High‐throughput phenotyping of plant growth rate to screen for waterlogging tolerance in lentil

2021 ◽  
Author(s):  
Lachlan Lake ◽  
Naila Izzat ◽  
Tielei Kong ◽  
Victor O. Sadras
Keyword(s):  
Growth Rate ◽  
Plant Growth ◽  
High Throughput ◽  
Waterlogging Tolerance ◽  
High Throughput Phenotyping

scholarly journals Genetic interactions derived from high-throughput phenotyping of 7,350 yeast cell cycle mutants

2019 ◽  
Author(s):  
Jenna E. Gallegos ◽  
Neil R. Adames ◽  
Mark F. Rogers ◽  
Pavel Kraikivski ◽  
Aubrey Ibele ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Growth Rate ◽  
Cell Division ◽  
Yeast Cell ◽  
High Throughput ◽  
Genetic Interactions ◽  
Control Network ◽  
Cell Cycle Genes ◽  
High Throughput Phenotyping ◽  
Yeast Mutants

AbstractOver the last 30 years, computational biologists have developed increasingly realistic mathematical models of the regulatory networks controlling the division of eukaryotic cells. These models capture data resulting from two complementary experimental approaches: low-throughput experiments aimed at extensively characterizing the functions of small numbers of genes, and large-scale genetic interaction screens that provide a systems-level perspective on the cell division process. The former is insufficient to capture the interconnectivity of the genetic control network, while the latter is fraught with irreproducibility issues. Here, we describe a hybrid approach in which the genetic interactions between 36 cell-cycle genes are quantitatively estimated by high-throughput phenotyping with an unprecedented number of biological replicates. Using this approach, we identify a subset of high-confidence genetic interactions, which we use to refine a previously published mathematical model of the cell cycle. We also present a quantitative dataset of the growth rate of these mutants under six different media conditions in order to inform future cell cycle models.Author SummaryThe process of cell division, also called the cell cycle, is controlled by a highly complex network of interconnected genes. If this process goes awry, diseases such as cancer can result. In order to unravel the complex interactions within the cell cycle control network, computational biologists have developed mathematical models that describe how different cell cycle genes are related. These models are built using large datasets describing the effect of mutating one or more genes within the network. In this manuscript, we present a novel method for producing such datasets. Using our method, we generate 7,350 yeast mutants to explore the interactions between key cell cycle genes. We measure the effect of the mutations by monitoring the growth rate of the yeast mutants under different environmental conditions. We use our mutants to revise an existing model of the yeast cell cycle and present a dataset of ∼44,000 gene by environment combinations as a resource to the yeast genetics and modeling communities.

scholarly journals Strong temporal dynamics of QTL action on plant growth progression revealed through high‐throughput phenotyping in canola

2019 ◽  
Vol 18 (1) ◽  
pp. 68-82 ◽  
Author(s):  
Dominic Knoch ◽  
Amine Abbadi ◽  
Fabian Grandke ◽  
Rhonda C. Meyer ◽  
Birgit Samans ◽  
...  
Keyword(s):  
Plant Growth ◽  
High Throughput ◽  
Temporal Dynamics ◽  
High Throughput Phenotyping

scholarly journals Genetic mapping of the early responses to salt stress in Arabidopsis thaliana

2020 ◽  
Author(s):  
Mariam Awlia ◽  
Nouf Alshareef ◽  
Noha Saber ◽  
Arthur Korte ◽  
Helena Oakey ◽  
...  
Keyword(s):  
Salt Stress ◽  
Quantum Yield ◽  
Plant Growth ◽  
High Throughput ◽  
Stress Responses ◽  
Genome Wide Association Study ◽  
Multivariate Analyses ◽  
Machine Learning Algorithms ◽  
Genetic Components ◽  
High Throughput Phenotyping

AbstractSalt stress decreases plant growth prior to significant ion accumulation in the shoot. However, the processes underlying this rapid reduction in growth are still unknown. To understand the changes in salt stress responses through time and at multiple physiological levels, examining different plant processes within a single setup is required. Recent advances in phenotyping has allowed the image-based estimation of plant growth, morphology, colour and photosynthetic activity. In this study, we examined the salt stress-induced responses of 191 Arabidopsis accessions from one hour to seven days after treatment using high-throughput phenotyping. Multivariate analyses and machine learning algorithms identified that quantum yield measured in the light-adapted state (Fv′/Fm′) greatly affected growth maintenance in the early phase of salt stress, while maximum quantum yield (QY max) was crucial at a later stage. In addition, our genome-wide association study (GWAS) identified 770 loci that were specific to salt stress, in which two loci associated with QY max and Fv′/Fm′ were selected for validation using T-DNA insertion lines. We characterised an unknown protein kinase found in the QY max locus, which reduced photosynthetic efficiency and growth maintenance under salt stress. Understanding the molecular context of the identified candidate genes will provide valuable insights into the early plant responses to salt stress. Furthermore, our work incorporates high-throughput phenotyping, multivariate analyses and GWAS, uncovering details of temporal stress responses, while identifying associations across different traits and time points, which likely constitute the genetic components of salinity tolerance.

scholarly journals Unmanned Aircraft System- (UAS-) Based High-Throughput Phenotyping (HTP) for Tomato Yield Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Anjin Chang ◽  
Jinha Jung ◽  
Junho Yeom ◽  
Murilo M. Maeda ◽  
Juan A. Landivar ◽  
...  
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
High Throughput ◽  
Unmanned Aircraft ◽  
Yield Prediction ◽  
Phenotypic Data ◽  
Breeding Programs ◽  
Tomato Yield ◽  
High Throughput Phenotyping ◽  
Aircraft System

Yield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high R 2 values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.

scholarly journals High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

2017 ◽  
Vol 173 (3) ◽  
pp. 1554-1564 ◽  
Author(s):  
Xuehai Zhang ◽  
Chenglong Huang ◽  
Di Wu ◽  
Feng Qiao ◽  
Wenqiang Li ◽  
...  
Keyword(s):  
Plant Growth ◽  
Qtl Mapping ◽  
High Throughput ◽  
Genetic Architecture ◽  
Maize Plant ◽  
High Throughput Phenotyping

scholarly journals In-field High Throughput Phenotyping and Cotton Plant Growth Analysis Using LiDAR

2018 ◽  
Vol 9 ◽  
Author(s):  
Shangpeng Sun ◽  
Changying Li ◽  
Andrew H. Paterson ◽  
Yu Jiang ◽  
Rui Xu ◽  
...  
Keyword(s):  
Plant Growth ◽  
Cotton Plant ◽  
High Throughput ◽  
Growth Analysis ◽  
High Throughput Phenotyping

scholarly journals A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ronghao Wang ◽  
Yumou Qiu ◽  
Yuzhen Zhou ◽  
Zhikai Liang ◽  
James C. Schnable
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Data Analysis ◽  
Statistical Analysis ◽  
Plant Growth ◽  
High Throughput ◽  
Confidence Region ◽  
Morphological Operations ◽  
Growth Curve Analysis ◽  
High Throughput Phenotyping

High-throughput phenotyping system has become more and more popular in plant science research. The data analysis for such a system typically involves two steps: plant feature extraction through image processing and statistical analysis for the extracted features. The current approach is to perform those two steps on different platforms. We develop the package “implant” in R for both robust feature extraction and functional data analysis. For image processing, the “implant” package provides methods including thresholding, hidden Markov random field model, and morphological operations. For statistical analysis, this package can produce nonparametric curve fitting with its confidence region for plant growth. A functional ANOVA model to test for the treatment and genotype effects on the plant growth dynamics is also provided.

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