Supporting data for "Arabidopsis phenotyping through Geometric Morphometrics"

Dataset type: Imaging
Data released on June 11, 2018

Manacorda CA; Asurmendi S (2018): Supporting data for "Arabidopsis phenotyping through Geometric Morphometrics" GigaScience Database.


Recently, much technical progress was done regarding plant phenotyping. High-throughput platforms and the development of improved algorithms for the rosette image segmentation make now possible to massively extract shape and size parameters for genetic, physiological and environmental studies. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of platform and segmentation software used are still lacking and shape descriptions still rely on ad hoc or even sometimes contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations amongst groups and measure them in shape distance units. Here, it is proposed a particular scheme of landmarks placement on Arabidopsis rosette images to study shape variation in the case of viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown and reproducibility issues are assessed. Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.

Additional details

Read the peer-reviewed publication(s):

Manacorda, C. A., & Asurmendi, S. (2018). Arabidopsis phenotyping through geometric morphometrics. GigaScience, 7(7). doi:10.1093/gigascience/giy073

File NameSample IDData TypeFile FormatSizeRelease Date 
ImageTAR215.18 MB2018-05-16
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Funding body Awardee Award ID Comments
Fondo para la Investigación Científica y Tecnológica S Asurmendi PICT 2014-1163
Instituto Nacional de Tecnología Agropecuaria (INTA) S Asurmendi PE 11310022
Date Action
June 11, 2018 Dataset publish
July 9, 2018 Manuscript Link added : 10.1093/gigascience/giy073