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RNAseq analysis for transcriptomes of ATO treated U937 cell lines expressing 10 type 1 p53 mutants, type-3 p53-R273H, p53 null, or p53 wild-type

GSE182565 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/08/01 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 24 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
Homo sapiens (human)
Samples
24 samples: 24 samples, each with a distinct title — no replicated groupsGroups 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)
To globally evaluate to what extend type-1 p53 mutant transcription activity can be restored by arsenic trioxide (ATO) (compared to wild-type p53), p53-null U937 cells introduced with 10 frequent type-1 p53, type-3 p53-R273H (negative control), empty vector or wild-type p53 were treated with or without 1 μg/mL ATO. mRNA was isolated and then subject to deep sequencing, using Illumina HiSeq.
Read the full summary ↓
The sequence reads that passed quality filters were analyzed using Cutadapt. Results and conclusions: The type-1 p53 mutants are the major cellular targets of ATO in the current cell contexts. The expression profiles of well-established p53 targets in cells expressing wild-type p53 highly correlated with the ones in cells expressing ATO-rescued type-1 mutants, but not those in cells expressing ATO-treated R273H. In cells harboring type-1 mutants, the median expression levels of these targets were elevated by ATO to extents comparable to wild-type p53.
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 GSE182565_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: 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 PRJNA756908 and SRA study SRP333674. Searching any of these in the dataset finder brings you back here.

Study design
24 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 24 sample titles: 24 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
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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.