Abstract
If we want to understand how the environment has shaped the appearance and behavior of living creatures, we need to compare groups of individuals that differ in genetic makeup and environment experience. For complex phenotypic features, such as body posture or facial expression in humans, comparison is not straightforward because some of the contributing factors cannot easily be quantified or averaged across individuals. Therefore, computational methods are used to reconstruct representative prototypes using a range of algorithms for filling in missing information and calculating means. The same problem applies to the root system architecture (RSA) of plants. Several computer programs are available for extracting numerical data from root images, but they usually do not offer customized data analysis or visual reconstruction of RSA. We developed Root-VIS, a free software tool that facilitates the determination of means and variance of many different RSA features across user-selected sets of root images. Furthermore, Root-VIS offers several options to generate visual reconstructions of root systems from the averaged data to enable screening and modeling. We confirmed the suitability of Root-VIS, combined with a new version of EZ-Rhizo, for the rapid characterization of genotype-environment interactions and gene discovery through genome-wide association studies in Arabidopsis (Arabidopsis thaliana).
Highlights
Roots play a critical role in soil water and nutrient uptake and, in plant productivity
Averaging across replicates is easy for some simple root system architecture (RSA) traits, such as main root (MR) length and lateral root (LR) number, but it is less straightforward for other traits
Root-VIS is novel for its ability to average RSA traits using several different algorithms and to visually reconstruct the averaged RSA in several manners
Summary
Roots play a critical role in soil water and nutrient uptake and, in plant productivity. Several platforms to obtain root images from plants grown in soil (field, pots, or rhizotrons) are available, together with software to capture and model the overall size and shape of the complex root systems (Lobet et al, 2011; Bucksch et al, 2014; Kalogiros et al, 2016) These studies are complemented by analyses of roots developing on synthetic surfaces, which offer more precise control and manipulation of the root environment. Reconstructing Root Architecture (Delory et al, 2016, 2018), RootScape (Ristova et al, 2013), and the commercial WinRhizo (Arsenault et al, 1995) They have facilitated the quantitative assessment of more advanced root systems of Arabidopsis and other dicot plants, including information on lateral root features such as position, length, density, and angle. It is important that RSA analysis software, in addition to enabling data acquisition, offers different options for extracting mean root traits and their variance, which can be used as a numerical input for genetic studies or developmental models
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