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Enzyme-mediated methylation and alkynylation enables transcriptome-wide identification of pseudouridine modifications

GSE236530 Homo sapiens (human) DNA methylation 50 samples Submitted 2026/02/15 Platform GPL24676Platform GPL18573Platform GPL27609Platform GPL34281
At a glance

DNA methylation · human · 50 samples

What it is
DNA methylationChemical marks on DNA that help switch genes on or off.
Organism
Homo sapiens (human)
Samples
50 samples: 21 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)
Pseudouridine (Ψ) is one of the most abundant chemical modifications and plays important roles in RNA function. Advances in our understanding of Ψ have been hindered by a limit of robust methods to precisely and sensitively map their distributions in cellular RNAs. Here, we present ELAP-seq (Enzymatic Labeling and Pull-down for Sequencing) for Ψ detection, which leverages a naturally occurring N1-methyl pseudouridine methyltransferase from Methanocaldococcus jannaschii (Mj1640).
Read the full summary ↓
This enzyme promiscuously converts Ψ to N1-methyl-Ψ (m1Ψ) or installs a propargyl group at the same location in vitro under a mild condition, exhibiting high sensitivity and specificity, and is also functional inside cells. ELAP-seq enriches Ψ-containing RNA fragments and enables single-nucleotide-resolution Ψ detection with markedly enhanced signal-to-noise ratio and reduced sequencing and computational demands. Using ELAP-seq, we identify thousands of candidate Ψ sites in human HeLa and HEK 293T transcriptomes, validating many previously identified sites as well as reporting new ones. This versatile enzymatic platform expands the toolkit for sensitive labeling and detection of Ψ, advancing the study of RNA modification biology.
This dataset
Open GSE236530 in finder → Find more 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 GSE236530_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: Look for beta-value tables or raw IDAT files among the supplementary files.

Also filed as BioProject PRJNA991538 and SRA study SRP447527. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
21 conditions, mostly in duplicate
HeLa-input-III ×3 HeLa-input-IV ×3 HeLa-IP-III ×3 HeLa-IP-IV ×3 HEK-IP-III ×3 HEK-IP-IV ×3 Oligo-input ×2 Oligo-IP ×2 +15 more

Supports a between-group comparison across 48 samples.

How we read this: 21 replicated groups read from 50 sample titles; they account for 48 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

+ 10 more — browse all 50 samples with per-sample file links →

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