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Single-nucleotide resolution mapping of m6A and m6Am throughout the transcriptome

GSE63753 Homo sapiens (human) Single-nucleus RNA-seq 7 samples Submitted 2015/06/29 Platform GPL11154Platform GPL13112
At a glance

Single-nucleus RNA-seq · human · 7 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
7 samples: 5 distinct titles, 1 of them repeatedGroups 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)
N6-methyladenosine (m6A) is the most abundant modified base in eukaryotic mRNA and has been linked to diverse effects on mRNA fate and function. Current m6A mapping approaches rely on immunoprecipitation of m6A-containing RNA fragments to identify regions of transcripts that contain m6A. This approach localizes m6A residues to 100-200 nt-long regions of transcripts.
Read the full summary ↓
The precise position of m6A in mRNAs cannot be identified on a transcriptome-wide level because there are no chemical methods to distinguish between m6A and adenosine. Here we show that anti-m6A antibodies can induce specific mutational signatures at m6A residues after ultraviolet light-induced antibody-RNA crosslinking and reverse transcription. Similarly, we find these antibodies induce mutational signatures at N6, 2’-O-dimethyladenosine (m6Am), a nucleotide found at the first encoded position of certain mRNAs. Using these mutational signatures, we map m6A and m6Am at single-nucleotide resolution in human and mouse mRNA and identify snoRNAs as a novel class of m6A-containing ncRNAs.
This dataset
Open GSE63753 in finder → Find more 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 GSE63753_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 PRJNA268974 and SRA study SRP050393. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 26121403) ↗
Study design
5 distinct titles, 1 of them repeated
HEK293_mRNA_miCLIP ×3

How we read this: Read from 7 sample titles: 5 distinct titles with little repetition. 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
Datasets on a similar topic

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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.