Maize Gene Atlas Developed by RNA Sequencing and Comparative Evaluation of Transcriptomes Based on RNA Sequencing and Microarrays
Open accessAbstract
Transcriptome analysis is a valuable tool for identification and characterization of genes and pathways underlying plant growth and development. We previously published a microarray-based maize gene atlas from the analysis of 60 unique spatially and temporally separated tissues from 11 maize organs [1]. To enhance the coverage and resolution of the maize gene atlas, we have analyzed 18 selected tissues representing five organs using RNA sequencing (RNA-Seq). For a direct comparison of the two methodologies, the same RNA samples originally used for our microarray-based atlas were evaluated using RNA-Seq. Both technologies produced similar transcriptome profiles as evident from high Pearson's correlation statistics ranging from 0.70 to 0.83, and from nearly identical clustering of the tissues. RNA-Seq provided enhanced coverage of the transcriptome, with 82.1% of the filtered maize genes detected as expressed in at least one tissue by RNA-Seq compared to only 56.5% detected by microarrays. Further, from the set of 465 maize genes that have been historically well characterized by mutant analysis, 427 show significant expression in at least one tissue by RNA-Seq compared to 390 by microarray analysis. RNA-Seq provided higher resolution for identifying tissue-specific expression as well as for distinguishing the expression profiles of closely related paralogs as compared to microarray-derived profiles. Co-expression analysis derived from the microarray and RNA-Seq data revealed that broadly similar networks result from both platforms, and that co-expression estimates are stable even when constructed from mixed data including both RNA-Seq and microarray expression data. The RNA-Seq information provides a useful complement to the microarray-based maize gene atlas and helps to further understand the dynamics of transcription during maize development.
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RNA-seq has a higher resolution for identifying tissue-specific expression and distinguishes expression profiles of closely related paralogues better than microarray-derived profiles.
“It increases accuracy for low-abundance transcripts [44], has a higher resolution for identifying tissue-specific expression and distinguishes expression profiles of closely related paralogues better than microarray-derived profiles [45].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [45]
Few conclusions have been drawn about the utility of RNA-seq co-expression networks.
“Very few analyses to date have been performed on co-expression networks from RNA-seq data (although see, e.g.Iancu et al., 2012;Sekhon et al., 2013), with fewer conclusions drawn about their utility, nor has their presumed novelty been fully assessed.”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Sekhon et al., 2013
The presumed novelty of RNA-seq co-expression networks has not been fully assessed.
“Very few analyses to date have been performed on co-expression networks from RNA-seq data (although see, e.g.Iancu et al., 2012;Sekhon et al., 2013), with fewer conclusions drawn about their utility, nor has their presumed novelty been fully assessed.”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Sekhon et al., 2013
Very few analyses of co-expression networks from RNA-seq data have been performed.
“Very few analyses to date have been performed on co-expression networks from RNA-seq data (although see, e.g.Iancu et al., 2012;Sekhon et al., 2013), with fewer conclusions drawn about their utility, nor has their presumed novelty been fully assessed.”
From Guidance for RNA-seq co-expression network construction and analysis: safety in numbers (2015) · cites this paper as Sekhon et al., 2013
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- Authors
- Sekhon, R. S., Briskine, R., Hirsch, C. N., Myers, C. L., Springer, N. M., Buell, C. R., de Leon, N., & Kaeppler, S. M.
- Affiliations
- University of Wisconsin–Madison (US)
- University of Minnesota (US)
- Great Lakes Bioenergy Research Center (US)
- Corresponding author
- Kaeppler, Shawn M.
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-04-23
- Volume
- 8
- Issue
- 4
- Pages
- e61005
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3634062
- OpenAlex
- W2033579591
- Cited by
- 163
- References
- 50
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 5 · 2023: 3 · 2022: 13 · 2021: 14 · 2020: 14 · 2019: 17 · 2018: 12 · 2017: 11 · 2016: 20 · 2015: 27 · 2014: 18 · 2013: 5
- FWCI
- 10.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (1126950, 0953881)
- U.S. Department of Energy (FC02-07ER64494, 07ER64494, DEFC0207ER64494)
- Office of Science (FC02-07ER64494, DEFC0207ER64494)
- Great Lakes Bioenergy Research Center (DE‐FC02‐07ER64494)
- Biological and Environmental Research (DE-FC02-07ER64494)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Molecular Biology Research
- Keywords
- transcriptome, RNA sequencing, microarray comparison, tissue-specific gene expression, co-expression network, maize development, gene expression profiling, maize
- MeSH terms
- Zea mays, Genes, Plant, Sequence Analysis, RNA, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Gene Regulatory Networks
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