Evidence map›Paper›PMID 25958198›Full record

ArticleBMC genomics2015

RNA-Seq versus oligonucleotide array assessment of dose-dependent TCDD-elicited hepatic gene expression in mice.

Rance Nault, Kelly A Fader, Tim Zacharewski

Open access · goldAbstract readComparative Study
In one paragraph

Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

25 citing papers in PubMed, 40 citations in OpenAlex.

  1. Functional genomic analysis of non-canonical DNA regulatory elements of the aryl hydrocarbon receptor.Toxicological sciences : an official journal of the Society of Toxicology · 2026
    Article
  2. Article
  3. Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations.Toxicological sciences : an official journal of the Society of Toxicology · 2025
    Article
  4. Review
  5. Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice.Toxicological sciences : an official journal of the Society of Toxicology · 2023
    Article
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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.

Rance NaultDepartment of Biochemistry and Molecular Biology, Center for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA. naultran@msu.edu.
Kelly A FaderDepartment of Biochemistry and Molecular Biology, Center for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA. kfader@msu.edu.
Tim ZacharewskiDepartment of Biochemistry and Molecular Biology, Center for Integrative Toxicology, Michigan State University, East Lansing, MI, 48824, USA. tzachare@msu.edu.
Michigan State University · US

Funding

VOLATILE ORGANIC CONTAMINANTS--QUANTIFYING THEIR MOVEMENT IN THE UNSATURATED ZONEP42ES004911 · NIEHS · MICHIGAN STATE UNIVERSITY · PI Timothy R. Zacharewski · 1989 to 2026
$64.9M
NIEHS NIH HHS NIEHS SBRP P42ES04911NIEHS NIH HHS P42 ES004911
6 · The paper itself

Abstract

backgroundDose-dependent differential gene expression provides critical information required for regulatory decision-making. The lower costs associated with RNA-Seq have made it the preferred technology for transcriptomic analysis. However, concordance between RNA-Seq and microarray analyses in dose response studies has not been adequately vetted.

resultsWe compared the hepatic transcriptome of C57BL/6 mice following gavage with sesame oil vehicle, 0.01, 0.03, 0.1, 0.3, 1, 3, 10, or 30 μg/kg TCDD every 4 days for 28 days using Illumina HiSeq RNA-Sequencing (RNA-Seq) and Agilent 4 × 44 K microarrays using the same normalization and analysis approach. RNA-Seq and microarray analysis identified a total of 18,063 and 16,403 genes, respectively, that were expressed in the liver. RNA-Seq analysis for differentially expressed genes (DEGs) varied dramatically depending on the P1(t) cut-off while microarray results varied more based on the fold change criteria, although responses strongly correlated. Verification by WaferGen SmartChip QRTPCR revealed that RNA-Seq had a false discovery rate of 24% compared to 54% for microarray analysis. Dose-response modeling of RNA-Seq and microarray data demonstrated similar point of departure (POD) and ED50 estimates for common DEGs.

conclusionsThere was a strong correspondence between RNA-Seq and Agilent array transcriptome profiling when using the same samples and analysis strategy. However, RNA-Seq provided superior quantitative data, identifying more genes and DEGs, as well as qualitative information regarding identity and annotation for dose response modeling in support of regulatory decision-making.

Indexed as

AnimalsDose-Response Relationship, DrugFemaleGene Expression ProfilingGene Expression RegulationLiverMiceMice, Inbred C57BLOligonucleotide Array Sequence AnalysisPolychlorinated DibenzodioxinsSequence Analysis, RNAPolychlorinated Dibenzodioxins

Identifiers

PMID25958198
PMCPMC4456707
OpenAlexW1489807667

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.