Towards a Reference Architecture for Cloud-Based Plant Genotyping and Phenotyping Analysis Frameworks

Abstract

The domain of plant genotyping and phenotyping presents a number of challenges in the area of large data computation. Various tools and systems have been developed to automate the scientific workflows and support the computational needs of this domain. In this paper, we review a number of the popular systems (i.e., Galaxy, iPlant, GenAp and LemnaTec) in the domain of plant genotyping and phenotyping using the scenario-based architectural analysis method (SAAM). In particular, we focus on how different stakeholders are using these systems in a variety of scenarios and to what extent the systems support their needs. Our SAAM analysis shows that the existing systems have shortcomings. For example, they are limited in their support for high throughput processing of large amounts of heterogeneous types of data. Based on our findings we propose a reference architecture along with a preliminary evaluation in the subject domain. The reference architecture and its evaluation is aimed at helping developers/architects create suitable architectural designs and select appropriate technologies when developing plant phenotyping and genotyping systems.