Dynamics and consequences of widespread transcript isoform changes during neuronal differentiation [RNA-Seq]
RNA-seq (gene expression) · human · 24 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
- 24 samples: 5 conditions, mostly with about 5 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
- Uncovering the dynamics and consequences of RNA isoform changes during neuronal differentiationMolecular systems biology · 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 GSE245323_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 PRJNA1027835 and SRA study SRP466255. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 24 samples.
How we read this: 5 replicated groups read from 24 sample titles; they account for 24 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.
- GSM7840858 RNA-seq, NGN3m, day 0, rep 1 RNA-seq, NGN3m, day 0,
- GSM7840859 RNA-seq, NGN3m, day 0, rep 2 RNA-seq, NGN3m, day 0,
- GSM7840860 RNA-seq, NGN3m, day 0, rep 3 RNA-seq, NGN3m, day 0,
- GSM7840861 RNA-seq, NGN3m, day 0, rep 4 RNA-seq, NGN3m, day 0,
- GSM7840862 RNA-seq, NGN3m, day 0, rep 5 RNA-seq, NGN3m, day 0,
- GSM7840863 RNA-seq, NGN3m, day 1, rep 1 RNA-seq, NGN3m, day 1,
- GSM7840864 RNA-seq, NGN3m, day 1, rep 2 RNA-seq, NGN3m, day 1,
- GSM7840865 RNA-seq, NGN3m, day 1, rep 3 RNA-seq, NGN3m, day 1,
- GSM7840866 RNA-seq, NGN3m, day 1, rep 4 RNA-seq, NGN3m, day 1,
- GSM7840867 RNA-seq, NGN3m, day 1, rep 5 RNA-seq, NGN3m, day 1,
- GSM7840868 RNA-seq, NGN3m, day 2, rep 1 RNA-seq, NGN3m, day 2,
- GSM7840869 RNA-seq, NGN3m, day 2, rep 2 RNA-seq, NGN3m, day 2,
- GSM7840870 RNA-seq, NGN3m, day 2, rep 3 RNA-seq, NGN3m, day 2,
- GSM7840871 RNA-seq, NGN3m, day 2, rep 4 RNA-seq, NGN3m, day 2,
- GSM7840872 RNA-seq, NGN3m, day 2, rep 5 RNA-seq, NGN3m, day 2,
- GSM7840873 RNA-seq, NGN3m, day 3, rep 1 RNA-seq, NGN3m, day 3,
- GSM7840874 RNA-seq, NGN3m, day 3, rep 2 RNA-seq, NGN3m, day 3,
- GSM7840875 RNA-seq, NGN3m, day 3, rep 3 RNA-seq, NGN3m, day 3,
- GSM7840876 RNA-seq, NGN3m, day 3, rep 5 RNA-seq, NGN3m, day 3,
- GSM7840877 RNA-seq, NGN3m, day 5, rep 1 RNA-seq, NGN3m, day 5,
- GSM7840878 RNA-seq, NGN3m, day 5, rep 2 RNA-seq, NGN3m, day 5,
- GSM7840879 RNA-seq, NGN3m, day 5, rep 4 RNA-seq, NGN3m, day 5,
- GSM7840880 RNA-seq, NGN3m, day 5, rep 5 RNA-seq, NGN3m, day 5,
- GSM7840881 RNA-seq, NGN3m, day 5, rep 6 RNA-seq, NGN3m, day 5,
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares dynamics, rna-seq
- 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 transcript, changes
- GSE310058 Single-nucleus transcriptomic profiling of human lung tissue in chronic obstructive pulmonary disease (COPD) [snRNA-seq] 149 samples shares transcript, rna-seq
- GSE330029 Temporal changes in metabolism guide oligodendrocyte precursor cell dynamics in aging and multiple sclerosis [BulkRNAseq] 108 samples shares dynamics, changes
- GSE250426 Transcriptomic profiling of kidney transplant recipients [RNA-seq] 74 samples shares transcript, rna-seq
- GSE342032 Isoform-selective antisense oligonucleotide knockdown of SNCA-dIRE and SNCA-IRE transcripts in SK-MEL-28 cells (dose-response) 36 samples shares transcript, isoform
- GSE339456 Integrated bulk and spatial transcriptomic analysis identifies progression-associated molecular signatures in biopsy-proven hypertensive nephropathy [RNA-seq] 35 samples shares transcript, rna-seq
- GSE261713 Linking single cell genomes and transcriptomes at scale to decode breast cancer progression [scRNA-seq] 35 samples shares transcript, rna-seq
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.