The Transcriptomic Analysis of Profibrinolytic (PLAUR) and Fibrinolytic Inhibitor (SERPINE1, A2M) Genes in COVID-19 Patients and Its Relationship with D-Dimer Levels
RNA-seq (gene expression) · human · 50 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
- 50 samples: 25 × Moderate-Severe, vs 25 × Mild,Groups 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
- Transcriptomic analysis of profibrinolytic and fibrinolytic inhibitor genes in COVID-19 patientsNarra J · 2024
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 GSE259214_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 PRJNA1080455 and SRA study SRP491687. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 50 samples.
How we read this: 2 replicated groups read from 50 sample titles; they account for 50 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.
- GSM8111400 Moderate-Severe,1 Moderate-Severe,
- GSM8111401 Moderate-Severe,2 Moderate-Severe,
- GSM8111402 Moderate-Severe,3 Moderate-Severe,
- GSM8111403 Moderate-Severe,4 Moderate-Severe,
- GSM8111404 Mild,1 Mild,
- GSM8111405 Mild,2 Mild,
- GSM8111406 Mild,3 Mild,
- GSM8111407 Mild,4 Mild,
- GSM8111408 Mild,5 Mild,
- GSM8111409 Mild,6 Mild,
- GSM8111410 Mild,7 Mild,
- GSM8111411 Moderate-Severe,5 Moderate-Severe,
- GSM8111412 Moderate-Severe,6 Moderate-Severe,
- GSM8111413 Moderate-Severe,7 Moderate-Severe,
- GSM8111414 Moderate-Severe,8 Moderate-Severe,
- GSM8111415 Moderate-Severe,9 Moderate-Severe,
- GSM8111416 Moderate-Severe,10 Moderate-Severe,
- GSM8111417 Moderate-Severe,11 Moderate-Severe,
- GSM8111418 Moderate-Severe,12 Moderate-Severe,
- GSM8111419 Moderate-Severe,13 Moderate-Severe,
- GSM8111420 Moderate-Severe,14 Moderate-Severe,
- GSM8111421 Moderate-Severe,15 Moderate-Severe,
- GSM8111422 Moderate-Severe,16 Moderate-Severe,
- GSM8111423 Moderate-Severe,17 Moderate-Severe,
- GSM8111424 Moderate-Severe,18 Moderate-Severe,
- GSM8111425 Mild,8 Mild,
- GSM8111426 Mild,9 Mild,
- GSM8111427 Moderate-Severe, 19 Moderate-Severe,
- GSM8111428 Mild,10 Mild,
- GSM8111429 Mild,11 Mild,
- GSM8111430 Moderate-Severe,20 Moderate-Severe,
- GSM8111431 Moderate-Severe,21 Moderate-Severe,
- GSM8111432 Moderate-Severe,22 Moderate-Severe,
- GSM8111433 Mild,12 Mild,
- GSM8111434 Mild,13 Mild,
- GSM8111435 Mild,14 Mild,
- GSM8111436 Moderate-Severe,23 Moderate-Severe,
- GSM8111437 Moderate-Severe,24 Moderate-Severe,
- GSM8111438 Mild,15 Mild,
- GSM8111439 Mild,16 Mild,
+ 10 more — browse all 50 samples with per-sample file links →
- GSE300696 Identification of reactive CpGs and RNA expression in early COVID-19 through cis-eQTM analysis reflecting disease severity and recovery [RNA-seq] 346 samples shares covid-19
- GSE281864 Detailed delineation of the immune response to COVID-19 mRNA vaccination via high-temporal frequency blood transcriptomics [third] 193 samples shares covid-19
- GSE264443 Dynamic gene expression analysis reveals distinct severity phases of immune and cellular dysregulation in COVID-19 189 samples shares covid-19
- GSE228839 COVID-19 vaccination is associated with attenuation of inflammatory responses during breakthrough disease [Illumina RNA-Seq] 180 samples shares covid-19
- GSE268230 Extracellular vesicles alter trophoblast function in pregnancies complicated by COVID-19 129 samples shares covid-19
- GSE161777 Longitudinal multi-omics identifies responses of megakaryocytes, erythroid cells and plasmablasts as hallmarks of severe COVID-19 trajectories [sequencing] 101 samples shares covid-19
- GSE297329 Evolution of SARS-CoV-2 T cell responses as a function of multiple COVID-19 boosters 98 samples shares covid-19
- GSE336577 Integrated Transcriptomic and Immune Analysis Reveals Distinct Mitochondrial and Immune Signature in COVID-19 ARDS Requiring Invasive Mechanical Ventilation 94 samples shares covid-19
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.