What is cost-efficient phenotyping? Optimizing costs for different scenarios

Progress in remote sensing and robotic technologies decreases the hardware costs of phenotyping. Here, we first review cost-effective imaging devices and environmental sensors, and present a trade-off between investment and manpower costs. We then discuss the structure of costs in various real-world scenarios. Hand-held low-cost sensors are suitable for quick and infrequent plant diagnostic measurements. In experiments for genetic or agronomic analyses, (i) major costs arise from plant handling and manpower; (ii) the total costs per plant/microplot are similar in robotized platform or field experiments with drones, hand-held or robotized ground vehicles; (iii) the cost of vehicles carrying sensors represents only 5–26% of the total costs. These conclusions depend on the context, in particular for labor cost, the quantitative demand of phenotyping and the number of days available for phenotypic measurements due to climatic constraints. Data analysis represents 10–20% of total cost if pipelines have already been developed. A trade-off exists between the initial high cost of pipeline development and labor cost of manual operations. Overall, depending on the context and objsectives, “cost-effective” phenotyping may involve either low investment (“affordable phenotyping”), or initial high investments in sensors, vehicles and pipelines that result in higher quality and lower operational costs

Genome and Environmental Activity of a Chrysochromulina parva Virus and Its Virophages

Some giant viruses are ecological agents that are predicted to be involved in the top-down control of single-celled eukaryotic algae populations in aquatic ecosystems. Despite an increased interest in giant viruses since the discovery and characterization of Mimivirus and other viral giants, little is known about their physiology and ecology. In this study, we characterized the genome and functional potential of a giant virus that infects the freshwater haptophyte Chrysochromulina parva, originally isolated from Lake Ontario. This virus, CpV-BQ2, is a member of the nucleo-cytoplasmic large DNA virus (NCLDV) group and possesses a 437 kb genome encoding 503 ORFs with a GC content of 25%. Phylogenetic analyses of core NCLDV genes place CpV-BQ2 amongst the emerging group of algae-infecting Mimiviruses informally referred to as the “extended Mimiviridae,” making it the first virus of this group to be isolated from a freshwater ecosystem. During genome analyses, we also captured and described the genomes of three distinct virophages that co-occurred with CpV-BQ2 and likely exploit CpV for their own replication. These virophages belong to the polinton-like viruses (PLV) group and encompass 19–23 predicted genes, including all of the core PLV genes as well as several genes implicated in genome modifications. We used the CpV-BQ2 and virophage reference sequences to recruit reads from available environmental metatranscriptomic data to estimate their activity in fresh waters. We observed moderate recruitment of both virus and virophage transcripts in samples obtained during Microcystis aeruginosa blooms in Lake Erie and Lake Tai, China in 2013, with a spike in activity in one sample. Virophage transcript abundance for two of the three isolates strongly correlated with that of the CpV-BQ2. Together, the results highlight the importance of giant viruses in the environment and establish a foundation for future research on the physiology and ecology CpV-BQ2 as a model system for algal Mimivirus dynamics in freshwaters

Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput.DescriptionHere, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size.ConclusionDIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://​dirt.​iplantcollaborat​ive.​org/​ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science