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Base-resolution mapping reveals distinct classes of N1-methyladenosine methylome in nuclear- and mitochondrial-encoded transcripts

GSE102040 Homo sapiens (human) DNA methylation 24 samples Submitted 2017/10/18 Platform GPL16791
At a glance

DNA methylation · human · 24 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
24 samples: 6 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)
Gene expression can be post-transcriptionally regulated via dynamic and reversible RNA modifications. N1-methyladenosine (m1A) is a recently identified mRNA modification; however, little is known about its precise location, regulation and function. Here, we develop a single-nucleotide resolution m1A profiling method, based on m1A-induced misincorporation during reverse transcription, and report distinct classes of m1A methylome in the human transcriptome.
Read the full summary ↓
m1A in the 5’-untranslated region, particularly those located exactly at the first nucleotide of mRNA transcripts, associates with increased translation efficiency. A different subset of m1A sites exhibit a GUUCRA tRNA-like motif, are evenly distributed in the transcriptome and are dependent on the methyltransferase complex TRMT6/61A. Additionally, we show for the first time that m1A is prevalent in the mitochondrial-encoded transcripts. Manipulation of m1A level via TRMT61B, a mitochondria-localizing m1A methyltransferase, demonstrates that m1A in mitochondrial mRNA interferes with translation. Collectively, our approaches reveal distinct classes of m1A methylome and provide a resource for functional studies of m1A-mediated epitranscriptomic regulation.
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 GSE102040_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 beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA396456 and SRA study SRP114321. Searching any of these in the dataset finder brings you back here.

Study design
6 conditions, mostly in duplicate
RT ×10 m1A_input_TGIRT ×2 m1A_IP_TGIRT ×2 m1A_IP_demethylase_TGIRT ×2 61B-OE ×2 61B-OE-Mock ×2 HEK293T_RNA-seq ×1 HEK293T_Ribo-seq ×1 +2 more

Supports a between-group comparison across 20 samples.

How we read this: 6 replicated groups read from 24 sample titles; they account for 20 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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