Transcriptome analysis of cells after SARS-CoV-2 infection and treatment with MEK1/2-inhibitor Zapnometinib.
RNA-seq (gene expression) · human · 36 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
- 36 samples
- 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.
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 GSE303020_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 PRJNA1293367 and SRA study SRP601925. Searching any of these in the dataset finder brings you back here.
The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Sample titles run ROD_37 … ROD_72 and carry no group information.
- GSM9115951 ROD_37
- GSM9115952 ROD_38
- GSM9115953 ROD_39
- GSM9115954 ROD_40
- GSM9115955 ROD_41
- GSM9115956 ROD_42
- GSM9115957 ROD_43
- GSM9115958 ROD_44
- GSM9115959 ROD_45
- GSM9115960 ROD_46
- GSM9115961 ROD_47
- GSM9115962 ROD_48
- GSM9115963 ROD_49
- GSM9115964 ROD_50
- GSM9115965 ROD_51
- GSM9115966 ROD_52
- GSM9115967 ROD_53
- GSM9115968 ROD_54
- GSM9115969 ROD_55
- GSM9115970 ROD_56
- GSM9115971 ROD_57
- GSM9115972 ROD_58
- GSM9115973 ROD_59
- GSM9115974 ROD_60
- GSM9115975 ROD_61
- GSM9115976 ROD_62
- GSM9115977 ROD_63
- GSM9115978 ROD_64
- GSM9115979 ROD_65
- GSM9115980 ROD_66
- GSM9115981 ROD_67
- GSM9115982 ROD_68
- GSM9115983 ROD_69
- GSM9115984 ROD_70
- GSM9115985 ROD_71
- GSM9115986 ROD_72
- GSE254266 Single-cell analysis of lung epithelial cells reveals age and cell population-specific responses to SARS-CoV-2 infection in ciliated cells 14 samples shares sars-cov-2
- GSE240766 SARS-CoV-2 infection of human pluripotent stem cell-derived vascular cells reveals smooth muscle cells as key mediators of vascular pathology during infection 101 samples shares sars-cov-2
- GSE311993 Symptomatic SARS-CoV-2 breakthrough infections broaden the repertoire of Spike-reactive CD4 T cells 69 samples shares sars-cov-2
- GSE272756 SARS-CoV-2 infection of human lung ALI cultures reveals basal cells as relevant targets 54 samples shares sars-cov-2
- GSE250377 SARS-CoV-2 infection response in iPSC-derived alveolar epithelial cell type-2 40 samples shares sars-cov-2
- GSE281237 Widespread gene-environment interactions shape the immune response to SARS-CoV-2 infection in hospitalized COVID-19 patients 29 samples shares sars-cov-2
- GSE304243 Human B cells induced by primary SARS-CoV-2 infection 27 samples shares sars-cov-2
- GSE239450 SARS-CoV-2 infection elucidates unique features of pregnancy-specific immunity [GEX] 26 samples shares sars-cov-2
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.