GiA Roots: software for the high throughput analysis of plant root system architecture

BMC Plant Biology

Software

Brad Moore¹  Paul R Zurek²  Christopher N Topp²  Philip N Benfey²  Anjali S Iyer-Pascuzzi²  Suqin Fang³  Taras Galkovskyi⁴  Yuriy Mileyko⁴  John Harer⁵  Charles A Price⁶  Olga Symonova⁷  Alexander Bucksch⁸  Joshua S Weitz⁹ 

[1] Department of Biology, Duke University, Durham, NC, USA;Department of Biology, Duke University, Durham, NC, USA;Duke Center for Systems Biology, Duke University, Durham, NC, USA;Department of Biology, Duke University, Durham, NC, USA;Duke Center for Systems Biology, Duke University, Durham, NC, USA;Root Biology Center, South China Agricultural University, Guangzhou, China;Department of Mathematics, Duke University, Durham, NC, USA;Department of Mathematics, Duke University, Durham, NC, USA;Duke Center for Systems Biology, Duke University, Durham, NC, USA;Department of Plant Biology, University of Western Australia, Perth, Australia;Institute of Science and Technology, Vienna, Austria;School of Biology, Georgia Institute of Technology, Atlanta, GA, USA;School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA;School of Biology, Georgia Institute of Technology, Atlanta, GA, USA;School of Physics, Georgia Institute of Technology, Atlanta, GA, USA;

关键词: Processing Pipeline;    Root System Architecture;    Root Crown;    Adaptive Thresholding;    Thresholding Algorithm;   

DOI  :  10.1186/1471-2229-12-116

 received in 2012-01-13, accepted in 2012-06-28,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundCharacterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.ResultsWe have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.ConclusionsWe demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

【 授权许可】

Unknown   
© Galkovskyi et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.