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Article Dans Une Revue (Data Paper) Data in Brief Année : 2023

Unraveling the dataset transcriptomic response of Hydrangea macrophylla stem to mechanical stimulation: De novo assembly and functional annotation

Résumé

A crucial attribute of potted ornamental plants is compactness characterized by well branched plants with rather short stems bearing numerous flowers. To gain plant compactness, producers use plant growth regulators (PGRs), in particular growth retardants during culture. However, due to their negative environmental impacts, growth retardants are progressively withdrawn from the market. As a response, eco-friendly alternative methods to chemicals need to be developed. One method consists in mimicking mechanical stimulation (MS) imposed by wind on plants which causes reduction in stem elongation, an increase in stem diameter and an increase in branching, all contributing to plant compactness. So far, few plant species were studied under MS and little is known on molecular response mechanisms to MS. This first transcriptomic data after MS in Hydrangea macrophylla will contribute unravelling how plants respond to mechanical stimuli. RNAseq data were obtained from total mRNA of stems collected 15 min before MS and 1, 3, 24 and 72 h after MS treatment. RNA from non-MS treated plants were used as control. MS treatment consisted in 12 consecutive bendings (i.e. 6 forth and 6 back) applied at 9 a.m. during 1 h and for a single day. From RNAseq data a de novo assembly of the transcriptome was produced and 78,398 transcripts functionally annotated. These transcriptomic data also contribute to a better knowledge of how outdoor crop respond to the increasing frequency of strong harmful winds under climate change.
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hal-04423961 , version 1 (29-01-2024)

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Paternité - Pas d'utilisation commerciale - Pas de modification

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Béra Ley-Ngardigal, Sandra Pelletier, Vincent Guérin, Lydie Huché-Thélier, Nathalie Brouard, et al.. Unraveling the dataset transcriptomic response of Hydrangea macrophylla stem to mechanical stimulation: De novo assembly and functional annotation. Data in Brief, 2023, 51, pp.109784. ⟨10.1016/j.dib.2023.109784⟩. ⟨hal-04423961⟩
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