The evolution of the type I IFN transcriptional response across human populations
RNA-seq (gene expression) · human · 46 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
- 46 samples: 20 conditions, mostly in duplicateGroups 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
- An intronic LINE-1 regulates IFNAR1 expression in human immune cellsMobile DNA · 2023
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 GSE217313_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 PRJNA898370 and SRA study SRP406336. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 46 samples.
How we read this: 20 replicated groups read from 46 sample titles; they account for 46 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.
- GSM6710597 PRO-seq, Dave, BSA, 1 hour, rep 1 PRO-seq, Dave, BSA, 1 h…
- GSM6710598 PRO-seq, Dave, BSA, 1 hour, rep 2 PRO-seq, Dave, BSA, 1 h…
- GSM6710599 PRO-seq, Dave, BSA, 1 hour, rep 3 PRO-seq, Dave, BSA, 1 h…
- GSM6710600 PRO-seq, Eric, BSA, 1 hour, rep 1 PRO-seq, Eric, BSA, 1 h…
- GSM6710601 PRO-seq, Eric, BSA, 1 hour, rep 2 PRO-seq, Eric, BSA, 1 h…
- GSM6710602 PRO-seq, Eric, BSA, 1 hour, rep 3 PRO-seq, Eric, BSA, 1 h…
- GSM6710603 PRO-seq, Ethan, BSA, 1 hour, rep 1 PRO-seq, Ethan, BSA, 1 …
- GSM6710604 PRO-seq, Ethan, BSA, 1 hour, rep 2 PRO-seq, Ethan, BSA, 1 …
- GSM6710605 PRO-seq, Ethan, BSA, 1 hour, rep 3 PRO-seq, Ethan, BSA, 1 …
- GSM6710606 PRO-seq, GM12878, BSA, 1 hour, rep 1 PRO-seq, GM12878, BSA, …
- GSM6710607 PRO-seq, GM12878, BSA, 1 hour, rep 2 PRO-seq, GM12878, BSA, …
- GSM6710608 PRO-seq, GM18489, BSA, 1 hour, rep 1 PRO-seq, GM18489, BSA, …
- GSM6710609 PRO-seq, GM18489, BSA, 1 hour, rep 2 PRO-seq, GM18489, BSA, …
- GSM6710610 PRO-seq, GM18530, BSA, 1 hour, rep 1 PRO-seq, GM18530, BSA, …
- GSM6710611 PRO-seq, GM18530, BSA, 1 hour, rep 2 PRO-seq, GM18530, BSA, …
- GSM6710612 PRO-seq, GM19024, BSA, 1 hour, rep 1 PRO-seq, GM19024, BSA, …
- GSM6710613 PRO-seq, GM19024, BSA, 1 hour, rep 2 PRO-seq, GM19024, BSA, …
- GSM6710614 PRO-seq, HG02150, BSA, 1 hour, rep 1 PRO-seq, HG02150, BSA, …
- GSM6710615 PRO-seq, HG02150, BSA, 1 hour, rep 2 PRO-seq, HG02150, BSA, …
- GSM6710616 PRO-seq, HG03077, BSA, 1 hour, rep 1 PRO-seq, HG03077, BSA, …
- GSM6710617 PRO-seq, HG03077, BSA, 1 hour, rep 2 PRO-seq, HG03077, BSA, …
- GSM6710618 PRO-seq, HG03645, BSA, 1 hour, rep 1 PRO-seq, HG03645, BSA, …
- GSM6710619 PRO-seq, HG03645, BSA, 1 hour, rep 2 PRO-seq, HG03645, BSA, …
- GSM6710620 PRO-seq, Dave, IFNB, 1 hour, rep 1 PRO-seq, Dave, IFNB, 1 …
- GSM6710621 PRO-seq, Dave, IFNB, 1 hour, rep 2 PRO-seq, Dave, IFNB, 1 …
- GSM6710622 PRO-seq, Dave, IFNB, 1 hour, rep 3 PRO-seq, Dave, IFNB, 1 …
- GSM6710623 PRO-seq, Eric, IFNB, 1 hour, rep 1 PRO-seq, Eric, IFNB, 1 …
- GSM6710624 PRO-seq, Eric, IFNB, 1 hour, rep 2 PRO-seq, Eric, IFNB, 1 …
- GSM6710625 PRO-seq, Eric, IFNB, 1 hour, rep 3 PRO-seq, Eric, IFNB, 1 …
- GSM6710626 PRO-seq, Ethan, IFNB, 1 hour, rep 1 PRO-seq, Ethan, IFNB, 1…
- GSM6710627 PRO-seq, Ethan, IFNB, 1 hour, rep 2 PRO-seq, Ethan, IFNB, 1…
- GSM6710628 PRO-seq, Ethan, IFNB, 1 hour, rep 3 PRO-seq, Ethan, IFNB, 1…
- GSM6710629 PRO-seq, GM12878, IFNB, 1 hour, rep 1 PRO-seq, GM12878, IFNB,…
- GSM6710630 PRO-seq, GM12878, IFNB, 1 hour, rep 2 PRO-seq, GM12878, IFNB,…
- GSM6710631 PRO-seq, GM18489, IFNB, 1 hour, rep 1 PRO-seq, GM18489, IFNB,…
- GSM6710632 PRO-seq, GM18489, IFNB, 1 hour, rep 2 PRO-seq, GM18489, IFNB,…
- GSM6710633 PRO-seq, GM18530, IFNB, 1 hour, rep 1 PRO-seq, GM18530, IFNB,…
- GSM6710634 PRO-seq, GM18530, IFNB, 1 hour, rep 2 PRO-seq, GM18530, IFNB,…
- GSM6710635 PRO-seq, GM19024, IFNB, 1 hour, rep 1 PRO-seq, GM19024, IFNB,…
- GSM6710636 PRO-seq, GM19024, IFNB, 1 hour, rep 2 PRO-seq, GM19024, IFNB,…
+ 6 more — browse all 46 samples with per-sample file links →
- GSE306664 Revealing differential type I and type II IFN responses in different immune cells in various human diseases by a new IFNs transcriptomic atlas 131 samples shares ifn, type
- GSE305618 Repeats mimic pathogen-associated patterns across a vast evolutionary landscape [RIP-Seq] 16 samples shares evolution, across
- GSE74193 Mapping DNA methylation across development, genotype, and schizophrenia in the human frontal cortex 675 samples shares across, type
- GSE327707 Sorted bulk RNA-seq profiling of human peripheral blood mononuclear cell populations across diverse in vitro immune stimuli 150 samples shares populations, across
- GSE268210 Single-cell analysis of human PBMCs in healthy and type 2 diabetes populations: Dysregulated immune networks in type 2 diabetes unveiled through single-cell profiling 30 samples shares populations, type
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares evolution, type
- GSE319448 DNA methylation stochasticity is linked to transcriptional variability and identifies convergent epigenetic disruption across genetically-defined subtypes of AML [CellCulture_WGBS] 12 samples shares across, type
- GSE290695 Identification of Crohn’s disease subtypes in single cell RNA sequencing signatures of treatment naïve samples across the gastrointestinal tract 95 samples shares across, type
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.