The MUC5B IPF risk variant promotes a distal airway secretory phenotype and loss of alveolar markers
RNA-seq (gene expression) · human · 310 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
- 310 samples: 9 conditions, mostly with about 25 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
- MUC5B Idiopathic Pulmonary Fibrosis Risk Variant Promotes a Mucosecretory Phenotype and Loss of Small Airway Secretory CellsAmerican journal of respiratory and critical care medicine · 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 GSE267521_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 PRJNA1111763. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 310 samples.
How we read this: 9 replicated groups read from 310 sample titles; they account for 310 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.
- GSM8267828 Block_3-2_001 Block_3-2
- GSM8267829 Block_3-2_002 Block_3-2
- GSM8267830 Block_3-2_003 Block_3-2
- GSM8267831 Block_3-2_004 Block_3-2
- GSM8267832 Block_3-2_005 Block_3-2
- GSM8267833 Block_3-2_006 Block_3-2
- GSM8267834 Block_3-2_009 Block_3-2
- GSM8267835 Block_3-2_010 Block_3-2
- GSM8267836 Block_3-2_011 Block_3-2
- GSM8267837 Block_3-2_012 Block_3-2
- GSM8267838 Block_3-2_013 Block_3-2
- GSM8267839 Block_3-2_014 Block_3-2
- GSM8267840 Block_4-2_001 Block_4-2
- GSM8267841 Block_4-2_002 Block_4-2
- GSM8267842 Block_4-2_003 Block_4-2
- GSM8267843 Block_4-2_004 Block_4-2
- GSM8267844 Block_4-2_005 Block_4-2
- GSM8267845 Block_4-2_006 Block_4-2
- GSM8267846 Block_4-2_007 Block_4-2
- GSM8267847 Block_4-2_008 Block_4-2
- GSM8267848 Block_4-2_009 Block_4-2
- GSM8267849 Block_4-2_010 Block_4-2
- GSM8267850 Block_4-2_011 Block_4-2
- GSM8267851 Block_4-2_012 Block_4-2
- GSM8267852 Block_4-2_013 Block_4-2
- GSM8267853 Block_4-2_014 Block_4-2
- GSM8267854 Block_4-2_015 Block_4-2
- GSM8267855 Block_4-2_016 Block_4-2
- GSM8267856 Block_4-2_017 Block_4-2
- GSM8267857 Block_4-2_018 Block_4-2
- GSM8267858 Block_4-2_019 Block_4-2
- GSM8267859 Block_4-2_020 Block_4-2
- GSM8267860 Block_4-2_021 Block_4-2
- GSM8267861 Block_4-2_040 Block_4-2
- GSM8267862 Block_4-2_041 Block_4-2
- GSM8267863 Block_4-2_043 Block_4-2
- GSM8267864 Block_4-2_044 Block_4-2
- GSM8267865 Block_4-2_045 Block_4-2
- GSM8267866 Block_4-2_046 Block_4-2
- GSM8267867 Block_4-2_047 Block_4-2
+ 270 more — browse all 310 samples with per-sample file links →
- GSE311877 Genotype-phenotype single-cell transcriptomics for massive parallel assessment of genetic variants [RNAseq_mutants] 80 samples shares phenotype, variant
- GSE295260 CDK4/6 inhibition induces a senescence-associated secretory phenotype via delayed NF-κB activation 40 samples shares secretory, phenotype
- GSE276620 Oncogene induced senescence and senescence-associated secretory phenotype (SASP) determines the efficacy of palbociclib in PIK3CA mutated colorectal cancer 26 samples shares secretory, phenotype
- GSE199345 Siglec-1−/FSClow alveolar macrophages exhibit pro-inflammatory phenotypes in chronic obstructive pulmonary disease 24 samples shares phenotype, alveolar
- GSE301142 Multiple mechanisms lead to differential loss-of-function effects of pathogenic SARS2 variants: implications for genotype-phenotype analyses 18 samples shares phenotype, variant
- GSE282384 Substrate Stiffness Dictates Unique Doxorubicin-induced Senescence-associated Secretory Phenotypes and Transcriptomic Signatures in Human Pulmonary Fibroblasts 12 samples shares secretory, phenotype
- GSE299101 DICER1 dysregulation triggers miRNA reprogramming, promotes mesenchymal cell fates and slows TNBC tumorigenic phenotypes 12 samples shares phenotype, promotes
- GSE259222 Deleterious, protein-altering variants in the transcriptional coregulator ZMYM3 in 27 individuals with a neurodevelopmental delay phenotype [RNA-seq] 12 samples shares phenotype, variant
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.