TET2 Mutant Clonal Hematopoiesis Enhances Severity of COVID-19 Related Cytokine Release Syndrome and is Associated with Increased Mortality.
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 363 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Homo sapiens (human)
- Samples
- 363 samples: 7 conditions, mostly with about 18 replicates eachGroups 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
- RNA Shielding of p65 Is Required to Potentiate Oncogenic Inflammation in TET2-Mutated Clonal HematopoiesisCancer discovery · 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 GSE210435_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA865718 and SRA study SRP390601. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 363 samples.
How we read this: 7 replicated groups read from 363 sample titles; they account for 363 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.
- GSM6430109 Methylation - Patient 1 Methylation - Patient
- GSM6430110 Methylation - Patient 5 Methylation - Patient
- GSM6430111 Methylation - Patient 6 Methylation - Patient
- GSM6430112 Methylation - Patient 8 Methylation - Patient
- GSM6430113 Methylation - Patient 12 Methylation - Patient
- GSM6430114 Methylation - Patient 16 Methylation - Patient
- GSM6430115 Methylation - Patient 17 Methylation - Patient
- GSM6430116 Methylation - Patient 26 Methylation - Patient
- GSM6430117 Methylation - Patient 33 Methylation - Patient
- GSM6430118 Methylation - Patient 35 Methylation - Patient
- GSM6430119 Methylation - Patient 36 Methylation - Patient
- GSM6430120 Methylation - Patient 38 Methylation - Patient
- GSM6430121 Methylation - Patient 41 Methylation - Patient
- GSM6430122 Methylation - Patient 42 Methylation - Patient
- GSM6430123 Methylation - Patient 44 Methylation - Patient
- GSM6430124 Methylation - Patient 45 Methylation - Patient
- GSM6430125 Methylation - Patient 46 Methylation - Patient
- GSM6430126 Methylation - Patient 48 Methylation - Patient
- GSM6430143 Multiome - GEX - Patient 1 Multiome - GEX - Patient
- GSM6430144 Multiome - GEX - Patient 4 Multiome - GEX - Patient
- GSM6430145 Multiome - GEX - Patient 5 Multiome - GEX - Patient
- GSM6430146 Multiome - GEX - Patient 7 Multiome - GEX - Patient
- GSM6430147 Multiome - GEX - Patient 8 Multiome - GEX - Patient
- GSM6430148 Multiome - GEX - Patient 12 Multiome - GEX - Patient
- GSM6430149 Multiome - GEX - Patient 15 Multiome - GEX - Patient
- GSM6430150 Multiome - GEX - Patient 16 Multiome - GEX - Patient
- GSM6430151 Multiome - GEX - Patient 18 Multiome - GEX - Patient
- GSM6430152 Multiome - GEX - Patient 21 Multiome - GEX - Patient
- GSM6430153 Multiome - GEX - Patient 24 Multiome - GEX - Patient
- GSM6430154 Multiome - GEX - Patient 30 Multiome - GEX - Patient
- GSM6430155 Multiome - GEX - Patient 35 Multiome - GEX - Patient
- GSM6430156 Multiome - GEX - Patient 36 Multiome - GEX - Patient
- GSM6430157 Multiome - GEX - Patient 38 Multiome - GEX - Patient
- GSM6430158 Multiome - GEX - Patient 41 Multiome - GEX - Patient
- GSM6430159 Multiome - GEX - Patient 42 Multiome - GEX - Patient
- GSM6430160 Multiome - GEX - Patient 43 Multiome - GEX - Patient
- GSM6430161 Multiome - ATAC - Patient 1 Multiome - ATAC - Patie…
- GSM6430162 Multiome - ATAC - Patient 4 Multiome - ATAC - Patie…
- GSM6430163 Multiome - ATAC - Patient 5 Multiome - ATAC - Patie…
- GSM6430164 Multiome - ATAC - Patient 7 Multiome - ATAC - Patie…
+ 323 more — browse all 363 samples with per-sample file links →
- GSE210432 TET2 Mutant Clonal Hematopoiesis Enhances Severity of COVID-19 Related Cytokine Release Syndrome and is Associated with Increased Mortality [scMULT] 36 samples shares covid-19, tet2, hematopoiesis, increased, mortality, enhances, severity, cytokine
- GSE210433 TET2 Mutant Clonal Hematopoiesis Enhances Severity of COVID-19 Related Cytokine Release Syndrome and is Associated with Increased Mortality [scRNA-seq] 34 samples shares covid-19, tet2, hematopoiesis, increased, mortality, enhances, severity, cytokine
- GSE167410 Histone H4 is increased in SARS-CoV-2-infected lung epithelial cells and linked to COVID-19 severity 16 samples shares covid-19, increased, severity
- 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, severity
- GSE264443 Dynamic gene expression analysis reveals distinct severity phases of immune and cellular dysregulation in COVID-19 189 samples shares covid-19, severity
- GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares covid-19, severity
- GSE297529 Discovery of Key Regulators in Classical Monocyte Phenotypes Linked to COVID-19 Severity using Single-cell Multi-omics Sequencing 34 samples shares covid-19, severity
- GSE208342 Transcriptome and microbiome profile of nasopharyngeal samples among COVID-19 patients with different levels of severity revealed through RNA-seq analysis 20 samples shares covid-19, severity
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.