The RNA exosome complex function in non-coding RNAs processing during erythropoiesis.
RNA-seq (gene expression) · mouse · 76 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
- Mus musculus (mouse)
- Samples
- 76 samples: 24 conditions, mostly in triplicateGroups 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.
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 GSE215077_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 PRJNA888365 and SRA study SRP401616. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 76 samples.
How we read this: 24 replicated groups read from 76 sample titles; they account for 76 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.
- GSM6620869 dis3WTtotal -Rep1 dis3WTtotal
- GSM6620870 dis3WTtotal -Rep2 dis3WTtotal
- GSM6620871 dis3WTtotal -Rep3 dis3WTtotal
- GSM6620872 dis3WTtotal -Rep4 dis3WTtotal
- GSM6620873 dis3KOtotal -Rep1 dis3KOtotal
- GSM6620874 dis3KOtotal -Rep2 dis3KOtotal
- GSM6620875 dis3KOtotal -Rep3 dis3KOtotal
- GSM6620876 dis3KOtotal -Rep4 dis3KOtotal
- GSM6620877 dis3WTS0 population-Rep1 dis3WTS0 population
- GSM6620878 dis3WTS0 population-Rep2 dis3WTS0 population
- GSM6620879 dis3WTS0 population-Rep3 dis3WTS0 population
- GSM6620880 dis3WTS1 population-Rep1 dis3WTS1 population
- GSM6620881 dis3WTS1 population-Rep2 dis3WTS1 population
- GSM6620882 dis3WTS1 population-Rep3 dis3WTS1 population
- GSM6620883 dis3WTS2 population-Rep1 dis3WTS2 population
- GSM6620884 dis3WTS2 population-Rep2 dis3WTS2 population
- GSM6620885 dis3WTS2 population-Rep3 dis3WTS2 population
- GSM6620886 dis3WTS3 population-Rep1 dis3WTS3 population
- GSM6620887 dis3WTS3 population-Rep2 dis3WTS3 population
- GSM6620888 dis3WTS3 population-Rep3 dis3WTS3 population
- GSM6620889 dis3WTS45 population-Rep1 dis3WTS45 population
- GSM6620890 dis3WTS45 population-Rep2 dis3WTS45 population
- GSM6620891 dis3WTS45 population-Rep3 dis3WTS45 population
- GSM6620892 dis3KOS0 population-Rep1 dis3KOS0 population
- GSM6620893 dis3KOS0 population-Rep2 dis3KOS0 population
- GSM6620894 dis3KOS0 population-Rep3 dis3KOS0 population
- GSM6620895 dis3KOS1 population-Rep1 dis3KOS1 population
- GSM6620896 dis3KOS1 population-Rep2 dis3KOS1 population
- GSM6620897 dis3KOS1 population-Rep3 dis3KOS1 population
- GSM6620898 dis3KOS2 population-Rep1 dis3KOS2 population
- GSM6620899 dis3KOS2 population-Rep2 dis3KOS2 population
- GSM6620900 dis3KOS2 population-Rep3 dis3KOS2 population
- GSM6620901 dis3KOS3 population-Rep1 dis3KOS3 population
- GSM6620902 dis3KOS3 population-Rep2 dis3KOS3 population
- GSM6620903 dis3KOS3 population-Rep3 dis3KOS3 population
- GSM6620904 dis3KOS45 population-Rep1 dis3KOS45 population
- GSM6620905 dis3KOS45 population-Rep2 dis3KOS45 population
- GSM6620906 dis3KOS45 population-Rep3 dis3KOS45 population
- GSM6620907 exs10WTtotal -Rep1 exs10WTtotal
- GSM6620908 exs10WTtotal -Rep2 exs10WTtotal
+ 36 more — browse all 76 samples with per-sample file links →
- GSE249460 Transcriptome analysis of long non-coding RNAs in Mycobacterium avium complex-infected macrophages 32 samples shares non-coding, complex, rnas
- GSE312072 Allelic chromatin structure is a pervasive feature of imprinted domains and functions cooperatively with cis-acting long non-coding RNAs at the Mest-Copg2 locus [RNA-seq] 18 samples shares non-coding, rnas
- GSE272793 An atlas of small non-coding RNAs in Human Preimplantation Development (Mouse embryo RNA-seq) 132 samples shares non-coding, rnas
- GSE229416 Alzheimer’s disease-induced phagocytic microglia express a specific profile of coding and non-coding RNAs [BV-2 cells] 32 samples shares non-coding, rnas
- GSE252429 Small non-coding RNAs encapsulating mammalian cells fuels innate immunity 24 samples shares non-coding, rnas
- GSE27218 Disrupted processing of long pre-mRNAs and widespread RNA missplicing are components of neuronal vulnerability from loss of nuclear TDP-43 (RNA-seq) 21 samples shares processing, rnas
- GSE229488 Transcriptomic profiling of long non-coding RNAs identifies circRmst as a regulator of midbrain dopamine neuron migration 21 samples shares non-coding, rnas
- GSE213612 Endothelial derived, secreted long non-coding RNAs Gadlor1 and Gadlor2 aggravate cardiac remodeling 20 samples shares non-coding, rnas
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.