Evidence map›Paper›PMID 24823911›Full record

ArticlePloS one2014

A range finding protocol to support design for transcriptomics experimentation: examples of in-vitro and in-vivo murine UV exposure.

Oskar Bruning, Wendy Rodenburg, Conny T van Oostrom, Martijs J Jonker, Mark de Jong, Rob J Dekker, Han Rauwerda, Wim A Ensink, Annemieke de Vries, Timo M Breit

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.4field-weighted citation impact, top 35% of its field
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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
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

10 authors at 3 institutions in 1 country.

Oskar BruningRNA Biology & Applied Bioinformatics research group and MAD: Dutch Genomics Service & Support Provider, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands; Netherlands Bioinformatics Centre (NBIC), Nijmegen, the Netherlands.
Wendy RodenburgCentre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Conny T van OostromCentre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Martijs J JonkerRNA Biology & Applied Bioinformatics research group and MAD: Dutch Genomics Service & Support Provider, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands; Netherlands Bioinformatics Centre (NBIC), Nijmegen, the Netherlands.
Mark de JongRNA Biology & Applied Bioinformatics research group and MAD: Dutch Genomics Service & Support Provider, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands; Netherlands Bioinformatics Centre (NBIC), Nijmegen, the Netherlands.
Rob J DekkerRNA Biology & Applied Bioinformatics research group and MAD: Dutch Genomics Service & Support Provider, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands; Netherlands Bioinformatics Centre (NBIC), Nijmegen, the Netherlands.
Han RauwerdaRNA Biology & Applied Bioinformatics research group and MAD: Dutch Genomics Service & Support Provider, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands; Netherlands Bioinformatics Centre (NBIC), Nijmegen, the Netherlands.
Wim A EnsinkRNA Biology & Applied Bioinformatics research group and MAD: Dutch Genomics Service & Support Provider, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands.
Annemieke de VriesCentre for Health Protection (GZB), National Institute of Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
Timo M BreitRNA Biology & Applied Bioinformatics research group and MAD: Dutch Genomics Service & Support Provider, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, the Netherlands; Netherlands Bioinformatics Centre (NBIC), Nijmegen, the Netherlands.
University of Amsterdam · NLNational Institute for Public Health and the Environment · NLNetherlands Bioinformatics Centre · NL

Funding

Humanized Mouse Models for the p53 R72P SNPR01ES015587 · NIEHS · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JOHNSON, DAVID G. · 2009 to 2013
$2.2M
NIEHS NIH HHS 1R01 ES015587-01A2NIEHS NIH HHS R01 ES015587
6 · The paper itself

Abstract

In transcriptomics research, design for experimentation by carefully considering biological, technological, practical and statistical aspects is very important, because the experimental design space is essentially limitless. Usually, the ranges of variable biological parameters of the design space are based on common practices and in turn on phenotypic endpoints. However, specific sub-cellular processes might only be partially reflected by phenotypic endpoints or outside the associated parameter range. Here, we provide a generic protocol for range finding in design for transcriptomics experimentation based on small-scale gene-expression experiments to help in the search for the right location in the design space by analyzing the activity of already known genes of relevant molecular mechanisms. Two examples illustrate the applicability: in-vitro UV-C exposure of mouse embryonic fibroblasts and in-vivo UV-B exposure of mouse skin. Our pragmatic approach is based on: framing a specific biological question and associated gene-set, performing a wide-ranged experiment without replication, eliminating potentially non-relevant genes, and determining the experimental 'sweet spot' by gene-set enrichment plus dose-response correlation analysis. Examination of many cellular processes that are related to UV response, such as DNA repair and cell-cycle arrest, revealed that basically each cellular (sub-) process is active at its own specific spot(s) in the experimental design space. Hence, the use of range finding, based on an affordable protocol like this, enables researchers to conveniently identify the 'sweet spot' for their cellular process of interest in an experimental design space and might have far-reaching implications for experimental standardization.

Indexed as

Research DesignAnimalsCrosses, GeneticDose-Response Relationship, RadiationFibroblastsGene Expression ProfilingGene Expression RegulationMaleMiceMicroarray AnalysisSkinUltraviolet Rays

Identifiers

PMID24823911
PMCPMC4019648
OpenAlexW2127784993

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.