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

Taras Galkovskyi1, Yuriy Mileyko1, Alexander Bucksch23, Brad Moore4, Olga Symonova5, Charles A Price6, Christopher N Topp47, Anjali S Iyer-Pascuzzi47, Paul R Zurek47, Suqin Fang478, John Harer17, Philip N Benfey47 and Joshua S Weitz29*

Author Affiliations

1 Department of Mathematics, Duke University, Durham, NC, USA

2 School of Biology, Georgia Institute of Technology, Atlanta, GA, USA

3 School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA

4 Department of Biology, Duke University, Durham, NC, USA

5 , Institute of Science and Technology, Vienna, Austria

6 Department of Plant Biology, University of Western Australia, Perth, Australia

7 Duke Center for Systems Biology, Duke University, Durham, NC, USA

8 Root Biology Center, South China Agricultural University, Guangzhou, China

9 School of Physics, Georgia Institute of Technology, Atlanta, GA, USA

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BMC Plant Biology 2012, 12:116  doi:10.1186/1471-2229-12-116

Published: 26 July 2012

Abstract

Background

Characterizing 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.

Results

We 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.

Conclusions

We 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.