Effect of NAT10 knockdown on gene expression in human valve interstitial cells
RNA-seq (gene expression) · human · 8 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
- 8 samples: 4 × RNA seq of shNAT10 vs 4 × RNA seq of shNCGroups 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.
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 GSE335315_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 PRJNA1477900 and SRA study SRP709160. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 8 samples.
How we read this: 2 replicated groups read from 8 sample titles; they account for 8 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.
- GSM9810965 RNA seq of shNAT10-1 RNA seq of shNAT10
- GSM9810966 RNA seq of shNAT10-2 RNA seq of shNAT10
- GSM9810967 RNA seq of shNAT10-3 RNA seq of shNAT10
- GSM9810968 RNA seq of shNAT10-4 RNA seq of shNAT10
- GSM9810969 RNA seq of shNC-1 RNA seq of shNC
- GSM9810970 RNA seq of shNC-2 RNA seq of shNC
- GSM9810971 RNA seq of shNC-3 RNA seq of shNC
- GSM9810972 RNA seq of shNC-4 RNA seq of shNC
- GSE185960 RNA-seq and acRIP-seq in NAT10-knockdown PDAC cells 18 samples shares nat10, knockdown
- GSE248476 Transcriptome-wide profiles and ac4c modification profile in RA FLS with NAT10 knockdown 18 samples shares nat10, knockdown
- GSE284183 Elevation of caldesmon-1 is essential for the differentiation of aortic valve interstitial cells into osteoblasts. 9 samples shares interstitial, valve
- GSE295765 Circular ZBTB44-encoded ZBTB44-342aa alleviates osteogenic differentiation of human aortic valve interstitial cells by inhibiting the cGAS-STING pathway 11 samples shares interstitial, valve
- GSE282141 Effect of SLC25A1 knockdown on gene expression during differentiation of human airway basal cells 30 samples shares knockdown
- GSE278671 Effect of ATP7A knockdown on gene expression in glioma stem cells 12 samples shares knockdown
- GSE203625 Effect of knockdown of CENPU on gene expression of Huh-7 and MHCC-97H hepatoma cells 12 samples shares knockdown
- GSE280172 Effect of NUDT21 knockdown on gene expression in MUTZ-1 and SKM-1 myelodysplastic syndrome cells 12 samples shares knockdown
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.