Transcriptomics Tutorial


Carlos Hernandez-Garcia, The Ohio State University

This tutorial provides a list of plant gene-expression databases and related resources, provides step-by-step instructions to generate gene expression profiles, reviews considerations relevant to the use of gene expression databases, and uses web-based tools for visualization of transcriptomic data.


Expression databases hosting microarray-derived data have been fundamental to study gene expression in plants; however, this technology is biased toward known ribonucleic acids (RNAs) used to generate the probes for microarray chips. With the advent of next-generation sequencing (RNA-Seq), global RNA (transcriptome) analysis is becoming routine for many plant species. RNA-Seq is a powerful tool not only to validate gene annotation, but also to unravel quantitative gene expression for all sets of genes transcribed in a sample. The vast amount of information generated using RNA-Seq technology allows the generation of databases that capture a wider snapshot of the transcriptome, including absolute numbers of transcripts for most genes in the genome.

See below for the attached pdf tutorial.

Screenshot of the soybase homepage.

Figure 1. Screenshot of the soybase homepage. Soybase is one of the expression databases featured in the tutorial. Screenshot credit: Heather Merk, The Ohio State University.

References Cited

  • Kalinski, A., J. M. Weisman, B. F. Mathews, and M. Herman. 1989. Molecular cloning of a protein associated with soybean seed oil bodies that is similar to thiol proteases of the papain family. Journal of Biological Chemistry 265: 13843-13848.
  • Libault, M, A. Farmer, T. Joshi, K. Takahashi, R. J. Langley, L. D. Franklin, J. He, D. Xu, G. May, and G. Stacey. 2010. An integrated transcriptome atlas of the crop model (Glycine max) and its use in comparative analyses in plants. Plant Journal 63: 86-99. (Available online at: (verified 3 May 2012).
  • Natarajan, S. S., C. Xu, H. Bae, T. J. Caperna, and W. Garrett. 2007. Determination of optimal protein quantity required to identify abundant and less abundant soybean seed proteins by 2D-PAGE and MS. Plant Molecular Biology Report 25: 55-62.
  • Severin, A., J. Woody, Y. –T. Bolon, B. Joseph, B. Diers, A. Farmer, G. Muehlbauer, R. Nelson, D. Grant, J. Specht, M. Graham, S. Cannon, G. May, C. Vance, and R. Shoemaker. 2010. RNA-Seq Atlas of Glycine max: A guide to the soybean transcriptome. BMC Plant Biology 10: 160. (Available online at: (verified 3 May 2012).
  • Vodkin, L.O., and N. V. Raikhel. 1986. Soybean lectin and related proteins in seeds and roots of Le+ and Le− soybean varieties. Plant Physiology 81: 558–565.

External Links

Funding Statement

Development of this lesson was supported in part by the National Institute of Food and Agriculture (NIFA) Solanaceae Coordinated Agricultural Project, agreement 2009-85606-05673, administered by Michigan State University and CONACYT, Mexico. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the United States Department of Agriculture or any of the other aforementioned entities.


TranscriptomicTutorialCarlosfinal.pdf (3.11 MB)

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