Evidence map›Paper›PMID 18549499›Full record

ArticleBMC genomics2008

Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories.

Michael J Boedigheimer, Russell D Wolfinger, Michael B Bass, Pierre R Bushel, Jeff W Chou, Matthew Cooper, J Christopher Corton, Jennifer Fostel, Susan Hester, Janice S Lee and 6 more

Abstract read
In one paragraph

Article in BMC genomics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 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

51 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

16 authors.

Michael J BoedigheimerCDER, US FDA, Silver Spring, MD 20993, USA . mboedigh@amgen.com
Russell D Wolfinger
Michael B Bass
Pierre R Bushel
Jeff W Chou
Matthew Cooper
J Christopher Corton
Jennifer Fostel
Susan Hester
Janice S Lee
Fenglong Liu
Jie Liu
Hui-Rong Qian
John Quackenbush
Syril Pettit
Karol L Thompson

Funding

Microarray and Genome InformaticsZIAES102345 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI BUSHEL, ROBERT · 2009 to 2021
$10.8M
Microarray and Genome InformaticsZ01ES102345 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI BUSHEL, PIERRE · 2007 to 2008
$2.5M
6 · The paper itself

Abstract

backgroundThe use of gene expression profiling in both clinical and laboratory settings would be enhanced by better characterization of variance due to individual, environmental, and technical factors. Meta-analysis of microarray data from untreated or vehicle-treated animals within the control arm of toxicogenomics studies could yield useful information on baseline fluctuations in gene expression, although control animal data has not been available on a scale and in a form best served for data-mining.

resultsA dataset of control animal microarray expression data was assembled by a working group of the Health and Environmental Sciences Institute's Technical Committee on the Application of Genomics in Mechanism Based Risk Assessment in order to provide a public resource for assessments of variability in baseline gene expression. Data from over 500 Affymetrix microarrays from control rat liver and kidney were collected from 16 different institutions. Thirty-five biological and technical factors were obtained for each animal, describing a wide range of study characteristics, and a subset were evaluated in detail for their contribution to total variability using multivariate statistical and graphical techniques.

conclusionThe study factors that emerged as key sources of variability included gender, organ section, strain, and fasting state. These and other study factors were identified as key descriptors that should be included in the minimal information about a toxicogenomics study needed for interpretation of results by an independent source. Genes that are the most and least variable, gender-selective, or altered by fasting were also identified and functionally categorized. Better characterization of gene expression variability in control animals will aid in the design of toxicogenomics studies and in the interpretation of their results.

Indexed as

Gene Expression ProfilingGenetic VariationAnimalsComputational BiologyDatabases, Nucleic AcidDiscriminant AnalysisFastingFemaleKidneyLiverMaleMultivariate AnalysisOligonucleotide Array Sequence AnalysisPrincipal Component AnalysisRatsRats, Inbred F344

Identifiers

PMID18549499
PMCPMC2453529

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Registered trials

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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.