BMC Plant Biology (Jul 2020)

The oilseed rape developmental expression resource: a resource for the investigation of gene expression dynamics during the floral transition in oilseed rape

  • D. Marc Jones,
  • Tjelvar S. G. Olson,
  • Nick Pullen,
  • Rachel Wells,
  • Judith A. Irwin,
  • Richard J. Morris

DOI
https://doi.org/10.1186/s12870-020-02509-x
Journal volume & issue
Vol. 20, no. 1
pp. 1 – 10

Abstract

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Abstract Background Transcriptome time series can be used to track the expression of genes during development, allowing the timing, intensity, and dynamics of genetic programmes to be determined. Furthermore, time series analysis can reveal causal relationships between genes, leading to an understanding of how the regulatory networks are rewired during development. Due to its impact on yield, a developmental transition of agricultural interest in crops is the switch from vegetative to floral growth. We previously reported the collection of genome-wide gene expression data during the floral transition in the allopolyploid crop Brassica napus (oilseed rape, OSR). To provide the OSR research community with easy access to this dataset, we have developed the Oilseed Rape Developmental Expression Resource (ORDER; http://order.jic.ac.uk ). Results ORDER enables users to search for genes of interest and plot expression patterns during the floral transition in both a winter and a spring variety of OSR. We illustrate the utility of ORDER using two case studies: the first investigating the interaction between transcription factors, the second comparing genes that mediate the vernalisation response between OSR and radish (Raphanus sativus L.). All the data is downloadable and the generic website platform underlying ORDER, called AionPlot, is made freely and openly available to facilitate the dissemination of other time series datasets. Conclusions ORDER provides the OSR research community with access to a dataset focused on a period of OSR development important for yield. AionPlot, the platform on which ORDER is built, will allow researchers from all fields to share similar time series datasets.

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