A Novel Method for Screening Arabidopsis Mutants in silico based on Statistical Modeling

Computational phenomics becomes more important in the remarkable advance of functional genomics. For the research of functional genomics, to establish the high-throughput phenotypic screening system is a matter of vital importance. Towards the high-throughput screening system, we have proposed a novel methodology of the phenotypic analysis which is based on the 3-dimensional(3D) model reconstruction via surface measurement by the Laser Range Finder(LRF) and the computational extraction of morphological traits on the 3D reconstructed model[3]. Several conventional studies of mutant screens depend on visual observation of digital images[2][5]. However, visual observation is insufficient for the detection of subtle changes and may cause subjective bias. Thus we propose a novel method of in silico phenotypic mutant screens by applying statistical modeling using gaussian mixtures and parameter estimation method to quantified traits. In this report, we attempt an experiment of the proposed mutant screening method to investigate its effectiveness.