Evidence map›Paper›PMID 23269463›Full record

ReviewNature reviews. Genetics2013

Reuse of public genome-wide gene expression data.

Johan Rung, Alvis Brazma

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Genetics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 193 papers, 16 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
193citing papers in PubMed, 16 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

193 citing papers in PubMed, 16 syntheses or guidelines pooled it.

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  5. Distinct gene expression characteristics in epithelial cell-International journal of medical sciences · 2019
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133 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Johan RungEMBL-EBI, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
Alvis Brazma

Funding

Biotechnology and Biological Sciences Research Council BB/E001653/1
6 · The paper itself

Abstract

Our understanding of gene expression has changed dramatically over the past decade, largely catalysed by technological developments. High-throughput experiments - microarrays and next-generation sequencing - have generated large amounts of genome-wide gene expression data that are collected in public archives. Added-value databases process, analyse and annotate these data further to make them accessible to every biologist. In this Review, we discuss the utility of the gene expression data that are in the public domain and how researchers are making use of these data. Reuse of public data can be very powerful, but there are many obstacles in data preparation and analysis and in the interpretation of the results. We will discuss these challenges and provide recommendations that we believe can improve the utility of such data.

Indexed as

Databases, GeneticPublic SectorAnimalsComputational BiologyGene Expression ProfilingHigh-Throughput Nucleotide SequencingHigh-Throughput Screening AssaysHumansOligonucleotide Array Sequence Analysis

Identifiers

PMID23269463

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.