Evidence map›Paper›PMID 21450099›Full record

ArticleBMC genomics2011

Stochastic variation of transcript abundance in C57BL/6J mice.

Peter T Vedell, Karen L Svenson, Gary A Churchill

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

16 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Article
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  4. Review
  5. Article
  6. Predictability of human differential gene expression.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  7. Article
  8. Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2018
    Article
  9. Article
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  12. Review
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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

3 authors at 1 institution in 1 country.

Peter T VedellThe Jackson Laboratory, Bar Harbor, Maine 04609, USA.
Karen L Svenson
Gary A Churchill
Jackson Laboratory · US

Funding

UNC and PERLEGEN, IncP50GM076468 · NIGMS · JACKSON LABORATORY · PI SMITH, RANDY VON · 2006 to 2015
$31.0M
Assessing Variation in mRNA Transcript Levels in Mice Using Microarray DataF32GM087849 · NIGMS · JACKSON LABORATORY · PI VEDELL, PETER T · 2009 to 2010
$68k
NIGMS NIH HHS F32GM087849-01NIGMS NIH HHS GM076468NIGMS NIH HHS P50 GM076468
6 · The paper itself

Abstract

backgroundTranscripts can exhibit significant variation in tissue samples from inbred laboratory mice. We have designed and carried out a microarray experiment to examine transcript variation across samples from adipose, heart, kidney, and liver tissues of C57BL/6J mice and to partition variation into within-mouse and between-mouse components. Within-mouse variance captures variation due to heterogeneity of gene expression within tissues, RNA-extraction, and array processing. Between-mouse variance reflects differences in transcript abundance between genetically identical mice.

resultsThe nature and extent of transcript variation differs across tissues. Adipose has the largest total variance and the largest within-mouse variance. Liver has the smallest total variance, but it has the most between-mouse variance. Genes with high variability can be classified into groups with correlated patterns of expression that are enriched for specific biological functions. Variation between mice is associated with circadian rhythm, growth hormone signaling, immune response, androgen regulation, lipid metabolism, and the extracellular matrix. Genes showing correlated patterns of within-mouse variation are also associated with biological functions that largely reflect heterogeneity of cell types within tissues.

conclusionsGenetically identical mice can experience different individual outcomes for medically important traits. Variation in gene expression observed between genetically identical mice can identify functional classes of genes that are likely to vary in the absence of experimental perturbations, can inform experimental design decisions, and provides a baseline for the interpretation of gene expression data in interventional studies. The extent of transcript variation among genetically identical mice underscores the importance of stochastic and micro-environmental factors and their phenotypic consequences.

Indexed as

Genetic VariationOligonucleotide Array Sequence AnalysisAdipose TissueAnalysis of VarianceAnimalsComputational BiologyDatabases, GeneticGene Regulatory NetworksKidneyLiverMaleMiceMice, Inbred C57BLMyocardiumSequence Analysis, DNAStochastic Processes

Identifiers

PMID21450099
PMCPMC3082245
OpenAlexW1994579532

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.