m6A is decoded by modified tRNAs to coordinate mRNA decay.
RNA-seq (gene expression) · mouse · 89 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
- Mus musculus (mouse)
- Samples
- 89 samples: 27 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.
- Paper
- m(6)A alters ribosome dynamics to initiate mRNA degradationCell · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE248279_RAW.tar (it can run to tens of gigabytes).
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: 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 PRJNA1043151 and SRA study SRP318725. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 64 samples.
How we read this: 27 replicated groups read from 89 sample titles; they account for 64 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.
- GSM5283730 RPF_WT_rep1 RPF_WT
- GSM5283731 RPF_WT_rep2 RPF_WT
- GSM5283732 RPF_WT_rep3 RPF_WT
- GSM5283733 RPF_ELP1_KO_rep1 RPF_ELP1_KO
- GSM5283734 RPF_ELP1_KO_rep2 RPF_ELP1_KO
- GSM5283735 RPF_ELP1_KO_rep3 RPF_ELP1_KO
- GSM5283736 RPF_CTU2_KO_rep1 RPF_CTU2_KO
- GSM5283737 RPF_CTU2_KO_rep2 RPF_CTU2_KO
- GSM5283738 RPF_CTU2_KO_rep3 RPF_CTU2_KO
- GSM7898570 CTU2_0h_A
- GSM7898571 CTU2_0h_B
- GSM7898572 CTU2_1h_A
- GSM7898573 CTU2_1h_B
- GSM7898574 CTU2_2h_A
- GSM7898575 CTU2_2h_B
- GSM7898576 ELP1_0h_A
- GSM7898577 ELP1_0h_B
- GSM7898578 ELP1_1h_A
- GSM7898579 ELP1_1h_B
- GSM7898580 ELP1_2h_A
- GSM7898581 ELP1_2h_B
- GSM7898582 WT_0h_A
- GSM7898583 WT_0h_B
- GSM7898584 WT_1h_A
- GSM7898585 WT_1h_B
- GSM7898586 WT_2h_A
- GSM7898587 WT_2h_B
- GSM7906056 HEK293_total_RNA_miCLIP
- GSM7916047 METTL3KO_noEMT_0h_rep1 METTL3KO_noEMT_0h
- GSM7916048 METTL3KO_noEMT_1h_rep1 METTL3KO_noEMT_1h
- GSM7916049 METTL3KO_noEMT_3h_rep1 METTL3KO_noEMT_3h
- GSM7916050 METTL3KO_EMT_0h_rep1 METTL3KO_EMT_0h
- GSM7916051 METTL3KO_EMT_1h_rep1 METTL3KO_EMT_1h
- GSM7916052 METTL3KO_EMT_3h_rep1 METTL3KO_EMT_3h
- GSM7916053 METTL3KO_noEMT_0h_rep2 METTL3KO_noEMT_0h
- GSM7916054 METTL3KO_noEMT_1h_rep2 METTL3KO_noEMT_1h
- GSM7916055 METTL3KO_noEMT_3h_rep2 METTL3KO_noEMT_3h
- GSM7916056 METTL3KO_EMT_0h_rep2 METTL3KO_EMT_0h
- GSM7916057 METTL3KO_EMT_1h_rep2 METTL3KO_EMT_1h
- GSM7916058 METTL3KO_EMT_3h_rep2 METTL3KO_EMT_3h
+ 49 more — browse all 89 samples with per-sample file links →
- GSE98085 YTHDC2 regulates spermatogenesis through promoting the translation of N6-methyladenosine-modified RNA 16 samples shares modified
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.