Journal articles on the topic 'High Throughput Phenotypic Data'
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Wu, Peter I.-Fan, Curtis Ross, Deborah A. Siegele, and James C. Hu. "Insights from the reanalysis of high-throughput chemical genomics data for Escherichia coli K-12." G3 Genes|Genomes|Genetics 11, no. 1 (December 22, 2020): 1–13. http://dx.doi.org/10.1093/g3journal/jkaa035.
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Abstract Despite the demonstrated success of genome-wide genetic screens and chemical genomics studies at predicting functions for genes of unknown function or predicting new functions for well-characterized genes, their potential to provide insights into gene function has not been fully explored. We systematically reanalyzed a published high-throughput phenotypic dataset for the model Gram-negative bacterium Escherichia coli K-12. The availability of high-quality annotation sets allowed us to compare the power of different metrics for measuring phenotypic profile similarity to correctly infer gene function. We conclude that there is no single best method; the three metrics tested gave comparable results for most gene pairs. We also assessed how converting quantitative phenotypes to discrete, qualitative phenotypes affected the association between phenotype and function. Our results indicate that this approach may allow phenotypic data from different studies to be combined to produce a larger dataset that may reveal functional connections between genes not detected in individual studies.
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Kim, Minsu, Chaewon Lee, Subin Hong, Song Lim Kim, JeongHo Baek, and Kyung-Hwan Kim. "High-Throughput Phenotyping Methods for Breeding Drought-Tolerant Crops." International Journal of Molecular Sciences 22, no. 15 (July 31, 2021): 8266. http://dx.doi.org/10.3390/ijms22158266.
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Drought is a main factor limiting crop yields. Modern agricultural technologies such as irrigation systems, ground mulching, and rainwater storage can prevent drought, but these are only temporary solutions. Understanding the physiological, biochemical, and molecular reactions of plants to drought stress is therefore urgent. The recent rapid development of genomics tools has led to an increasing interest in phenomics, i.e., the study of phenotypic plant traits. Among phenomic strategies, high-throughput phenotyping (HTP) is attracting increasing attention as a way to address the bottlenecks of genomic and phenomic studies. HTP provides researchers a non-destructive and non-invasive method yet accurate in analyzing large-scale phenotypic data. This review describes plant responses to drought stress and introduces HTP methods that can detect changes in plant phenotypes in response to drought.
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Xu, Rui, and Changying Li. "A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots." Plant Phenomics 2022 (June 17, 2022): 1–20. http://dx.doi.org/10.34133/2022/9760269.
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Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot’s main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.
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Yu, Sheng, Yumeng Ma, Jessica Gronsbell, Tianrun Cai, Ashwin N. Ananthakrishnan, Vivian S. Gainer, Susanne E. Churchill, et al. "Enabling phenotypic big data with PheNorm." Journal of the American Medical Informatics Association 25, no. 1 (November 3, 2017): 54–60. http://dx.doi.org/10.1093/jamia/ocx111.
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Abstract Objective Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. Methods The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. Results We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn’s disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100–300, with no statistically significant difference. Conclusion The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level – phenotypic big data.
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Reimer, Lorenz Christian, Anna Vetcininova, Joaquim Sardà Carbasse, Carola Söhngen, Dorothea Gleim, Christian Ebeling, and Jörg Overmann. "BacDivein 2019: bacterial phenotypic data for High-throughput biodiversity analysis." Nucleic Acids Research 47, no. D1 (September 26, 2018): D631—D636. http://dx.doi.org/10.1093/nar/gky879.
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Bastarache, Lisa. "Using Phecodes for Research with the Electronic Health Record: From PheWAS to PheRS." Annual Review of Biomedical Data Science 4, no. 1 (July 20, 2021): 1–19. http://dx.doi.org/10.1146/annurev-biodatasci-122320-112352.
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Electronic health records (EHRs) are a rich source of data for researchers, but extracting meaningful information out of this highly complex data source is challenging. Phecodes represent one strategy for defining phenotypes for research using EHR data. They are a high-throughput phenotyping tool based on ICD (International Classification of Diseases) codes that can be used to rapidly define the case/control status of thousands of clinically meaningful diseases and conditions. Phecodes were originally developed to conduct phenome-wide association studies to scan for phenotypic associations with common genetic variants. Since then, phecodes have been used to support a wide range of EHR-based phenotyping methods, including the phenotype risk score. This review aims to comprehensively describe the development, validation, and applications of phecodes and suggest some future directions for phecodes and high-throughput phenotyping.
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Chang, Anjin, Jinha Jung, Junho Yeom, Murilo M. Maeda, Juan A. Landivar, Juan M. Enciso, Carlos A. Avila, and Juan R. Anciso. "Unmanned Aircraft System- (UAS-) Based High-Throughput Phenotyping (HTP) for Tomato Yield Estimation." Journal of Sensors 2021 (February 9, 2021): 1–14. http://dx.doi.org/10.1155/2021/8875606.
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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.
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Crain, Jared, Matthew Reynolds, and Jesse Poland. "Utilizing High-Throughput Phenotypic Data for Improved Phenotypic Selection of Stress-Adaptive Traits in Wheat." Crop Science 57, no. 2 (January 3, 2017): 648–59. http://dx.doi.org/10.2135/cropsci2016.02.0135.
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Kurbatova, Natalja, Jeremy C. Mason, Hugh Morgan, Terrence F. Meehan, and Natasha A. Karp. "PhenStat: A Tool Kit for Standardized Analysis of High Throughput Phenotypic Data." PLOS ONE 10, no. 7 (July 6, 2015): e0131274. http://dx.doi.org/10.1371/journal.pone.0131274.
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Patel, Dhara A., Anand C. Patel, William C. Nolan, Guangming Huang, Arthur G. Romero, Nichole Charlton, Eugene Agapov, Yong Zhang, and Michael J. Holtzman. "High-Throughput Screening Normalized to Biological Response." Journal of Biomolecular Screening 19, no. 1 (July 16, 2013): 119–30. http://dx.doi.org/10.1177/1087057113496848.
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The process of conducting cell-based phenotypic screens can result in data sets from small libraries or portions of large libraries, making accurate hit picking from multiple data sets important for efficient drug discovery. Here, we describe a screen design and data analysis approach that allow for normalization not only between quadrants and plates but also between screens or batches in a robust, quantitative fashion, enabling hit selection from multiple data sets. We independently screened the MicroSource Spectrum and NCI Diversity Set II libraries using a cell-based phenotypic high-throughput screening (HTS) assay that uses an interferon-stimulated response element (ISRE)–driven luciferase-reporter assay to identify interferon (IFN) signal enhancers. Inclusion of a per-plate, per-quadrant IFN dose-response standard curve enabled conversion of ISRE activity to effective IFN concentrations. We identified 45 hits based on a combined z score ≥2.5 from the two libraries, and 25 of 35 available hits were validated in a compound concentration-response assay when tested using fresh compound. The results provide a basis for further analysis of chemical structure in relation to biological function. Together, the results establish an HTS method that can be extended to screening for any class of compounds that influence a quantifiable biological response for which a standard is available.
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Wu, Sheng, Weiliang Wen, Yongjian Wang, Jiangchuan Fan, Chuanyu Wang, Wenbo Gou, and Xinyu Guo. "MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction." Plant Phenomics 2020 (March 12, 2020): 1–17. http://dx.doi.org/10.34133/2020/1848437.
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Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform’s device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.
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Wheeler, Nicolas J., Kendra J. Gallo, Elena J. G. Rehborg, Kaetlyn T. Ryan, John D. Chan, and Mostafa Zamanian. "wrmXpress: A modular package for high-throughput image analysis of parasitic and free-living worms." PLOS Neglected Tropical Diseases 16, no. 11 (November 18, 2022): e0010937. http://dx.doi.org/10.1371/journal.pntd.0010937.
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Advances in high-throughput and high-content imaging technologies require concomitant development of analytical software capable of handling large datasets and generating relevant phenotypic measurements. Several tools have been developed to analyze drug response phenotypes in parasitic and free-living worms, but these are siloed and often limited to specific instrumentation, worm species, and single phenotypes. No unified tool exists to analyze diverse high-content phenotypic imaging data of worms and provide a platform for future extensibility. We have developed wrmXpress, a unified framework for analyzing a variety of phenotypes matched to high-content experimental assays of free-living and parasitic nematodes and flatworms. We demonstrate its utility for analyzing a suite of phenotypes, including motility, development/size, fecundity, and feeding, and establish the package as a platform upon which to build future custom phenotypic modules. We show that wrmXpress can serve as an analytical workhorse for anthelmintic screening efforts across schistosomes, filarial nematodes, and free-living model nematodes and holds promise for enabling collaboration among investigators with diverse interests.
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Ampatzidis, Yiannis, and Victor Partel. "UAV-Based High Throughput Phenotyping in Citrus Utilizing Multispectral Imaging and Artificial Intelligence." Remote Sensing 11, no. 4 (February 17, 2019): 410. http://dx.doi.org/10.3390/rs11040410.
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Traditional plant breeding evaluation methods are time-consuming, labor-intensive, and costly. Accurate and rapid phenotypic trait data acquisition and analysis can improve genomic selection and accelerate cultivar development. In this work, a technique for data acquisition and image processing was developed utilizing small unmanned aerial vehicles (UAVs), multispectral imaging, and deep learning convolutional neural networks to evaluate phenotypic characteristics on citrus crops. This low-cost and automated high-throughput phenotyping technique utilizes artificial intelligence (AI) and machine learning (ML) to: (i) detect, count, and geolocate trees and tree gaps; (ii) categorize trees based on their canopy size; (iii) develop individual tree health indices; and (iv) evaluate citrus varieties and rootstocks. The proposed remote sensing technique was able to detect and count citrus trees in a grove of 4,931 trees, with precision and recall of 99.9% and 99.7%, respectively, estimate their canopy size with overall accuracy of 85.5%, and detect, count, and geolocate tree gaps with a precision and recall of 100% and 94.6%, respectively. This UAV-based technique provides a consistent, more direct, cost-effective, and rapid method to evaluate phenotypic characteristics of citrus varieties and rootstocks.
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Andrade-Sanchez, Pedro, Michael A. Gore, John T. Heun, Kelly R. Thorp, A. Elizabete Carmo-Silva, Andrew N. French, Michael E. Salvucci, and Jeffrey W. White. "Development and evaluation of a field-based high-throughput phenotyping platform." Functional Plant Biology 41, no. 1 (2014): 68. http://dx.doi.org/10.1071/fp13126.
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Physiological and developmental traits that vary over time are difficult to phenotype under relevant growing conditions. In this light, we developed a novel system for phenotyping dynamic traits in the field. System performance was evaluated on 25 Pima cotton (Gossypium barbadense L.) cultivars grown in 2011 at Maricopa, Arizona. Field-grown plants were irrigated under well watered and water-limited conditions, with measurements taken at different times on 3 days in July and August. The system carried four sets of sensors to measure canopy height, reflectance and temperature simultaneously on four adjacent rows, enabling the collection of phenotypic data at a rate of 0.84 ha h–1. Measurements of canopy height, normalised difference vegetation index and temperature all showed large differences among cultivars and expected interactions of cultivars with water regime and time of day. Broad-sense heritabilities (H2)were highest for canopy height (H2 = 0.86–0.96), followed by the more environmentally sensitive normalised difference vegetation index (H2 = 0.28–0.90) and temperature (H2 = 0.01–0.90) traits. We also found a strong agreement (r2 = 0.35–0.82) between values obtained by the system, and values from aerial imagery and manual phenotyping approaches. Taken together, these results confirmed the ability of the phenotyping system to measure multiple traits rapidly and accurately.
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Shu, Meiyan, Shuaipeng Fei, Bingyu Zhang, Xiaohong Yang, Yan Guo, Baoguo Li, and Yuntao Ma. "Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits." Plant Phenomics 2022 (August 28, 2022): 1–17. http://dx.doi.org/10.34133/2022/9802585.
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High-throughput estimation of phenotypic traits from UAV (unmanned aerial vehicle) images is helpful to improve the screening efficiency of breeding maize. Accurately estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior varieties. Constructing an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data. This study aims to apply the ensemble learning model to improve the feasibility and accuracy of estimating maize phenotypic traits using UAV-based red-green-blue (RGB) and multispectral sensors. The UAV images of four growth stages were obtained, respectively. The reflectance of visible light bands, canopy coverage, plant height (PH), and texture information were extracted from RGB images, and the vegetation indices were calculated from multispectral images. We compared and analyzed the estimation accuracy of single-type feature and multiple features for LAI (leaf area index), fresh weight (FW), and dry weight (DW) of maize. The basic models included ridge regression (RR), support vector machine (SVM), random forest (RF), Gaussian process (GP), and K-neighbor network (K-NN). The ensemble learning models included stacking and Bayesian model averaging (BMA). The results showed that the ensemble learning model improved the accuracy and stability of maize phenotypic traits estimation. Among the features extracted from UAV RGB images, the highest accuracy was obtained by the combination of spectrum, structure, and texture features. The model had the best accuracy constructed using all features of two sensors. The estimation accuracies of ensemble learning models, including stacking and BMA, were higher than those of the basic models. The coefficient of determination (R2) of the optimal validation results were 0.852, 0.888, and 0.929 for LAI, FW, and DW, respectively. Therefore, the combination of UAV-based multisource data and ensemble learning model could accurately estimate phenotyping traits of breeding maize at plot scale.
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Wilson, Aaron C., Ioannis K. Moutsatsos, Gary Yu, Javier J. Pineda, Yan Feng, and Douglas S. Auld. "A Scalable Pipeline for High-Throughput Flow Cytometry." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 7 (May 16, 2018): 708–18. http://dx.doi.org/10.1177/2472555218774770.
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Flow cytometry (FC) provides high-content data for a variety of applications, including phenotypic analysis of cell surface and intracellular markers, characterization of cell supernatant or lysates, and gene expression analysis. Historically, sample preparation, acquisition, and analysis have presented as a bottleneck for running such types of assays at scale. This article will outline the solutions that have been implemented at Novartis which have allowed high-throughput FC to be successfully conducted and analyzed for a variety of cell-based assays. While these experiments were generally conducted to measure phenotypic responses from a well-characterized and information-rich small molecular probe library known as the Mechanism-of-Action (MoA) Box, they are broadly applicable to any type of test sample. The article focuses on application of automated methods for FC sample preparation in 384-well assay plates. It also highlights a pipeline for analyzing large volumes of FC data, covering a visualization approach that facilitates review of screen-level data by dynamically embedding FlowJo (FJ) workspace images for each sample into a Spotfire file, directly linking them to the metric being observed. Finally, an application of these methods to a screen for MHC-I expression upregulators is discussed.
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Liao, Katherine P., Jiehuan Sun, Tianrun A. Cai, Nicholas Link, Chuan Hong, Jie Huang, Jennifer E. Huffman, et al. "High-throughput multimodal automated phenotyping (MAP) with application to PheWAS." Journal of the American Medical Informatics Association 26, no. 11 (August 7, 2019): 1255–62. http://dx.doi.org/10.1093/jamia/ocz066.
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Abstract Objective Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). Materials and Methods We developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the Unified Medical Language System. Along with health care utilization, aggregated ICD and NLP counts were jointly analyzed by fitting an ensemble of latent mixture models. The multimodal automated phenotyping (MAP) algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying participants with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort phenome-wide association studies (PheWAS) for 2 single nucleotide polymorphisms with known associations. Results The MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes. Conclusion The MAP approach increased the accuracy of phenotype definition while maintaining scalability, thereby facilitating use in studies requiring large-scale phenotyping, such as PheWAS.
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Cai, Shuangze, Wenbo Gou, Weiliang Wen, Xianju Lu, Jiangchuan Fan, and Xinyu Guo. "Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail." Plants 12, no. 3 (January 20, 2023): 483. http://dx.doi.org/10.3390/plants12030483.
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Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is equipped with an autopilot system, a small electric slide rail, and Light Detection and Ranging (LiDAR) to achieve high-throughput, high-precision automatic crop point cloud acquisition and map building. The phenotype analysis system realized single plant segmentation and pipeline extraction of plant height and maximum crown width of the crop point cloud using the Random sampling consistency (RANSAC), Euclidean clustering, and k-means clustering algorithm. This phenotyping system was used to collect point cloud data and extract plant height and maximum crown width for 54 greenhouse-potted lettuce plants. The results showed that the correlation coefficient (R2) between the collected data and manual measurements were 0.97996 and 0.90975, respectively, while the root mean square error (RMSE) was 1.51 cm and 4.99 cm, respectively. At less than a tenth of the cost of the PlantEye F500, UGV achieves phenotypic data acquisition with less error and detects morphological trait differences between lettuce types. Thus, it could be suitable for actual 3D phenotypic measurements of greenhouse crops.
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Gao, Tian, Feiyu Zhu, Puneet Paul, Jaspreet Sandhu, Henry Akrofi Doku, Jianxin Sun, Yu Pan, Paul Staswick, Harkamal Walia, and Hongfeng Yu. "Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants." Remote Sensing 13, no. 11 (May 27, 2021): 2113. http://dx.doi.org/10.3390/rs13112113.
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The use of 3D plant models for high-throughput phenotyping is increasingly becoming a preferred method for many plant science researchers. Numerous camera-based imaging systems and reconstruction algorithms have been developed for the 3D reconstruction of plants. However, it is still challenging to build an imaging system with high-quality results at a low cost. Useful comparative information for existing imaging systems and their improvements is also limited, making it challenging for researchers to make data-based selections. The objective of this study is to explore the possible solutions to address these issues. We introduce two novel systems for plants of various sizes, as well as a pipeline to generate high-quality 3D point clouds and meshes. The higher accuracy and efficiency of the proposed systems make it a potentially valuable tool for enhancing high-throughput phenotyping by integrating 3D traits for increased resolution and measuring traits that are not amenable to 2D imaging approaches. The study shows that the phenotype traits derived from the 3D models are highly correlated with manually measured phenotypic traits (R2 > 0.91). Moreover, we present a systematic analysis of different settings of the imaging systems and a comparison with the traditional system, which provide recommendations for plant scientists to improve the accuracy of 3D construction. In summary, our proposed imaging systems are suggested for 3D reconstruction of plants. Moreover, the analysis results of the different settings in this paper can be used for designing new customized imaging systems and improving their accuracy.
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Jiang, Yu, and Changying Li. "Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review." Plant Phenomics 2020 (April 9, 2020): 1–22. http://dx.doi.org/10.34133/2020/4152816.
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Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.
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Njage, Patrick Murigu Kamau, Pimlapas Leekitcharoenphon, Lisbeth Truelstrup Hansen, Rene S. Hendriksen, Christel Faes, Marc Aerts, and Tine Hald. "Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes." Microorganisms 8, no. 11 (November 11, 2020): 1772. http://dx.doi.org/10.3390/microorganisms8111772.
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The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA.
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Mangat, Chand S., Amrita Bharat, Sebastian S. Gehrke, and Eric D. Brown. "Rank Ordering Plate Data Facilitates Data Visualization and Normalization in High-Throughput Screening." Journal of Biomolecular Screening 19, no. 9 (May 14, 2014): 1314–20. http://dx.doi.org/10.1177/1087057114534298.
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High-throughput screening (HTS) of chemical and microbial strain collections is an indispensable tool for modern chemical and systems biology; however, HTS data sets have inherent systematic and random error, which may lead to false-positive or false-negative results. Several methods of normalization of data exist; nevertheless, due to the limitations of each, no single method has been universally adopted. Here, we present a method of data visualization and normalization that is effective, intuitive, and easy to implement in a spreadsheet program. For each plate, the data are ordered by ascending values and a plot thereof yields a curve that is a signature of the plate data. Curve shape characteristics provide intuitive visualization of the frequency and strength of inhibitors, activators, and noise on the plate, allowing potentially problematic plates to be flagged. To reduce plate-to-plate variation, the data can be normalized by the mean of the middle 50% of ordered values, also called the interquartile mean (IQM) or the 50% trimmed mean of the plate. Positional effects due to bias in columns, rows, or wells can be corrected using the interquartile mean of each well position across all plates (IQMW) as a second level of normalization. We illustrate the utility of this method using data sets from biochemical and phenotypic screens.
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Liao, Ben-Yang, and Meng-Pin Weng. "Unraveling the association between mRNA expressions and mutant phenotypes in a genome-wide assessment of mice." Proceedings of the National Academy of Sciences 112, no. 15 (March 30, 2015): 4707–12. http://dx.doi.org/10.1073/pnas.1415046112.
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High-throughput gene expression profiling has revealed substantial leaky and extraneous transcription of eukaryotic genes, challenging the perceptions that transcription is strictly regulated and that changes in transcription have phenotypic consequences. To assess the functional implications of mRNA transcription directly, we analyzed mRNA expression data derived from microarrays, RNA-sequencing, and in situ hybridization, together with phenotype data of mouse mutants as a proxy of gene function at the tissue level. The results indicated that despite the presence of widespread ectopic transcription, mRNA expression and mutant phenotypes of mammalian genes or tissues remain associated. The expression-phenotype association at the gene level was particularly strong for tissue-specific genes, and the association could be underestimated due to data insufficiency and incomprehensive phenotyping of mouse mutants; the strength of expression-phenotype association at the tissue level depended on tissue functions. Mutations on genes expressed at higher levels or expressed at earlier embryonic stages more often result in abnormal phenotypes in the tissues where they are expressed. The mRNA expression profiles that have stronger associations with their phenotype profiles tend to be more evolutionarily conserved, indicating that the evolution of transcriptome and the evolution of phenome are coupled. Therefore, mutations resulting in phenotypic aberrations in expressed tissues are more likely to occur in highly transcribed genes, tissue-specific genes, genes expressed during early embryonic stages, or genes with evolutionarily conserved mRNA expression profiles.
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Nguyen, Giao N., and Sally L. Norton. "Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm." Plants 9, no. 7 (June 29, 2020): 817. http://dx.doi.org/10.3390/plants9070817.
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Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.
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Haselimashhadi, Hamed, Jeremy C. Mason, Ann-Marie Mallon, Damian Smedley, Terrence F. Meehan, and Helen Parkinson. "OpenStats: A robust and scalable software package for reproducible analysis of high-throughput phenotypic data." PLOS ONE 15, no. 12 (December 30, 2020): e0242933. http://dx.doi.org/10.1371/journal.pone.0242933.
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Reproducibility in the statistical analyses of data from high-throughput phenotyping screens requires a robust and reliable analysis foundation that allows modelling of different possible statistical scenarios. Regular challenges are scalability and extensibility of the analysis software. In this manuscript, we describe OpenStats, a freely available software package that addresses these challenges. We show the performance of the software in a high-throughput phenomic pipeline in the International Mouse Phenotyping Consortium (IMPC) and compare the agreement of the results with the most similar implementation in the literature. OpenStats has significant improvements in speed and scalability compared to existing software packages including a 13-fold improvement in computational time to the current production analysis pipeline in the IMPC. Reduced complexity also promotes FAIR data analysis by providing transparency and benefiting other groups in reproducing and re-usability of the statistical methods and results. OpenStats is freely available under a Creative Commons license at www.bioconductor.org/packages/OpenStats.
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Byrne-Steele, Miranda, Wenjing Pan, Brittany E. Brown, Xiaohong Hou, Mollye Depinet, Mary Eisenhower, Daniel Weber, and Jian Han. "A novel method for high throughput TCR single cell VDJ-pairing with phenotypic analysis." Journal of Immunology 202, no. 1_Supplement (May 1, 2019): 131.6. http://dx.doi.org/10.4049/jimmunol.202.supp.131.6.
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Abstract Single cell transcriptional analysis reveals a wealth of information that is obscured when analyzing the RNA of a sample en masse. Furthermore, it is important to know the identity of the TCR chain VDJ-rearrangement associated with the phenotypic state. This information can provide direct calculations of clonal frequency in various cell subsets, tracking of specific lymphocytes with treatment, and reveal paired information for both chains of the receptor for downstream Car-T development. Here, we discuss a novel method that identifies paired VDJ-rearrangements for both TCR alpha and beta chains from single cells barcoded via the BD Rhapsody Express single cell system. The phenotype of the cell is obtained using the BD Rhapsody RNA-seq kit, while the receptor information is amplified from the same cDNA using an iRepertoire-derived method incorporating a multiplex mix of primers associated with both the TCR alpha and beta loci. This targeted-seq strategy requires less read depth when compared to template-switch approaches. In one experiment, 3,000 TCR-beta unique CDR3 (uCDR3) were identified from ~7,000 barcoded cells using 1.5 million NGS reads with a 0.83 Pearson correlation upon repeat. Approximately 1,000 TCR alpha-beta paired uCDR3 were also called from the data. Additionally, the BD phenotyping results independently verify the immune repertoire data since the TCR beta chain constant gene overlays with the TCR-beta uCDR3 clonotypes for CD4+ and CD8+ subsets but is largely absent in the CD14+ monocyte population for both analyses. Thus, the developed technology provides the ability to simultaneously pair both alpha and beta chains for thousands of T-cells while obtaining information related to the functional status of each cell.
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Young, Joanne, Yoran Margaron, Mathieu Fernandes, Eve Duchemin-Pelletier, Joris Michaud, Mélanie Flaender, Oana Lorintiu, Sébastien Degot, and Pauline Poydenot. "MyoScreen, a High-Throughput Phenotypic Screening Platform Enabling Muscle Drug Discovery." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 8 (March 2, 2018): 790–806. http://dx.doi.org/10.1177/2472555218761102.
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Despite the need for more effective drug treatments to address muscle atrophy and disease, physiologically accurate in vitro screening models and higher information content preclinical assays that aid in the discovery and development of novel therapies are lacking. To this end, MyoScreen was developed: a robust and versatile high-throughput high-content screening (HT/HCS) platform that integrates a physiologically and pharmacologically relevant micropatterned human primary skeletal muscle model with a panel of pertinent phenotypic and functional assays. MyoScreen myotubes form aligned, striated myofibers, and they show nerve-independent accumulation of acetylcholine receptors (AChRs), excitation–contraction coupling (ECC) properties characteristic of adult skeletal muscle and contraction in response to chemical stimulation. Reproducibility and sensitivity of the fully automated MyoScreen platform are highlighted in assays that quantitatively measure myogenesis, hypertrophy and atrophy, AChR clusterization, and intracellular calcium release dynamics, as well as integrating contractility data. A primary screen of 2560 compounds to identify stimulators of myofiber regeneration and repair, followed by further biological characterization of two hits, validates MyoScreen for the discovery and testing of novel therapeutics. MyoScreen is an improvement of current in vitro muscle models, enabling a more predictive screening strategy for preclinical selection of the most efficacious new chemical entities earlier in the discovery pipeline process.
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Harrill, J., L. Everett, J. Nyffeler, C. Willis, R. Brockway, K. P. Freidman, I. Shah, and R. Judson. "Strategic Use of High-Throughput Transcriptomics and Phenotypic Profiling Data in Support of Regulatory Decisions." Toxicology Letters 350 (September 2021): S46. http://dx.doi.org/10.1016/s0378-4274(21)00358-1.
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Morgan, Hugh, Tim Beck, Andrew Blake, Hilary Gates, Niels Adams, Guillaume Debouzy, Sophie Leblanc, et al. "EuroPhenome: a repository for high-throughput mouse phenotyping data." Nucleic Acids Research 38, suppl_1 (November 23, 2009): D577—D585. http://dx.doi.org/10.1093/nar/gkp1007.
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Banerjee, Bikram Pratap, German Spangenberg, and Surya Kant. "CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements." Biosensors 12, no. 1 (December 29, 2021): 16. http://dx.doi.org/10.3390/bios12010016.
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The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.
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Liao, Lihua, Lin Cao, Yaojian Xie, Jianzhong Luo, and Guibin Wang. "Phenotypic Traits Extraction and Genetic Characteristics Assessment of Eucalyptus Trials Based on UAV-Borne LiDAR and RGB Images." Remote Sensing 14, no. 3 (February 7, 2022): 765. http://dx.doi.org/10.3390/rs14030765.
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Phenotype describes the physical, physiological and biochemical characteristics of organisms that are determined or influenced by genes and environment. Accurate extraction of phenotypic data is a prerequisite for comprehensive forest phenotyping in order to improve the growth and development of forest plantations. Combined with the assessments of genetic characteristics, forest phenotyping will help to accelerate the breeding process, improve stress resistance and enhance the quality of the planted forest. In this study, we disposed our study in Eucalyptus trials within the Gaofeng forest farm (a typical Eucalyptus plantation site in southern China) for a high-throughput phenotypic traits extraction and genetic characteristics analysis based on high-density point clouds (acquired by a UAV-borne LiDAR sensor) and high-resolution RGB images (acquired by a UAV-borne camera), aiming at developing a high-resolution and high-throughput UAV-based phenotyping approach for tree breeding. First, we compared the effect of CHM-based Marker-Controlled Watershed Segmentation (MWS) and Point Cloud-based Cluster Segmentation (PCS) for extracting individual trees; Then, the phenotypic traits (i.e., tree height, diameter at breast height, crown width), the structural metrics (n = 19) and spectral indices (n = 9) of individual trees were extracted and assessed; Finally, a genetic characteristics analysis was carried out based on the above results, and we compared the differences between high-throughput phenotyping by UAV-based data and on manual measurements. Results showed that: in the relatively low stem density site of the trial (760 n/ha), the overall accuracy of MWS and PCS was similar, while in the higher stem density sites (982 n/ha, 1239 n/ha), the overall accuracy of MWS (F(2) = 0.93, F(3) = 0.86) was higher than PCS (F(2) = 0.84, F(3) = 0.74); With the increase of stem density, the difference between the overall accuracy of MWS and PCS gradually expanded. Both UAV–LiDAR extracted phenotypic traits and manual measurements were significantly different across the Eucalyptus clones (P < 0.05), as were most of the structural metrics (47/57) and spectral indices (26/27), revealing the genetic divergence between the clones. The rank of clones demonstrated that the pure clones (of E. urophylla), the hybrid clones (of E. urophylla as the female parent) and the hybrid clones (of E. wetarensis and E. grandis) have a higher fineness of growth. This study proved that UAV-based fine-resolution remote sensing could be an efficient, accurate and precise technology in phenotyping (used in genetic analysis) for tree breeding.
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Lesire, Laetitia, Ludovic Chaput, Paulina Cruz De Casas, Fanny Rousseau, Catherine Piveteau, Julie Dumont, David Pointu, Benoît Déprez, and Florence Leroux. "High-Throughput Image-Based Aggresome Quantification." SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, no. 7 (May 25, 2020): 783–91. http://dx.doi.org/10.1177/2472555220919708.
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Aggresomes are subcellular perinuclear structures where misfolded proteins accumulate by retrograde transport on microtubules. Different methods are available to monitor aggresome formation, but they are often laborious, time-consuming, and not quantitative. Proteostat is a red fluorescent molecular rotor dye, which becomes brightly fluorescent when it binds to protein aggregates. As this reagent was previously validated to detect aggresomes, we have miniaturized its use in 384-well plates and developed a method for high-throughput imaging and quantification of aggresomes. Two different image analysis methods, including one with machine learning, were evaluated. They lead to similar robust data to quantify cells having aggresome, with satisfactory Z′ factor values and reproducible EC50 values for compounds known to induce aggresome formation, like proteasome inhibitors. We demonstrated the relevance of this phenotypic assay by screening a chemical library of 1280 compounds to find aggresome modulators. We obtained hits that present similarities in their structural and physicochemical properties. Interestingly, some of them were previously described to modulate autophagy, which could explain their effect on aggresome structures. In summary, we have optimized and validated the Proteostat detection reagent to easily measure aggresome formation in a miniaturized, automated, quantitative, and high-content assay. This assay can be used at low, middle, or high throughput to quantify changes in aggresome formation that could help in the understanding of chemical compound activity in pathologies such as protein misfolding disorders or cancer.
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Haselimashhadi, Hamed, Jeremy C. Mason, Violeta Munoz-Fuentes, Federico López-Gómez, Kolawole Babalola, Elif F. Acar, Vivek Kumar, et al. "Soft windowing application to improve analysis of high-throughput phenotyping data." Bioinformatics 36, no. 5 (October 8, 2019): 1492–500. http://dx.doi.org/10.1093/bioinformatics/btz744.
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Abstract Motivation High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. Results Here we introduce ‘soft windowing’, a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype–phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. Availability and implementation The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. Supplementary information Supplementary data are available at Bioinformatics online.
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Perlo, Virginie, Agnelo Furtado, Frikkie Botha, and Robert Henry. "Analysis of Differences in Gene Expression Associated with Variation in Biomass Composition in Sugarcane." Proceedings 36, no. 1 (April 7, 2020): 164. http://dx.doi.org/10.3390/proceedings2019036164.
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Sugarcane has a high potential to support second-generation ethanol production and environmentally friendly by-products for use in chemical, pharmaceutical, medical, cosmetic and food industries. A crucial challenge for a long-term economic viability is to optimise the crop for production of a biomass composition that will ensure maximum economic benefit. Transcriptome data analysis provides a relevant explanation of phenotypic variances and gives a more accurate prediction of phenotypes than genomic information. This multi-omic approach, with an integrated transcriptomics and metabolomics analysis may reveal details of biological mechanisms and pathways. A global view of transcriptional regulation and the identification differentially expressed genes (DEGs) and metabolites may help the feasibility of tailoring engineering targeted biosynthetic pathways to improve the production of these bio-products from sugarcane. We propose a profiling analysis workflow (pipeline) to generate empirical correlations between gene expression, metabolites, proteins and phenotypic traits and pathway analysis, with a highlight focus on data visualisation. This study of genetic variation in gene expression and correlations with metabolic and protein phenotype relies on high-throughput methodology, measurement and analysis of 360 samples, 24 commercial sugarcane cultivars with different phenotypic characteristics at 5 different development stages with 3 replicates.
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Onnela, Jukka-Pekka, Caleb Dixon, Keary Griffin, Tucker Jaenicke, Leila Minowada, Sean Esterkin, Alvin Siu, Josh Zagorsky, and Eli Jones. "Beiwe: A data collection platform for high-throughput digital phenotyping." Journal of Open Source Software 6, no. 68 (December 15, 2021): 3417. http://dx.doi.org/10.21105/joss.03417.
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Issac, Amanda, Himani Yadav, Glen Rains, and Javad Mohammadpour Velni. "Dimensionality Reduction of High-throughput Phenotyping Data in Cotton Fields." IFAC-PapersOnLine 55, no. 32 (2022): 153–58. http://dx.doi.org/10.1016/j.ifacol.2022.11.131.
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Silva, Marina F. e., Gabriel M. Maciel, Rodrigo BA Gallis, Ricardo Luís Barbosa, Vinicius Q. Carneiro, Wender S. Rezende, and Ana Carolina S. Siquieroli. "High-throughput phenotyping by RGB and multispectral imaging analysis of genotypes in sweet corn." Horticultura Brasileira 40, no. 1 (January 2022): 92–98. http://dx.doi.org/10.1590/s0102-0536-2022012.
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ABSTRACT Sweet corn (Zea mays subsp. saccharata) is mainly intended for industrial processing. Optimizing time and costs during plant breeding is fundamental. An alternative is the use of high-throughput phenotyping (HTP) indirect associated with agronomic traits and chlorophyll contents. This study aimed to (i) verify whether HTP by digital images is useful for screening sweet corn genotypes and (ii) investigate the correlations between the traits evaluated by conventional methods and those obtained from images. Ten traits were evaluated in seven S3 populations of sweet corn and in two commercial hybrids, three traits by classical phenotyping and the others by HTP based on RGB (red, green, blue) and multispectral imaging analysis. The data were submitted to the analyses of variance and Scott-Knott test. In addition, a phenotypic correlation graph was plotted. The hybrids were more productive than the S3 populations, showing an efficient evaluation. The traits extracted using HTP and classical phenotyping showed a high degree of association. HTP was efficient in identifying sweet corn genotypes with higher and lower yield. The vegetative canopy area (VCA), normalized difference vegetation index (NDVI), and visible atmospherically resistant index (VARI) indices were strongly associated with grain yield.
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Hearn, Jessica M., George M. Hughes, Isolda Romero-Canelón, Alison F. Munro, Belén Rubio-Ruiz, Zhe Liu, Neil O. Carragher, and Peter J. Sadler. "Pharmaco-genomic investigations of organo-iridium anticancer complexes reveal novel mechanism of action." Metallomics 10, no. 1 (2018): 93–107. http://dx.doi.org/10.1039/c7mt00242d.
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Gruber, Franz S., Zoe C. Johnston, Neil R. Norcross, Irene Georgiou, Caroline Wilson, Kevin D. Read, Ian H. Gilbert, Jason R. Swedlow, Sarah Martins da Silva, and Christopher L. R. Barratt. "Compounds enhancing human sperm motility identified using a high-throughput phenotypic screening platform." Human Reproduction 37, no. 3 (January 20, 2022): 466–75. http://dx.doi.org/10.1093/humrep/deac007.
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Abstract STUDY QUESTION Can a high-throughput screening (HTS) platform facilitate male fertility drug discovery? SUMMARY ANSWER An HTS platform identified a large number of compounds that enhanced sperm motility. WHAT IS KNOWN ALREADY Several efforts to find small molecules modulating sperm function have been performed but none have used high-throughput technology. STUDY DESIGN, SIZE, DURATION Healthy donor semen samples were used and samples were pooled (3–5 donors per pool). Primary screening was performed singly; dose–response screening was performed in duplicate (using independent donor pools). PARTICIPANTS/MATERIALS, SETTING, METHODS Spermatozoa isolated from healthy donors were prepared by density gradient centrifugation and incubated in 384-well plates with compounds (6.25 μM) to identify those compounds with enhancing effects on motility. Approximately 17 000 compounds from the libraries, ReFRAME, Prestwick, Tocris, LOPAC, CLOUD and MMV Pathogen Box, were screened. Dose–response experiments of screening hits were performed to confirm the enhancing effect on sperm motility. Experiments were performed in a university setting. MAIN RESULTS AND THE ROLE OF CHANCE From our primary single concentration screening, 105 compounds elicited an enhancing effect on sperm motility compared to dimethylsulphoxide-treated wells. Confirmed enhancing compounds were grouped based on their annotated targets/target classes. A major target class, phosphodiesterase inhibitors, were identified, in particular PDE10A inhibitors as well as number of compounds not previously known to enhance human sperm motility, such as those related to GABA signalling. LARGE SCALE DATA N/A. LIMITATIONS, REASONS FOR CAUTION Although this approach provides data about the activity of the compound, it is only a starting point. For example, further substantive experiments are necessary to provide a more comprehensive picture of each compound’s activity, the effect on the kinetics of the cell populations and subpopulations, and their potential mechanisms of action. Compounds have been tested with prepared donor spermatozoa, incubated under non-capacitating conditions, and only incubated with compounds for a relatively short period of time. Therefore, the effect of compounds under different conditions, for example in whole semen, for longer incubation times, or using samples from patient groups, may be different and require further study. All experiments were performed in vitro. WIDER IMPLICATIONS OF THE FINDINGS This phenotypic screening assay identified a large number of compounds that increased sperm motility. In addition to furthering our understanding of human sperm function, for example identifying new avenues for discovery, we highlight potential compounds as promising start-point for a medicinal chemistry programme for potential enhancement of male fertility. Moreover, with disclosure of the results of screening, we present a substantial resource to inform further work in the field. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the Bill and Melinda Gates Foundation, Scottish Funding Council and Scottish Universities Life Science Alliance. C.L.R.B. is Editor for RBMO. C.L.R.B. receives funding from Chief Scientists Office (Scotland), ESHRE and Genus PLC, consulting fees from Exscientia and lecture fees from Cooper Surgical and Ferring. S.M.d.S. is an Associate Editor of Human Reproduction, and an Associate Editor of Reproduction and Fertility. S.M.d.S. receives funding from Cooper Surgical and British Dietetic Society. No other authors declared a COI.
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Peters, Luanne L., Eleanor M. Cheever, Heather R. Ellis, Phyllis A. Magnani, Karen L. Svenson, Randy Von Smith, and Molly A. Bogue. "Large-scale, high-throughput screening for coagulation and hematologic phenotypes in mice*." Physiological Genomics 11, no. 3 (December 3, 2002): 185–93. http://dx.doi.org/10.1152/physiolgenomics.00077.2002.
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The Mouse Phenome Project is an international effort to systematically gather phenotypic data for a defined set of inbred mouse strains. For such large-scale projects the development of high-throughput screening protocols that allow multiple tests to be performed on a single mouse is essential. Here we report hematologic and coagulation data for more than 30 inbred strains. Complete blood counts were performed using an Advia 120 analyzer. For coagulation testing, we successfully adapted the Dade Behring BCS automated coagulation analyzer for use in mice by lowering sample and reagent volume requirements. Seven automated assay procedures were developed. Small sample volume requirements make it possible to perform multiple tests on a single animal without euthanasia, while reductions in reagent volume requirements reduce costs. The data show that considerable variation in many basic hematological and coagulation parameters exists among the inbred strains. These data, freely available on the World Wide Web, allow investigators to knowledgeably select the most appropriate strain(s) to meet their individual study designs and goals.
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Li, Yinglun, Weiliang Wen, Xinyu Guo, Zetao Yu, Shenghao Gu, Haipeng Yan, and Chunjiang Zhao. "High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network." PLOS ONE 16, no. 1 (January 12, 2021): e0241528. http://dx.doi.org/10.1371/journal.pone.0241528.
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Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or tractor-based high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R2 = 0.96–0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.
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Smith, Cynthia L., and Janan T. Eppig. "The Mammalian Phenotype Ontology as a unifying standard for experimental and high-throughput phenotyping data." Mammalian Genome 23, no. 9-10 (September 9, 2012): 653–68. http://dx.doi.org/10.1007/s00335-012-9421-3.
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Lee, Kristin, Jasper Munro, Ricardo V. Ventura, Flavio S. Schenkel, Gordon Vander Voort, and Angela Cánovas4. "PSX-A-8 Updating Pre-Existing Genetic Evaluations System to Evaluate High-Throughput Data in Purebred and Crossbred Beef Cattle." Journal of Animal Science 100, Supplement_3 (September 21, 2022): 282. http://dx.doi.org/10.1093/jas/skac247.513.
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Abstract High-throughput technologies are available to aid producers in efficiently and sustainably raising many animals, including automated sensory technologies for phenotype measurement and genomic data. However, uptake of genetic selection by the beef cattle sector has been limited by segmentation of the industry. Therefore, easy to use genetic evaluations systems (GES) that can convert ‘big data’ into real-time and comprehensive results are needed, so that beef cattle producers can utilize the technologies that are becoming available to make accurate breeding decisions. The purpose of this study is to update a pre-existing purebred and crossbred beef cattle GES to become both flexible and efficient in its ability to evaluate high-throughput phenotypic data, and to assess the feasibility of including genotypes in a single-step genetic evaluation procedure (ssGBLUP). Firstly, computational operations required for the calculation of breeding values will be quantified and evaluated. A purebred and crossbred reference population will be assembled using data from Canadian beef breed associations purebred (n = 186,928) and commercial animals (n = 14,406). Multiple breeds will be considered (Angus, Charolais, Hereford, and Simmental), and multiple phenotypes analyzed (birth weight, weaning weight, yearling weight, and calving ease). Key component matrix operations with and without the use of Python Libraries (NumPy) will be evaluated. Computational performance of the different strategies will be compared, including CPU time, and memory allocation. Subsequently, a simulated ssGBLUP will be conducted using a population which mimics that observed in the preliminary analysis. Computational costs associated with the implementation of ssGBLUP will be compared to the existing GES. The results of this study will be directly applied to provide beef producers with the tools to improve the genetics of their own herds. This will facilitate the uptake of technology by the industry, thus increasing the economic value and sustainability of beef production.
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Howarth, Alison, Martin Schröder, Raquel C. Montenegro, David H. Drewry, Heba Sailem, Val Millar, Susanne Müller, and Daniel V. Ebner. "HighVia—A Flexible Live-Cell High-Content Screening Pipeline to Assess Cellular Toxicity." SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, no. 7 (May 27, 2020): 801–11. http://dx.doi.org/10.1177/2472555220923979.
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High-content screening to monitor disease-modifying phenotypes upon small-molecule addition has become an essential component of many drug and target discovery platforms. One of the most common phenotypic approaches, especially in the field of oncology research, is the assessment of cell viability. However, frequently used viability readouts employing metabolic proxy assays based on homogeneous colorimetric/fluorescent reagents are one-dimensional, provide limited information, and can in many cases yield conflicting or difficult-to-interpret results, leading to misinterpretation of data and wasted resources.The resurgence of high-content, phenotypic screening has significantly improved the quality and breadth of cell viability data, which can be obtained at the very earliest stages of drug and target discovery. Here, we describe a relatively inexpensive, high-throughput, high-content, multiparametric, fluorescent imaging protocol using a live-cell method of three fluorescent probes (Hoechst, Yo-Pro-3, and annexin V), that is amenable to the addition of further fluorophores. The protocol enables the accurate description and profiling of multiple cell death mechanisms, including apoptosis and necrosis, as well as accurate determination of compound IC50, and has been validated on a range of high-content imagers and image analysis software. To validate the protocol, we have used a small library of approximately 200 narrow-spectrum kinase inhibitors and clinically approved drugs. This fully developed, easy-to-use pipeline has subsequently been implemented in several academic screening facilities, yielding fast, flexible, and rich cell viability data for a range of early-stage high-throughput drug and target discovery programs.
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Hou, Shurong, Hervé Tiriac, Banu Priya Sridharan, Louis Scampavia, Franck Madoux, Jan Seldin, Glauco R. Souza, Donald Watson, David Tuveson, and Timothy P. Spicer. "Advanced Development of Primary Pancreatic Organoid Tumor Models for High-Throughput Phenotypic Drug Screening." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 6 (April 19, 2018): 574–84. http://dx.doi.org/10.1177/2472555218766842.
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Traditional high-throughput drug screening in oncology routinely relies on two-dimensional (2D) cell models, which inadequately recapitulate the physiologic context of cancer. Three-dimensional (3D) cell models are thought to better mimic the complexity of in vivo tumors. Numerous methods to culture 3D organoids have been described, but most are nonhomogeneous and expensive, and hence impractical for high-throughput screening (HTS) purposes. Here we describe an HTS-compatible method that enables the consistent production of organoids in standard flat-bottom 384- and 1536-well plates by combining the use of a cell-repellent surface with a bioprinting technology incorporating magnetic force. We validated this homogeneous process by evaluating the effects of well-characterized anticancer agents against four patient-derived pancreatic cancer KRAS mutant-associated primary cells, including cancer-associated fibroblasts. This technology was tested for its compatibility with HTS automation by completing a cytotoxicity pilot screen of ~3300 approved drugs. To highlight the benefits of the 3D format, we performed this pilot screen in parallel in both the 2D and 3D assays. These data indicate that this technique can be readily applied to support large-scale drug screening relying on clinically relevant, ex vivo 3D tumor models directly harvested from patients, an important milestone toward personalized medicine.
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BAEK, JeongHo, Eungyeong Lee, Nyunhee Kim, Song Lim Kim, Inchan Choi, Hyeonso Ji, Yong Suk Chung, Man-Soo Choi, Jung-Kyung Moon, and Kyung-Hwan Kim. "High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis." Sensors 20, no. 1 (January 1, 2020): 248. http://dx.doi.org/10.3390/s20010248.
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Data phenotyping traits on soybean seeds such as shape and color has been obscure because it is difficult to define them clearly. Further, it takes too much time and effort to have sufficient number of samplings especially length and width. These difficulties prevented seed morphology to be incorporated into efficient breeding program. Here, we propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis. As results, quantitative values for colors and various types of morphological traits could be screened to create a standard for subsequent evaluation of the genotype. Phenotyping method in the current study could define the morphology and color of soybean seeds in highly accurate and reliable manner. Further, this method enables the measurement and analysis of large amounts of plant seed phenotype data in a short time, which was not possible before. Fast and precise phenotype data obtained here may facilitate Genome Wide Association Study for the gene function analysis as well as for development of the elite varieties having desirable seed traits.
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Guo, Shangjing, Guoliang Zhou, Jinglu Wang, Xianju Lu, Huan Zhao, Minggang Zhang, Xinyu Guo, and Ying Zhang. "High-Throughput Phenotyping Accelerates the Dissection of the Phenotypic Variation and Genetic Architecture of Shank Vascular Bundles in Maize (Zea mays L.)." Plants 11, no. 10 (May 18, 2022): 1339. http://dx.doi.org/10.3390/plants11101339.
Full textAbstract:
The vascular bundle of the shank is an important ‘flow’ organ for transforming maize biological yield to grain yield, and its microscopic phenotypic characteristics and genetic analysis are of great significance for promoting the breeding of new varieties with high yield and good quality. In this study, shank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm. Moreover, five categories and 36 phenotypic traits of the shank including related to the cross-section, epidermis zone, periphery zone, inner zone and vascular bundle were analyzed through an automatic CT image process pipeline based on the functional zones. Next, we analyzed the phenotypic variations in vascular bundles at the base of the shank among a group of 202 inbred lines based on comprehensive phenotypic information for two environments. It was found that the number of vascular bundles in the inner zone (IZ_VB_N) and the area of the inner zone (IZ_A) varied the most among the different subgroups. Combined with genome-wide association studies (GWAS), 806 significant single nucleotide polymorphisms (SNPs) were identified, and 1245 unique candidate genes for 30 key traits were detected, including the total area of vascular bundles (VB_A), the total number of vascular bundles (VB_N), the density of the vascular bundles (VB_D), etc. These candidate genes encode proteins involved in lignin, cellulose synthesis, transcription factors, material transportation and plant development. The results presented here will improve the understanding of the phenotypic traits of maize shank and provide an important phenotypic basis for high-throughput identification of vascular bundle functional genes of maize shank and promoting the breeding of new varieties with high yield and good quality.
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Shields, Denis C., and Aisling M. O'Halloran. "Integrating Genotypic Data with Transcriptomic and Proteomic Data." Comparative and Functional Genomics 3, no. 1 (2002): 22–27. http://dx.doi.org/10.1002/cfg.135.
Full textAbstract:
Historically genotypic variation has been detected at the phenotypic level, at the metabolic level, and at the protein chemistry level. Advances in technology have allowed its direct visualisation at the level of DNA variation. Nevertheless, there is still an enormous interest in phenotypic, metabolic and protein property variability, since such variation gives insights into potential functionally important differences conferred by genetic variation. High-throughput transcriptomics and proteomics applied to different individuals drawn from a population has the potential to identify the functional consequences of genetic variability, in terms of either differences in expression of mRNA or in terms of differences in the quantities, pI(s) or molecular weight(s) of an expressed protein. Family studies can define the genetic component of such variation (segregation analysis) and with the genotyping of well-spaced markers can map the causative factors to broad chromosomal regions (linkage analysis). Association studies in the variant proteins have the greatest power to confirm the presence ofcis-acting genetic variants. The most powerful study designs may combine elements of both family and association studies applied to proteomic and transcriptomic analyses. Such studies may provide appreciable advances in our understanding of the genetic aetiology of complex disorders.
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Ding, Mei, Roger Clark, Catherine Bardelle, Anna Backmark, Tyrrell Norris, Wendy Williams, Mark Wigglesworth, and Rob Howes. "Application of High-Throughput Flow Cytometry in Early Drug Discovery: An AstraZeneca Perspective." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 7 (May 22, 2018): 719–31. http://dx.doi.org/10.1177/2472555218775074.
Full textAbstract:
Flow cytometry is a powerful tool providing multiparametric analysis of single cells or particles. The introduction of faster plate-based sampling technologies on flow cytometers has transformed the technology into one that has become attractive for higher throughput drug discovery screening. This article describes AstraZeneca’s perspectives on the deployment and application of high-throughput flow cytometry (HTFC) platforms for small-molecule high-throughput screening (HTS), structure–activity relationship (SAR) and phenotypic screening, and antibody screening. We describe the overarching HTFC workflow, including the associated automation and data analysis, along with a high-level overview of our HTFC assay portfolio. We go on to discuss the practical challenges encountered and solutions adopted in the course of our deployment of HTFC, as well as future enhancements and expansion of the technology to new areas of drug discovery.
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Ma, Xiaodan, Kexin Zhu, Haiou Guan, Jiarui Feng, Song Yu, and Gang Liu. "High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform." Remote Sensing 11, no. 9 (May 7, 2019): 1085. http://dx.doi.org/10.3390/rs11091085.
Full textAbstract:
Canopy color and structure can strongly reflect plant functions. Color characteristics and plant height as well as canopy breadth are important aspects of the canopy phenotype of soybean plants. High-throughput phenotyping systems with imaging capabilities providing color and depth information can rapidly acquire data of soybean plants, making it possible to quantify and monitor soybean canopy development. The goal of this study was to develop a 3D imaging approach to quantitatively analyze soybean canopy development under natural light conditions. Thus, a Kinect sensor-based high-throughput phenotyping (HTP) platform was developed for soybean plant phenotyping. To calculate color traits accurately, the distortion phenomenon of color images was first registered in accordance with the principle of three primary colors and color constancy. Then, the registered color images were applied to depth images for the reconstruction of the colorized three-dimensional canopy structure. Furthermore, the 3D point cloud of soybean canopies was extracted from the background according to adjusted threshold, and each area of individual potted soybean plants in the depth images was segmented for the calculation of phenotypic traits. Finally, color indices, plant height and canopy breadth were assessed based on 3D point cloud of soybean canopies. The results showed that the maximum error of registration for the R, G, and B bands in the dataset was 1.26%, 1.09%, and 0.75%, respectively. Correlation analysis between the sensors and manual measurements yielded R2 values of 0.99, 0.89, and 0.89 for plant height, canopy breadth in the west-east (W–E) direction, and canopy breadth in the north-south (N–S) direction, and R2 values of 0.82, 0.79, and 0.80 for color indices h, s, and i, respectively. Given these results, the proposed approaches provide new opportunities for the identification of the quantitative traits that control canopy structure in genetic/genomic studies or for soybean yield prediction in breeding programs.