GEO series

RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [RNA-Seq]

GSE49711 Homo sapiens (human) RNA-seq (gene expression) 498 samples Submitted 2015/05/22 Platform GPL17553

At a glance

RNA-seq (gene expression) · human · 498 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: 498 samples
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.

Summary (written by the submitters)

We generated gene expression profiles from 498 primary neuroblastomas using RNA-Seq and microarrays. We sought to systematically evaluate the capability of RNA deep-sequencing (RNA-Seq)-based classification for clinical endpoint prediction in comparison to microarray-based ones. The neuroblastoma cohort was randomly divided into training and validation sets, and 360 predictive models on six clinical endpoints were generated and evaluated.

Read the full summary ↓

While prediction performances did not differ considerably between the two technical platforms, the RNA-Seq data processing pipelines, or feature levels (i.e., gene, transcript, and exon junction levels), RNA-Seq models based on the AceView database performed best on most endpoints. Collectively, our study reveals an unprecedented complexity of the neuroblastoma transcriptome, and provides guidelines for the development of gene expression-based predictive classifiers using high-throughput technologies. Sample clinical characteristics definitions: dataset: Expression data set used for classification training (1) or validation (2) Sex: M = male; F= female age at diagnosis: the age in days at diagnosis mycn status: Amplification status of the MYCN proto-oncogene (amplified = 1, no amplification = 0; no information = N/A) high risk: Clinically considered as high-risk neuroblastoma (yes=1, no= 0) INSS stage: disease stage according to International Neuroblastoma Staging System (INSS) (1, 2, 3, 4 and 4S) class label: Maximally divergent disease courses - unfavorable (= 1): patient died despite intensive chemotherapy, favorable (=0): patient survived without chemotharapy for at least 1000 days post diagnosis; not applicable (N/A) progression: Occurrence of a tumor progression event (yes=1; no=0) death from disease: Occurrence of death from the disease (yes=1; no=0)

This dataset

Open GSE49711 in finder → Find more human RNA-seq datasets →

Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE49711_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA214797. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 25150839) ↗

Study design

A cohort of 498 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run SEQC_NB001 … SEQC_NB400 and carry no group information.

Samples in this study

GSM1205736 SEQC_NB001
GSM1205737 SEQC_NB002
GSM1205738 SEQC_NB003
GSM1205739 SEQC_NB004
GSM1205740 SEQC_NB005
GSM1205741 SEQC_NB006
GSM1205742 SEQC_NB007
GSM1205743 SEQC_NB008
GSM1205744 SEQC_NB009
GSM1205745 SEQC_NB010
GSM1205746 SEQC_NB011
GSM1205747 SEQC_NB012
GSM1205748 SEQC_NB013
GSM1205749 SEQC_NB014
GSM1205750 SEQC_NB015
GSM1205751 SEQC_NB016
GSM1205752 SEQC_NB017
GSM1205753 SEQC_NB018
GSM1205754 SEQC_NB019
GSM1205755 SEQC_NB020
GSM1205756 SEQC_NB021
GSM1205757 SEQC_NB022
GSM1205758 SEQC_NB023
GSM1205759 SEQC_NB024
GSM1205760 SEQC_NB025
GSM1205761 SEQC_NB026
GSM1205762 SEQC_NB027
GSM1205763 SEQC_NB028
GSM1205764 SEQC_NB029
GSM1205765 SEQC_NB030
GSM1205766 SEQC_NB031
GSM1205767 SEQC_NB032
GSM1205768 SEQC_NB033
GSM1205769 SEQC_NB034
GSM1205770 SEQC_NB035
GSM1205771 SEQC_NB036
GSM1205772 SEQC_NB037
GSM1205773 SEQC_NB038
GSM1205774 SEQC_NB039
GSM1205775 SEQC_NB040

+ 458 more — browse all 498 samples with per-sample file links →

Datasets on a similar topic

GSE49710 RNA-Seq reveals an unprecedented complexity of the neuroblastoma transcriptome and is suitable for clinical endpoint prediction [microarray] 498 samples shares unprecedented, neuroblastoma, complexity, prediction, suitable, clinical, endpoint, rna-seq
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GSE242685 Blood transcriptional signatures of asymptomatic FDG-PET/CT-defined Subclinical TB lesions and their prediction of future TB progression II 98 samples shares prediction, clinical
GSE287496 CDK12 as an entry point for combination therapy in high-risk neuroblastoma [RNA-seq] 59 samples shares neuroblastoma, rna-seq
GSE211354 The SAGA complex maintains the oncogenic gene expression program in MYCN-amplified neuroblastoma [RNA-Seq] 54 samples shares neuroblastoma, rna-seq
GSE240581 Small-molecule Inhibition of METTL3 Suppresses Tumor Growth and Promotes Neuroblastoma Differentiation [RNA-Seq Decay] 48 samples shares neuroblastoma, rna-seq
GSE222212 TCF4 is a key mediator of cell identity in neuroblastoma and vulnerable to protein degradation by multiple epigenetic factors [RNA-seq] 42 samples shares neuroblastoma, rna-seq
GSE273848 Definition of a multi-omics signature for Esophageal Adenocarcinoma prognosis prediction [totalRNA-seq] 40 samples shares prediction, rna-seq

Search all human RNA-seq datasets in GEO →

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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.