CRISPR pooled screen and QuantSeq 3' mRNA sequencing to identify microRNAs (miRNAs) important for Breast Cancer 3D growth [RNA-seq]
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.
- Paper
- CRISPR screens in 3D tumourspheres identified miR-4787-3p as a transcriptional start site miRNA essential for breast tumour-initiating cell growthCommunications biology · 2024
Read the full summary ↓
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 GSE243883_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 PRJNA1020408 and SRA study SRP462827. Searching any of these in the dataset finder brings you back here.
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.
- GSM7797760 HCC1395 treated with anti-miRNA control A
- GSM7797761 HCC1395 treated with anti-miRNA control B
- GSM7797762 HCC1395 treated with anti-miR-483-3p A
- GSM7797763 HCC1395 treated with anti-miR-483-3p B
- GSM7797764 MCF-7 treated with anti-miRNA control A
- GSM7797765 MCF-7 treated with anti-miRNA control B
- GSM7797766 MCF-7 treated with anti-miR-483-3p A
- GSM7797767 MCF-7 treated with anti-miR-483-3p B
- GSM7797768 HCC1395 treated with anti-mir-1468-5p A
- GSM7797769 HCC1395 treated with anti-mir-1468-5p B
- GSM7797770 MCF-7 treated with anti-mir-1468-5p A
- GSM7797771 MCF-7 treated with anti-mir-1468-5p B
- GSM7797772 HCC1395 treated with anti-mir-301-3p A
- GSM7797773 HCC1395 treated with anti-mir-301-3p B
- GSM7797774 HCC1395 treated with anti-mir-4446-3p A
- GSM7797775 HCC1395 treated with anti-mir-4446-3p B
- GSM7797776 MCF-7 treated with anti-mir-301-3p A
- GSM7797777 MCF-7 treated with anti-mir-301-3p B
- GSM7797778 HCC1395 treated with anti-mir-4787-3p A
- GSM7797779 HCC1395 treated with anti-mir-4787-3p B
- GSM7797780 MCF-7 treated with anti-mir-4787-3p A
- GSM7797781 MCF-7 treated with anti-mir-4787-3p B
- GSM7797782 MCF-7 treated with anti-mir-6501-3p A
- GSM7797783 MCF-7 treated with anti-mir-6501-3p B
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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.