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

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.

Figure 1

Annotated snapshot of the GiA Roots GUI. The GiA Roots GUI is a standalone application that provides a user interface to manage input images, define a processing pipeline and manage output. It features a main window with several task windows that can be accessed through the left sidebar. Sequential access to each window page accomplishes major tasks: managing data; selecting traits to measure; tweaking parameters; performing processing; reviewing the results. This linear design helps users to keep track of progress and proceed intuitively with processing of the data. Main computational steps are described in greater detail in Implementation.

Figure 2

GiA Roots processing chart. Data types are enclosed in ellipses, interactions are enclosed in rectangles. Interactions are realized by plugins, and have several variants. They can also be configured.

Figure 3

Comparison of GiA Roots estimation of trait values against a prior benchmark. The two plots represent a comparison of GiA Roots estimation of trait values compared against a previously validated set of algorithms [21]. Each point represents a trait estimate from one of 2393 images. Note that the median number of roots is defined to be an integer so nearly all comparisons in the left panel exactly coincide, but when plotted they appear to give rise to a ‘gridded’ pattern. The R²values confirm the strong correspondence of the two implementations of the same trait.

Figure 4

Comparison of the three available thresholding algorithms in GiA Roots. The three algorithms are global thresholding (GT), adaptive thresholding (AT), and double adaptive thresholding (DAT). Each algorithm is demonstrated on two relevant examples with default parameter settings and manually optimized parameter settings. Annotations 1-11 point at significant differences which are detailed in the text.

Figure 5

Schematic of GiA Roots extended with new trait estimation algorithms. A set of new traits is highlighted in the “Features & Algorithms” panel of GiA Roots. Installing new plugins is accomplished by copying the compiled plugin and documentation into the GiA Roots folder.