Three-dimensional definition of leaf morphological traits of arabidopsis in silico phenotypic analysis

Eli Kaminuma, Naohiko Heida, Yuko Tsumoto, Miki Nakazawa, Nobuharu Goto, Akihiko Konagaya, Minami Matsui, Tetsuro Toyoda

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The detection of phenotypic alterations of mutants and variants is one of the bottlenecks that hinder systematic gene functional studies of the model plant Arabidopsis. In an earlier study, we have addressed this problem by proposing a novel methodology for phenome analysis based on in silico analysis of polygon models that are acquired by 3-dimensional (3D) measurement and which precisely reconstruct the actual plant shape. However, 3D quantitative descriptions of morphological traits are rare, whereas conventional 2D descriptions have already been studied but may lack the necessary precision. In this report, we focus on six major leaf morphological traits, which are commonly used in the current manual mutant screens, and propose new 3D quantitative definitions that describe these traits. In experiments to extract the traits, we found significant differences between two variants of Arabidopsis with respect to blade roundness and blade epinasty. Remarkably, the detected difference between variants in the blade roundness trait was undetectable when using conventional 2D descriptions. Thus, the result of the experiment indicates that the proposed definitions with 3D description may lead to new discoveries of phenotypic alteration in gene functional studies that would not be possible using conventional 2D descriptions.

Original languageEnglish
Pages (from-to)401-414
Number of pages14
JournalJournal of Bioinformatics and Computational Biology
Volume3
Issue number2
DOIs
Publication statusPublished - Apr 2005
Externally publishedYes

Keywords*

  • 3D
  • Arabidopsis
  • Morphological traits
  • Phenome
  • Polygon model

Field of Science*

  • 1.6 Biological sciences

Publication Type*

  • 1.1. Scientific article indexed in Web of Science and/or Scopus database

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