL3KD ×2 +1 more

Supports a between-group comparison across 18 samples.

How we read this: 9 replicated groups read from 18 sample titles; they account for 18 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
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