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Cited by 512Gene expression and cancer classificationGenomics and Phylogenetic StudiesBioinformatics and Genomic Networks

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

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Wang, Charles, Gong, Binsheng, Bushel, Pierre R, Thierry-Mieg, Jean, Thierry-Mieg, Danielle, Xu, Joshua, Fang, Hong, Hong, Huixiao +38 more·2014·Nature Biotechnology
DOI 10.1038/nbt.3001PMC PMC4243706
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Abstract

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R(2)0.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

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  • Among 23.2 billion reads mapped to genes other than mitochondrial or ribosomal RNAs, 85.9% mapped to RefSeq, 92.9% to GENCODE, and 97.1% to NCBI AceView.

    “Among all 23.2 billion reads that could be mapped to genes other than those encoding mitochondrial or ribosomal RNAs, 85.9% were mapped to RefSeq, 12 whereas 92.9% mapped to GENCODE, 13 and 97.1% to NCBI AceView 14 (Fig. 2a).”

    From A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium (2014) · cites this paper as [12]

    The Results (Study design; page unavailable) say the study used an AceView-based RNA-seq pipeline and refer to mapping details, but do not give the claimed read-mapping percentages. Searches for 23.2 billion reads and for the RefSeq, GENCODE, and AceView percentages found no passage in our copy reporting those figures. (LLM, conf=0.73)

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Authors
Wang, C., Gong, B., Bushel, P. R., Thierry-Mieg, J., Thierry-Mieg, D., Xu, J., Fang, H., Hong, H., Shen, J., Su, Z., Meehan, J., Li, X., Yang, L., Li, H., Łabaj, P. P., Kreil, D. P., Megherbi, D., Gaj, S., Caiment, F., van Delft, J., Kleinjans, J., Scherer, A., Devanarayan, V., Wang, J., Yang, Y., Qian, H.-R., Lancashire, L. J., Bessarabova, M., Nikolsky, Y., Furlanello, C., Chierici, M., Albanese, D., Jurman, G., Riccadonna, S., Filosi, M., Visintainer, R., Zhang, K. K., Li, J., Hsieh, J.-H., Svoboda, D. L., Fuscoe, J. C., Deng, Y., Shi, L., Paules, R. S., Auerbach, S. S., & Tong, W.
Affiliations
  • Loma Linda University (US)
  • National Center for Toxicological Research (US)
  • National Institutes of Health (US)
  • City Of Hope National Medical Center (US)
  • BOKU University (AT)
  • University of Warwick (GB)
  • University of Massachusetts Lowell (US)
  • Maastricht University (NL)
  • Walter and Eliza Hall Institute of Medical Research (AU)
  • AbbVie (United States) (US)
  • Eli Lilly (United States) (US)
  • Thomson Reuters (United States) (US)
  • Russian Academy of Sciences (RU)
  • Fondazione Bruno Kessler (IT)
  • Fondazione Edmund Mach (IT)
  • University of North Dakota (US)
  • National Institute of Environmental Health Sciences (US)
  • SRA International (United States) (US)
  • Rush University Medical Center (US)
Corresponding author
  • Bushel, Pierre R
  • Auerbach, Scott S
  • Tong, Weida
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2014-08-24
Volume
32
Issue
9
Pages
926-932
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/nbt.3001
PMC
PMC4243706
OpenAlex
W1989996002
Cited by
512
References
7
Citations by year
2026: 9 · 2025: 24 · 2024: 61 · 2023: 37 · 2022: 26 · 2021: 61 · 2020: 38 · 2019: 39 · 2018: 39 · 2017: 46 · 2016: 63 · 2015: 56 · 2014: 12
FWCI
24.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
  • Research Publications (Maastricht University) (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
  • CINECA IRIS Institutional Research Information System (Fondazione Edmund Mach) (submittedVersion, other-oa)
  • Research Publications (Maastricht University) (submittedVersion)
Funders
  • Vienna Science and Technology Fund
  • National Institutes of Health
  • National Institute of Environmental Health Sciences (ZIAES102345, ES102345-04, Z01ES023026, Z01ES102345)
  • U.S. National Library of Medicine
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
cross-platform comparison, chemical exposure, differential gene expression, mRNA levels, low-abundance transcripts, mechanism of action, hepatotoxicity, quantitative PCR validation, high-throughput compound screening
MeSH terms
Animals, RNA, Messenger, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Rats

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