Altered TE-derived genes leads to pathogenesis of FXD [ShortReadRNASeq]
RNA-seq (gene expression) · human · 63 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
- 63 samples: 63 samples, each with a distinct title — no replicated groupsGroups 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
- C G composition in transposon-derived genes is increased in FXD with perturbed immune systemNAR molecular 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 GSE264090_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 PRJNA1100965 and SRA study SRP502143. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 63 sample titles: 63 distinct titles with little repetition. 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.
- GSM8210251 TAS1 replicate 1_Male FXD, Ribosomal RNA depletion
- GSM8210252 TAS1 replicate 2_Male FXD, Ribosomal RNA depletion
- GSM8210253 TAS1 replicate 3_Male FXD, Ribosomal RNA depletion
- GSM8210254 TAS2 replicate 1_Fale FXD, Ribosomal RNA depletion
- GSM8210255 TAS2 replicate 2_Fale FXD, Ribosomal RNA depletion
- GSM8210256 TAS2 replicate 3_Fale FXD, Ribosomal RNA depletion
- GSM8210257 TAS10 replicate 1_Male Healthy, Ribosomal RNA depletion
- GSM8210258 TAS10 replicate 2_Male Healthy, Ribosomal RNA depletion
- GSM8210259 TAS10 replicate 3_Male Healthy, Ribosomal RNA depletion
- GSM8210260 TAS11 replicate 1_Female Healthy, Ribosomal RNA depletion
- GSM8210261 TAS11 replicate 2_Female Healthy, Ribosomal RNA depletion
- GSM8210262 TAS11 replicate 3_Female Healthy, Ribosomal RNA depletion
- GSM8210263 TAS18 replicate 1_Male FXD, Ribosomal RNA depletion
- GSM8210264 TAS1 8 replicate 2_Male FXD, Ribosomal RNA depletion
- GSM8210265 TAS18 replicate 3_Male FXD, Ribosomal RNA depletion
- GSM8210266 TAS19 replicate 1_Fale FXD, Ribosomal RNA depletion
- GSM8210267 TAS19 replicate 2_Fale FXD, Ribosomal RNA depletion
- GSM8210268 TAS19 replicate 3_Fale FXD, Ribosomal RNA depletion
- GSM8210269 TAS20 replicate 1_Male FXD, Ribosomal RNA depletion
- GSM8210270 TAS20 replicate 2_Male FXD, Ribosomal RNA depletion
- GSM8210271 TAS20 replicate 3_Male FXD, Ribosomal RNA depletion
- GSM8210272 TAS21 replicate 1_Fale FXD, Ribosomal RNA depletion
- GSM8210273 TAS21 replicate 2_Fale FXD, Ribosomal RNA depletion
- GSM8210274 TAS21 replicate 3_Fale FXD, Ribosomal RNA depletion
- GSM8210275 TAS22 replicate 1_Female Healthy, Ribosomal RNA depletion
- GSM8210276 TAS22 replicate 2_Female Healthy, Ribosomal RNA depletion
- GSM8210277 TAS22 replicate 3_Female Healthy, Ribosomal RNA depletion
- GSM8210278 TAS23 replicate 1_Male Healthy, Ribosomal RNA depletion
- GSM8210279 TAS23 replicate 2_Male Healthy, Ribosomal RNA depletion
- GSM8210280 TAS23 replicate 3_Male Healthy, Ribosomal RNA depletion
- GSM8210281 TAS29 replicate 1_Fale FXD, Ribosomal RNA depletion
- GSM8210282 TAS29 replicate 2_Fale FXD, Ribosomal RNA depletion
- GSM8210283 TAS29 replicate 3_Fale FXD, Ribosomal RNA depletion
- GSM8210284 TAS30 replicate 1_Female Healthy, Ribosomal RNA depletion
- GSM8210285 TAS30 replicate 2_Female Healthy, Ribosomal RNA depletion
- GSM8210286 TAS30 replicate 3_Female Healthy, Ribosomal RNA depletion
- GSM8210287 TAS36 replicate 1_Male FXD, Ribosomal RNA depletion
- GSM8210288 TAS36 replicate 2_Male FXD, Ribosomal RNA depletion
- GSM8210289 TAS36 replicate 3_Male FXD, Ribosomal RNA depletion
- GSM8210290 TAS39 replicate 1_Male FXD, Ribosomal RNA depletion
+ 23 more — browse all 63 samples with per-sample file links →
- GSE264091 Altered TE-derived genes leads to pathogenesis of FXD 147 samples shares fxd, pathogenesis, te-derived, altered, leads
- GSE264089 Altered TE-derived genes leads to pathogenesis of FXD [PacBioRNASeq] 84 samples shares fxd, pathogenesis, te-derived, altered, leads
- GSE283315 Investigation of the Altered Transcriptomic Dynamics of Bone Marrow Adipose Tissue Under Exogenous Stimuli in the Context of AML Pathogenesis 36 samples shares pathogenesis, altered
- GSE221740 Plasma multi-omics identify early pathogenesis and biomarkers of SCD post-AMI 106 samples shares pathogenesis
- GSE261775 A systems-biology approach connects aging mechanisms with Alzheimer’s disease pathogenesis [TCPY] 94 samples shares pathogenesis
- GSE292317 CYP1B1-AS1 regulates CYP1B1 to promote Coxiella burnetii pathogenesis by inhibiting ROS and host cell death 78 samples shares pathogenesis
- GSE275765 Neuronal Activity-Dependent Gene Dysregulation inC9orf72i3Neuronal Models of ALS/FTD Pathogenesis 78 samples shares pathogenesis
- GSE272035 Gene community-regulated pathway analysis successfully deciphers pathogenesis and biomarkers of viral versus metabolic hepatocellular carcinoma 71 samples shares pathogenesis
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.