Academic literature on the topic 'High Throughput Phenotypic Data'
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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.
Dissertations / Theses on the topic "High Throughput Phenotypic Data"
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Yu, Haipeng. "Designing and modeling high-throughput phenotyping data in quantitative genetics." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97579.
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Quantitative genetics aims to bridge the genome to phenome gap. The advent of high-throughput genotyping technologies has accelerated the progress of genome to phenome mapping, but a challenge remains in phenotyping. Various high-throughput phenotyping (HTP) platforms have been developed recently to obtain economically important phenotypes in an automated fashion with less human labor and reduced costs. However, the effective way of designing HTP has not been investigated thoroughly. In addition, high-dimensional HTP data bring up a big challenge for statistical analysis by increasing computational demands. A new strategy for modeling high-dimensional HTP data and elucidating the interrelationships among these phenotypes are needed. Previous studies used pedigree-based connectetdness statistics to study the design of phenotyping. The availability of genetic markers provides a new opportunity to evaluate connectedness based on genomic data, which can serve as a means to design HTP. This dissertation first discusses the utility of connectedness spanning in three studies. In the first study, I introduced genomic connectedness and compared it with traditional pedigree-based connectedness. The relationship between genomic connectedness and prediction accuracy based on cross-validation was investigated in the second study. The third study introduced a user-friendly connectedness R package, which provides a suite of functions to evaluate the extent of connectedness. In the last study, I proposed a new statistical approach to model high-dimensional HTP data by leveraging the combination of confirmatory factor analysis and Bayesian network. Collectively, the results from the first three studies suggested the potential usefulness of applying genomic connectedness to design HTP. The statistical approach I introduced in the last study provides a new avenue to model high-dimensional HTP data holistically to further help us understand the interrelationships among phenotypes derived from HTP.Doctor of Philosophy
Quantitative genetics aims to bridge the genome to phenome gap. With the advent of genotyping technologies, the genomic information of individuals can be included in a quantitative genetic model. A new challenge is to obtain sufficient and accurate phenotypes in an automated fashion with less human labor and reduced costs. The high-throughput phenotyping (HTP) technologies have emerged recently, opening a new opportunity to address this challenge. However, there is a paucity of research in phenotyping design and modeling high-dimensional HTP data. The main themes of this dissertation are 1) genomic connectedness that could potentially be used as a means to design a phenotyping experiment and 2) a novel statistical approach that aims to handle high-dimensional HTP data. In the first three studies, I first compared genomic connectedness with pedigree-based connectedness. This was followed by investigating the relationship between genomic connectedness and prediction accuracy derived from cross-validation. Additionally, I developed a connectedness R package that implements a variety of connectedness measures. The fourth study investigated a novel statistical approach by leveraging the combination of dimension reduction and graphical models to understand the interrelationships among high-dimensional HTP data.
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Manrique, Tito. "Functional linear regression models : application to high-throughput plant phenotyping functional data." Thesis, Montpellier, 2016. http://www.theses.fr/2016MONTT264/document.
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L'Analyse des Données Fonctionnelles (ADF) est une branche de la statistique qui est de plus en plus utilisée dans de nombreux domaines scientifiques appliqués tels que l'expérimentation biologique, la finance, la physique, etc. Une raison à cela est l'utilisation des nouvelles technologies de collecte de données qui augmentent le nombre d'observations dans un intervalle de temps.Les jeux de données fonctionnelles sont des échantillons de réalisations de fonctions aléatoires qui sont des fonctions mesurables définies sur un espace de probabilité à valeurs dans un espace fonctionnel de dimension infinie.Parmi les nombreuses questions étudiées par l'ADF, la régression linéaire fonctionnelle est l'une des plus étudiées, aussi bien dans les applications que dans le développement méthodologique.L'objectif de cette thèse est l'étude de modèles de régression linéaire fonctionnels lorsque la covariable X et la réponse Y sont des fonctions aléatoires et les deux dépendent du temps. En particulier, nous abordons la question de l'influence de l'histoire d'une fonction aléatoire X sur la valeur actuelle d'une autre fonction aléatoire Y à un instant donné t.Pour ce faire, nous sommes surtout intéressés par trois modèles: le modèle fonctionnel de concurrence (Functional Concurrent Model: FCCM), le modèle fonctionnel de convolution (Functional Convolution Model: FCVM) et le modèle linéaire fonctionnel historique. En particulier pour le FCVM et FCCM nous avons proposé des estimateurs qui sont consistants, robustes et plus rapides à calculer par rapport à d'autres estimateurs déjà proposés dans la littérature.Notre méthode d'estimation dans le FCCM étend la méthode de régression Ridge développée dans le cas linéaire classique au cadre de données fonctionnelles. Nous avons montré la convergence en probabilité de cet estimateur, obtenu une vitesse de convergence et développé une méthode de choix optimal du paramètre de régularisation.Le FCVM permet d'étudier l'influence de l'histoire de X sur Y d'une manière simple par la convolution. Dans ce cas, nous utilisons la transformée de Fourier continue pour définir un estimateur du coefficient fonctionnel. Cet opérateur transforme le modèle de convolution en un FCCM associé dans le domaine des fréquences. La consistance et la vitesse de convergence de l'estimateur sont obtenues à partir du FCCM.Le FCVM peut être généralisé au modèle linéaire fonctionnel historique, qui est lui-même un cas particulier du modèle linéaire entièrement fonctionnel. Grâce à cela, nous avons utilisé l'estimateur de Karhunen-Loève du noyau historique. La question connexe de l'estimation de l'opérateur de covariance du bruit dans le modèle linéaire entièrement fonctionnel est également traitée. Finalement nous utilisons tous les modèles mentionnés ci-dessus pour étudier l'interaction entre le déficit de pression de vapeur (Vapour Pressure Deficit: VPD) et vitesse d'élongation foliaire (Leaf Elongation Rate: LER) courbes. Ce type de données est obtenu avec phénotypage végétal haut débit. L'étude est bien adaptée aux méthodes de l'ADFFunctional data analysis (FDA) is a statistical branch that is increasingly being used in many applied scientific fields such as biological experimentation, finance, physics, etc. A reason for this is the use of new data collection technologies that increase the number of observations during a time interval.Functional datasets are realization samples of some random functions which are measurable functions defined on some probability space with values in an infinite dimensional functional space.There are many questions that FDA studies, among which functional linear regression is one of the most studied, both in applications and in methodological development.The objective of this thesis is the study of functional linear regression models when both the covariate X and the response Y are random functions and both of them are time-dependent. In particular we want to address the question of how the history of a random function X influences the current value of another random function Y at any given time t.In order to do this we are mainly interested in three models: the functional concurrent model (FCCM), the functional convolution model (FCVM) and the historical functional linear model. In particular for the FCVM and FCCM we have proposed estimators which are consistent, robust and which are faster to compute compared to others already proposed in the literature.Our estimation method in the FCCM extends the Ridge Regression method developed in the classical linear case to the functional data framework. We prove the probability convergence of this estimator, obtain a rate of convergence and develop an optimal selection procedure of theregularization parameter.The FCVM allows to study the influence of the history of X on Y in a simple way through the convolution. In this case we use the continuous Fourier transform operator to define an estimator of the functional coefficient. This operator transforms the convolution model into a FCCM associated in the frequency domain. The consistency and rate of convergence of the estimator are derived from the FCCM.The FCVM can be generalized to the historical functional linear model, which is itself a particular case of the fully functional linear model. Thanks to this we have used the Karhunen–Loève estimator of the historical kernel. The related question about the estimation of the covariance operator of the noise in the fully functional linear model is also treated.Finally we use all the aforementioned models to study the interaction between Vapour Pressure Deficit (VPD) and Leaf Elongation Rate (LER) curves. This kind of data is obtained with high-throughput plant phenotyping platform and is well suited to be studied with FDA methods
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Paszkowski-Rogacz, Maciej. "Integration and analysis of phenotypic data from functional screens." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-63063.
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Motivation: Although various high-throughput technologies provide a lot of valuable information, each of them is giving an insight into different aspects of cellular activity and each has its own limitations. Thus, a complete and systematic understanding of the cellular machinery can be achieved only by a combined analysis of results coming from different approaches. However, methods and tools for integration and analysis of heterogenous biological data still have to be developed.
Results: This work presents systemic analysis of basic cellular processes, i.e. cell viability and cell cycle, as well as embryonic stem cell pluripotency and differentiation. These phenomena were studied using several high-throughput technologies, whose combined results were analysed with existing and novel clustering and hit selection algorithms.
This thesis also introduces two novel data management and data analysis tools. The first, called DSViewer, is a database application designed for integrating and querying results coming from various genome-wide experiments. The second, named PhenoFam, is an application performing gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Both programs are accessible through a web interface.
Conclusions: Eventually, investigations presented in this work provide the research community with novel and markedly improved repertoire of computational tools and methods that facilitate the systematic analysis of accumulated information obtained from high-throughput studies into novel biological insights.
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Xue, Zeyun. "Integration of high-throughput phenotyping and genomics data to explore Arabidopsis natural variation." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASB001.
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L'azote et l'eau sont essentiels à la survie des plantes ainsi qu'au rendement des cultures, mais les mécanismes moléculaires que les plantes mobilisent en réponse à une déficience en azote (N) en eau (W) et à leur combinaison restent en partie à élucider. Les interconnexions entre l'état hydrique des plantes et la disponibilité de l'azote ont attiré beaucoup d'attention. Étant donné leur importance cruciale, il est très important de disséquer le rôle de chaque stress dans le stress combiné. Nous abordons ici la question de l'intégration des réponses aux stress sécheresse et azoté modérés et de la manière dont ils entravent la croissance des rosettes et le métabolisme des plantes. Dans cette thèse, une investigation systématique a été effectuée pour comprendre comment la carence en azote et en eau se conjuguent pour agir sur la croissance de la rosette chez Arabidopsis. Nous avons intégré des données transcriptomiques et métabolomiques pour obtenir une vue globale des interactions entre sécheresse et stress azoté. De plus, 5 accessions divergentes ont été utilisées pour étudier comment les composants génétiques régulent les réponses au stress, en d'autres termes, les interactions GxWxN. L'évaluation de la déficience en eau, en N et de leur combinaison au niveau transcriptome et métabolome a révélé des signatures de réponse au stress communes et spécifiques qui peuvent être conservées principalement à travers les génotypes, bien que de nombreuses autres réponses spécifiques au génotype aient également été découvertes. Les ajustements des transcriptomes et le profil métabolique spécifiques à l'accession reflètent le niveau physiologique de base distinct de chaque fond génétique, comme Col-0 et Tsu-0. Nous avons également trouvé un sous-ensemble de gènes sensibles au stress qui sont responsables du réglage fin de la réponse combinée au stress, tels que les ROXY, TAR4, NRT2.5, GLN1;4. En outre, nous avons intégré les données transcriptomiques et métabolomiques pour construire un réseau de régulation multi-omique. Deux métabolites réagissant au stress hydrique, le Raffinose et le Myoinositol, ont été mis en évidence par une analyse intégrée montrant des schémas de réponse à la carence en N partagés dans 5 accessions. Cette étude fournit une résolution moléculaire de la variation génétique dans les réponses combinées impliquant des interactions entre la carence en N et le stress hydrique et démontre cette plasticité transcriptomique et métabolomique. En outre, une analyse GWA à grande échelle utilisant un set d’accession mondial a été menée pour déchiffrer l'architecture génétique au niveau métabolique afin de rapprocher la compréhension de la plasticité métabolomique et de la diversité phénotypique et d'étendre notre vision de cette diversité à l'échelle des espèces. La comparaison de l'analyse GWA entre populations régionales et mondiale met en lumière la façon dont la structure de la population peut limiter le pouvoir de détection de l'analyse GWANitrogen and water are crucial for plant survival as well as for crop yield, however the molecular mechanisms that plants mobilise to respond to Nitrogen (N) or Water (W) deficiency and their combination still remain partly unknown. The interconnections between water status and N availability have drawn much attention. Given their critical importance, it is of great importance to dissect the role of each stress in the combined stress. We here address the question of how mild drought and nitrogen stress responses are integrated and how they impaired rosette growth and plant metabolism. In this thesis, a systematic investigation was performed to understand how the N deficiency and drought conjugate to shape dynamic rosette growth in Arabidopsis. We integrated transcriptome and metabolomic data to draw a holistic view of drought x N-deficiency interactions. Moreover, as a case study, 5 highly divergent accessions were used to investigate how genetic components regulate stress responses, in other words, GxWxN interactions. Evaluation of drought, N deficiency and combined stress transcriptomes and metabolomes revealed shared and stress-specific response signatures that were conserved primarily across genotypes, although many more genotype-specific responses also were uncovered. The accession-specific transcriptome adjustments and metabolic profile reflected distinct physiological basal status, such as those of Col-0 and Tsu-0. We also found a subset of stress-responsive genes that are responsible for fine-tuning combined stress response, such as ROXYs, TAR4, NRT2.5, GLN1;4. In addition, we integrated transcriptomic and metabolomic data to construct a multi-omics regulatory network. Two drought stress-responsive metabolites, Raffinose and Myoinositol were highlighted by integrative analysis showing shared N-deficiency patterns in 5 accessions. This study provides molecular resolution of genetic variation in combined stress responses involving interactions between N-deficiency and drought stress and illustrates respective transcriptome and metabolome plasticity. Moreover, large-scale GWA analysis using worldwide populations was conducted to decipher the genetic architecture at the metabolic level and provide links between the metabolomic plasticity and phenotypic diversity behind local adaptation. In addition, this extends our vision of the diversity at the species scale. The comparison of GWA analysis based on regional-scale population and species-wide population also sheds light on how population structure can limit the detection power of GWA analysis
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Mack, Jennifer [Verfasser]. "Constraint-based automated reconstruction of grape bunches from 3D range data for high-throughput phenotyping / Jennifer Mack." Bonn : Universitäts- und Landesbibliothek Bonn, 2019. http://d-nb.info/1200020081/34.
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Mervin, Lewis. "Improved in silico methods for target deconvolution in phenotypic screens." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/283004.
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Target-based screening projects for bioactive (orphan) compounds have been shown in many cases to be insufficiently predictive for in vivo efficacy, leading to attrition in clinical trials. Phenotypic screening has hence undergone a renaissance in both academia and in the pharmaceutical industry, partly due to this reason. One key shortcoming of this paradigm shift is that the protein targets modulated need to be elucidated subsequently, which is often a costly and time-consuming procedure. In this work, we have explored both improved methods and real-world case studies of how computational methods can help in target elucidation of phenotypic screens. One limitation of previous methods has been the ability to assess the applicability domain of the models, that is, when the assumptions made by a model are fulfilled and which input chemicals are reliably appropriate for the models. Hence, a major focus of this work was to explore methods for calibration of machine learning algorithms using Platt Scaling, Isotonic Regression Scaling and Venn-Abers Predictors, since the probabilities from well calibrated classifiers can be interpreted at a confidence level and predictions specified at an acceptable error rate. Additionally, many current protocols only offer probabilities for affinity, thus another key area for development was to expand the target prediction models with functional prediction (activation or inhibition). This extra level of annotation is important since the activation or inhibition of a target may positively or negatively impact the phenotypic response in a biological system. Furthermore, many existing methods do not utilize the wealth of bioactivity information held for orthologue species. We therefore also focused on an in-depth analysis of orthologue bioactivity data and its relevance and applicability towards expanding compound and target bioactivity space for predictive studies. The realized protocol was trained with 13,918,879 compound-target pairs and comprises 1,651 targets, which has been made available for public use at GitHub. Consequently, the methodology was applied to aid with the target deconvolution of AstraZeneca phenotypic readouts, in particular for the rationalization of cytotoxicity and cytostaticity in the High-Throughput Screening (HTS) collection. Results from this work highlighted which targets are frequently linked to the cytotoxicity and cytostaticity of chemical structures, and provided insight into which compounds to select or remove from the collection for future screening projects. Overall, this project has furthered the field of in silico target deconvolution, by improving the performance and applicability of current protocols and by rationalizing cytotoxicity, which has been shown to influence attrition in clinical trials.
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Roguski, Łukasz 1987. "High-throughput sequencing data compression." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/565775.
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Thanks to advances in sequencing technologies, biomedical research has experienced a revolution over recent years, resulting in an explosion in the amount of genomic data being generated worldwide. The typical space requirement for storing sequencing data produced by a medium-scale experiment lies in the range of tens to hundreds of gigabytes, with multiple files in different formats being produced by each experiment. The current de facto standard file formats used to represent genomic data are text-based. For practical reasons, these are stored in compressed form. In most cases, such storage methods rely on general-purpose text compressors, such as gzip. Unfortunately, however, these methods are unable to exploit the information models specific to sequencing data, and as a result they usually provide limited functionality and insufficient savings in storage space. This explains why relatively basic operations such as processing, storage, and transfer of genomic data have become a typical bottleneck of current analysis setups.
Therefore, this thesis focuses on methods to efficiently store and compress the data generated from sequencing experiments. First, we propose a novel general purpose FASTQ files compressor. Compared to gzip, it achieves a significant reduction in the size of the resulting archive, while also offering high data processing speed. Next, we present compression methods that exploit the high sequence redundancy present in sequencing data. These methods achieve the best compression ratio among current state-of-the-art FASTQ compressors, without using any external reference sequence. We also demonstrate different lossy compression approaches to store auxiliary sequencing data, which allow for further reductions in size. Finally, we propose a flexible framework and data format, which allows one to semi-automatically generate compression solutions which are not tied to any specific genomic file format. To facilitate data management needed by complex pipelines, multiple genomic datasets having heterogeneous formats can be stored together in configurable containers, with an option to perform custom queries over the stored data. Moreover, we show that simple solutions based on our framework can achieve results comparable to those of state-of-the-art format-specific compressors.
Overall, the solutions developed and described in this thesis can easily be incorporated into current pipelines for the analysis of genomic data. Taken together, they provide grounds for the development of integrated approaches towards efficient storage and management of such data.Gràcies als avenços en el camp de les tecnologies de seqüenciació, en els darrers anys la recerca biomèdica ha viscut una revolució, que ha tingut com un dels resultats l'explosió del volum de dades genòmiques generades arreu del món. La mida típica de les dades de seqüenciació generades en experiments d'escala mitjana acostuma a situar-se en un rang entre deu i cent gigabytes, que s'emmagatzemen en diversos arxius en diferents formats produïts en cada experiment. Els formats estàndards actuals de facto de representació de dades genòmiques són en format textual. Per raons pràctiques, les dades necessiten ser emmagatzemades en format comprimit. En la majoria dels casos, aquests mètodes de compressió es basen en compressors de text de caràcter general, com ara gzip. Amb tot, no permeten explotar els models d'informació especifícs de dades de seqüenciació. És per això que proporcionen funcionalitats limitades i estalvi insuficient d'espai d'emmagatzematge. Això explica per què operacions relativament bàsiques, com ara el processament, l'emmagatzematge i la transferència de dades genòmiques, s'han convertit en un dels principals obstacles de processos actuals d'anàlisi. Per tot això, aquesta tesi se centra en mètodes d'emmagatzematge i compressió eficients de dades generades en experiments de sequenciació. En primer lloc, proposem un compressor innovador d'arxius FASTQ de propòsit general. A diferència de gzip, aquest compressor permet reduir de manera significativa la mida de l'arxiu resultant del procés de compressió. A més a més, aquesta eina permet processar les dades a una velocitat alta. A continuació, presentem mètodes de compressió que fan ús de l'alta redundància de seqüències present en les dades de seqüenciació. Aquests mètodes obtenen la millor ratio de compressió d'entre els compressors FASTQ del marc teòric actual, sense fer ús de cap referència externa. També mostrem aproximacions de compressió amb pèrdua per emmagatzemar dades de seqüenciació auxiliars, que permeten reduir encara més la mida de les dades. En últim lloc, aportem un sistema flexible de compressió i un format de dades. Aquest sistema fa possible generar de manera semi-automàtica solucions de compressió que no estan lligades a cap mena de format específic d'arxius de dades genòmiques. Per tal de facilitar la gestió complexa de dades, diversos conjunts de dades amb formats heterogenis poden ser emmagatzemats en contenidors configurables amb l'opció de dur a terme consultes personalitzades sobre les dades emmagatzemades. A més a més, exposem que les solucions simples basades en el nostre sistema poden obtenir resultats comparables als compressors de format específic de l'estat de l'art. En resum, les solucions desenvolupades i descrites en aquesta tesi poden ser incorporades amb facilitat en processos d'anàlisi de dades genòmiques. Si prenem aquestes solucions conjuntament, aporten una base sòlida per al desenvolupament d'aproximacions completes encaminades a l'emmagatzematge i gestió eficient de dades genòmiques.
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Prinz, zu Salm-Horstmar Maximilian Philipp Albrecht. "The Chromosome 8p23.1 Inversion : High-Throughput Detection & Investigation of Phenotypic Impact." Thesis, Imperial College London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516479.
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Jin, Shuangshuang. "Integrated data modeling in high-throughput proteomices." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Dissertations/Fall2007/S_Jin_111907.pdf.
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Capparuccini, Maria. "Inferential Methods for High-Throughput Methylation Data." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/156.
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The role of abnormal DNA methylation in the progression of disease is a growing area of research that relies upon the establishment of sound statistical methods. The common method for declaring there is differential methylation between two groups at a given CpG site, as summarized by the difference between proportions methylated db=b1-b2, has been through use of a Filtered Two Sample t-test, using the recommended filter of 0.17 (Bibikova et al., 2006b). In this dissertation, we performed a re-analysis of the data used in recommending the threshold by fitting a mixed-effects ANOVA model. It was determined that the 0.17 filter is not accurate and conjectured that application of a Filtered Two Sample t-test likely leads to loss of power. Further, the Two Sample t-test assumes that data arise from an underlying distribution encompassing the entire real number line, whereas b1 and b2 are constrained on the interval . Additionally, the imposition of a filter at a level signifying the minimum level of detectable difference to a Two Sample t-test likely reduces power for smaller but truly differentially methylated CpG sites. Therefore, we compared the Two Sample t-test and the Filtered Two Sample t-test, which are widely used but largely untested with respect to their performance, to three proposed methods. These three proposed methods are a Beta distribution test, a Likelihood ratio test, and a Bootstrap test, where each was designed to address distributional concerns present in the current testing methods. It was ultimately shown through simulations comparing Type I and Type II error rates that the (unfiltered) Two Sample t-test and the Beta distribution test performed comparatively well.
Books on the topic "High Throughput Phenotypic Data"
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Rodríguez-Ezpeleta, Naiara, Michael Hackenberg, and Ana M. Aransay. Bioinformatics for high throughput sequencing. New York, NY: Springer, 2012.
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Geurts, Werner, Francky Catthoor, Serge Vernalde, and Hugo de Man. Accelerator Data-Path Synthesis for High-Throughput Signal Processing Applications. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8720-4.
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Werner, Geurts, ed. Accelerator data-path synthesis for high-throughput signal processing applications. Dordrecht: Kluwer Academic Publishers, 1997.
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library, Wiley online, ed. Systems biology in psychiatric research: From high-throughput data to mathematical modeling. Weinheim: Wiley-VCH, 2010.
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Yang, Po-sŏk. Twaeji yujŏnch'e taeryang yŏmgi sŏyŏl punsŏk mit yuyong yujŏnja palgul =: High-throughput DNA sequence analysis and identification of trait genes in pigs. [Kyŏnggi-do Suwŏn-si]: Nongch'on Chinhŭngch'ŏng, 2009.
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Rodríguez-Ezpeleta, Naiara, Ana M. Aransay, and Michael Hackenberg. Bioinformatics for High Throughput Sequencing. Springer, 2011.
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Rodríguez-Ezpeleta, Naiara, Ana M. Aransay, and Michael Hackenberg. Bioinformatics for High Throughput Sequencing. Springer, 2014.
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A, Ravishankar Rao, and Cecchi Guillermo A, eds. High-throughput image reconstruction and analysis. Norwood, MA: Artech House, 2009.
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Catthoor, Francky, Hugo De Man, Werner Geurts, and Serge Vernalde. Accelerator Data-Path Synthesis for High-Throughput Signal Processing Applications. Springer, 1996.
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Catthoor, Francky, Hugo De Man, Werner Geurts, and Serge Vernalde. Accelerator Data-Path Synthesis for High-Throughput Signal Processing Applications. Springer, 2012.
Find full textBook chapters on the topic "High Throughput Phenotypic Data"
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Araus, Jose Luis, Maria Luisa Buchaillot, and Shawn C. Kefauver. "High Throughput Field Phenotyping." In Wheat Improvement, 495–512. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90673-3_27.
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AbstractThe chapter aims to provide guidance on how phenotyping may contribute to the genetic advance of wheat in terms of yield potential and resilience to adverse conditions. Emphasis will be given to field high throughput phenotyping, including affordable solutions, together with the need for environmental and spatial characterization. Different remote sensing techniques and platforms are presented, while concerning lab techniques only a well proven trait, such as carbon isotope composition, is included. Finally, data integration and its implementation in practice is discussed. In that sense and considering the physiological determinants of wheat yield that are amenable for indirect selection, we highlight stomatal conductance and stay green as key observations. This choice of traits and phenotyping techniques is based on results from a large set of retrospective and other physiological studies that have proven the value of these traits together with the highlighted phenotypical approaches.
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Zhou, Jing, Chin Nee Vong, and Jianfeng Zhou. "Imaging Technology for High-Throughput Plant Phenotyping." In Sensing, Data Managing, and Control Technologies for Agricultural Systems, 75–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03834-1_4.
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Wardwell-Swanson, Judith, and Yanhua Hu. "Utilization of Multidimensional Data in the Analysis of Ultra-High-Throughput High Content Phenotypic Screens." In Methods in Molecular Biology, 267–90. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7357-6_16.
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Eberius, Matthias, and José Lima-Guerra. "High-Throughput Plant Phenotyping – Data Acquisition, Transformation, and Analysis." In Bioinformatics, 259–78. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92738-1_13.
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Habyarimana, Ephrem, and Sofia Michailidou. "Genomics Data." In Big Data in Bioeconomy, 69–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_6.
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AbstractIn silico prediction of plant performance is gaining increasing breeders’ attention. Several statistical, mathematical and machine learning methodologies for analysis of phenotypic, omics and environmental data typically use individual or a few data layers. Genomic selection is one of the applications, where heterogeneous data, such as those from omics technologies, are handled, accommodating several genetic models of inheritance. There are many new high throughput Next Generation Sequencing (NGS) platforms on the market producing whole-genome data at a low cost. Hence, large-scale genomic data can be produced and analyzed enabling intercrosses and fast-paced recurrent selection. The offspring properties can be predicted instead of manually evaluated in the field . Breeders have a short time window to make decisions by the time they receive data, which is one of the major challenges in commercial breeding. To implement genomic selection routinely as part of breeding programs, data management systems and analytics capacity have therefore to be in order. The traditional relational database management systems (RDBMS), which are designed to store, manage and analyze large-scale data, offer appealing characteristics, particularly when they are upgraded with capabilities for working with binary large objects. In addition, NoSQL systems were considered effective tools for managing high-dimensional genomic data. MongoDB system, a document-based NoSQL database, was effectively used to develop web-based tools for visualizing and exploring genotypic information. The Hierarchical Data Format (HDF5), a member of the high-performance distributed file systems family, demonstrated superior performance with high-dimensional and highly structured data such as genomic sequencing data.
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Morota, Gota, Diego Jarquin, Malachy T. Campbell, and Hiroyoshi Iwata. "Statistical Methods for the Quantitative Genetic Analysis of High-Throughput Phenotyping Data." In Methods in Molecular Biology, 269–96. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2537-8_21.
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AbstractThe advent of plant phenomics, coupled with the wealth of genotypic data generated by next-generation sequencing technologies, provides exciting new resources for investigations into and improvement of complex traits. However, these new technologies also bring new challenges in quantitative genetics, namely, a need for the development of robust frameworks that can accommodate these high-dimensional data. In this chapter, we describe methods for the statistical analysis of high-throughput phenotyping (HTP) data with the goal of enhancing the prediction accuracy of genomic selection (GS). Following the Introduction in Sec. 1, Sec. 2 discusses field-based HTP, including the use of unoccupied aerial vehicles and light detection and ranging, as well as how we can achieve increased genetic gain by utilizing image data derived from HTP. Section 3 considers extending commonly used GS models to integrate HTP data as covariates associated with the principal trait response, such as yield. Particular focus is placed on single-trait, multi-trait, and genotype by environment interaction models. One unique aspect of HTP data is that phenomics platforms often produce large-scale data with high spatial and temporal resolution for capturing dynamic growth, development, and stress responses. Section 4 discusses the utility of a random regression model for performing longitudinal modeling. The chapter concludes with a discussion of some standing issues.
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Pérez-Rodríguez, Paulino, Juan Burgueño, Osval A. Montesinos-López, Ravi P. Singh, Philomin Juliana, Suchismita Mondal, and José Crossa. "Prediction with big data in the genomic and high-throughput phenotyping era: a case study with wheat data." In Quantitative genetics, genomics and plant breeding, 213–26. Wallingford: CABI, 2020. http://dx.doi.org/10.1079/9781789240214.0213.
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Mahjoubfar, Ata, Claire Lifan Chen, and Bahram Jalali. "Label-Free High-Throughput Phenotypic Screening." In Artificial Intelligence in Label-free Microscopy, 33–41. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51448-2_5.
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Ahmed, Hafiz Ghulam Muhu-Din, Yawen Zeng, Sajid Fiaz, and Abdul Rehman Rashid. "Applications of High-Throughput Phenotypic Phenomics." In Sustainable Agriculture in the Era of the OMICs Revolution, 119–34. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-15568-0_6.
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Crossa, José, Osval Antonio Montesinos-López, Paulino Pérez-Rodríguez, Germano Costa-Neto, Roberto Fritsche-Neto, Rodomiro Ortiz, Johannes W. R. Martini, et al. "Genome and Environment Based Prediction Models and Methods of Complex Traits Incorporating Genotype × Environment Interaction." In Methods in Molecular Biology, 245–83. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2205-6_9.
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AbstractGenomic-enabled prediction models are of paramount importance for the successful implementation of genomic selection (GS) based on breeding values. As opposed to animal breeding, plant breeding includes extensive multienvironment and multiyear field trial data. Hence, genomic-enabled prediction models should include genotype × environment (G × E) interaction, which most of the time increases the prediction performance when the response of lines are different from environment to environment. In this chapter, we describe a historical timeline since 2012 related to advances of the GS models that take into account G × E interaction. We describe theoretical and practical aspects of those GS models, including the gains in prediction performance when including G × E structures for both complex continuous and categorical scale traits. Then, we detailed and explained the main G × E genomic prediction models for complex traits measured in continuous and noncontinuous (categorical) scale. Related to G × E interaction models this review also examine the analyses of the information generated with high-throughput phenotype data (phenomic) and the joint analyses of multitrait and multienvironment field trial data that is also employed in the general assessment of multitrait G × E interaction. The inclusion of nongenomic data in increasing the accuracy and biological reliability of the G × E approach is also outlined. We show the recent advances in large-scale envirotyping (enviromics), and how the use of mechanistic computational modeling can derive the crop growth and development aspects useful for predicting phenotypes and explaining G × E.
Conference papers on the topic "High Throughput Phenotypic Data"
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Zhu, Feiyu, Suresh Thapa, Tiao Gao, Yufeng Ge, Harkamal Walia, and Hongfeng Yu. "3D Reconstruction of Plant Leaves for High-Throughput Phenotyping." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622428.
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Zhu, Feiyu, Yu Pan, Tian Gao, Harkamal Walia, and Hongfeng Yu. "Interactive Visualization of Time-Varying Hyperspectral Plant Images for High-Throughput Phenotyping." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006003.
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Pallickara, Sangmi, and Maxwell Roselius. "Radix: Enabling High-Throughput Georeferencing for Phenotype Monitoring over Voluminous Observational Data." In 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). IEEE, 2018. http://dx.doi.org/10.1109/bdcloud.2018.00165.
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Sun, Shangpeng, and Changying Li. "<i>In-field high throughput phenotyping and phenotype data analysis for cotton plant growth using LiDAR</i>." In 2017 Spokane, Washington July 16 - July 19, 2017. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2017. http://dx.doi.org/10.13031/aim.201701210.
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Cholachgudda, Kartik E., Rajashekhar C. Biradar, Kouame Yann Olivier Akansie, Geetha D. Devanagavi, and Aditya A. Sannabhadti. "Design of a Multispectral and Thermal Data Acquisition System for High-Throughput Phenotyping of Plant Pathology." In 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE, 2022. http://dx.doi.org/10.1109/conecct55679.2022.9865773.
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Pour, Majid Khak, Reza Fotouhi, and Pierre Hucl. "Development of a Mobile Platform for Wheat Phenotyping." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-24329.
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Abstract Designing and implementing an affordable High-Throughput Phenotyping Platform (HTPP) for monitoring crops’ features in different stages of their growth can provide valuable information for crop-breeders to study possible correlation between genotypes and phenotypes. Conducting automatic field measurements can improve crop productions. In this research, we have focused on development of a mechatronic system, hardware and software, for a mobile field-based HTPP for autonomous crop monitoring for wheat field. The system can measure canopy’s height, temperature, vegetation indices and is able to take high quality photos of crops. The system includes developed software for data and image acquisition. The main contribution of this study is autonomous, reliable, and fast data collection for wheat and similar crops.
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Zhang, Qianwei, and Reza Fotouhi. "Vibration Analysis of a Long Boom for a Farm Machine." In ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/detc2018-86188.
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Crop phenotyping is frequently used by breeders and crop scientists to monitor growth of plants and to relate them to plants genotypes. Seemingly, this contributes to better crop growth and results in higher yield. Instead of traditional crop monitoring, which is labor intensive, high-throughput phenotyping (HTP) platforms using ground-based vehicle have several advantages (in speed, efficiency, and cost) over manual methods. A wheeled mobile platform for HTP was developed, and automated data collection were performed for different traits of canola and wheat. These data were compared with manual measured data. In this paper, vibration analysis of a relatively long cantilever boom attached to a vehicle is reported. The paper investigates how different factors influence the boom attached to a regular farming machine, its vibration, and effects on phenotyping sensors attached to the boom.
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Kumar, Pankaj, Jinhai Cai, and Stan Miklavcic. "High-throughput 3D modelling of plants for phenotypic analysis." In the 27th Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2425836.2425896.
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Singh, Rahul, Michalis Pittas, Ido Heskia, Fengyun Xu, James McKerrow, and Conor R. Caffrey. "Automated image-based phenotypic screening for high-throughput drug discovery." In 2009 22nd IEEE International Symposium on Computer-Based Medical Systems (CBMS). IEEE, 2009. http://dx.doi.org/10.1109/cbms.2009.5255338.
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Jackson, Philip T., Yinhai Wang, Sinead Knight, Hongming Chen, Thierry Dorval, Martin Brown, Claus Bendtsen, and Boguslaw Obara. "Phenotypic Profiling of High Throughput Imaging Screens with Generic Deep Convolutional Features." In 2019 16th International Conference on Machine Vision Applications (MVA). IEEE, 2019. http://dx.doi.org/10.23919/mva.2019.8757871.
Full textReports on the topic "High Throughput Phenotypic Data"
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Matthews, W. Achieving High Data Throughput in Research Networks. Office of Scientific and Technical Information (OSTI), September 2004. http://dx.doi.org/10.2172/833103.
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Bulaevskaya, V., and A. P. Sales. Adaptive Sampling for High Throughput Data Using Similarity Measures. Office of Scientific and Technical Information (OSTI), May 2015. http://dx.doi.org/10.2172/1184186.
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Langston, Michael A. Scalable Computational Methods for the Analysis of High-Throughput Biological Data. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1050046.
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Aharoni, Asaph, Zhangjun Fei, Efraim Lewinsohn, Arthur Schaffer, and Yaakov Tadmor. System Approach to Understanding the Metabolic Diversity in Melon. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7593400.bard.
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Fruit quality is determined by numerous genetic factors that affect taste, aroma, color, texture, nutritional value and shelf life. To unravel the genetic components involved in the metabolic pathways behind these traits, the major goal of the project was to identify novel genes that are involved in, or that regulate, these pathways using correlation analysis between genotype, metabolite and gene expression data. The original and specific research objectives were: (1) Collection of replicated fruit from a population of 96 RI lines derived from parents distinguished by great diversity in fruit development and quality phenotypes, (2) Phenotypic and metabolic profiling of mature fruit from all 96 RI lines and their parents, (3) 454 pyrosequencing of cDNA representing mRNA of mature fruit from each line to facilitate gene expression analysis based on relative EST abundance, (4) Development of a database modeled after an existing database developed for tomato introgression lines (ILs) to facilitate online data analysis by members of this project and by researchers around the world. The main functions of the database will be to store and present metabolite and gene expression data so that correlations can be drawn between variation in target traits or metabolites across the RI population members and variation in gene expression to identify candidate genes which may impact phenotypic and chemical traits of interest, (5) Selection of RI lines for segregation and/or hybridization (crosses) analysis to ascertain whether or not genes associated with traits through gene expression/metabolite correlation analysis are indeed contributors to said traits. The overall research strategy was to utilize an available recombinant inbred population of melon (Cucumis melo L.) derived from phenotypically diverse parents and for which over 800 molecular markers have been mapped for the association of metabolic trait and gene expression QTLs. Transcriptomic data were obtained by high throughput sequencing using the Illumina platform instead of the originally planned 454 platform. The change was due to the fast advancement and proven advantages of the Illumina platform, as explained in the first annual scientific report. Metabolic data were collected using both targeted (sugars, organic acids, carotenoids) and non-targeted metabolomics analysis methodologies. Genes whose expression patterns were associated with variation of particular metabolites or fruit quality traits represent candidates for the molecular mechanisms that underlie them. Candidate genes that may encode enzymes catalyzingbiosynthetic steps in the production of volatile compounds of interest, downstream catabolic processes of aromatic amino acids and regulatory genes were selected and are in the process of functional analyses. Several of these are genes represent unanticipated effectors of compound accumulation that could not be identified using traditional approaches. According to the original plan, the Cucurbit Genomics Network (http://www.icugi.org/), developed through an earlier BARD project (IS-3333-02), was expanded to serve as a public portal for the extensive metabolomics and transcriptomic data resulting from the current project. Importantly, this database was also expanded to include genomic and metabolomic resources of all the cucurbit crops, including genomes of cucumber and watermelon, EST collections, genetic maps, metabolite data and additional information. In addition, the database provides tools enabling researchers to identify genes, the expression patterns of which correlate with traits of interest. The project has significantly expanded the existing EST resource for melon and provides new molecular tools for marker-assisted selection. This information will be opened to the public by the end of 2013, upon the first publication describing the transcriptomic and metabolomics resources developed through the project. In addition, well-characterized RI lines are available to enable targeted breeding for genes of interest. Segregation of the RI lines for specific metabolites of interest has been shown, demonstrating the utility in these lines and our new molecular and metabolic data as a basis for selection targeting specific flavor, quality, nutritional and/or defensive compounds. To summarize, all the specific goals of the project have been achieved and in many cases exceeded. Large scale trascriptomic and metabolomic resources have been developed for melon and will soon become available to the community. The usefulness of these has been validated. A number of novel genes involved in fruit ripening have been selected and are currently being functionally analyzed. We thus fully addressed our obligations to the project. In our view, however, the potential value of the project outcomes as ultimately manifested may be far greater than originally anticipated. The resources developed and expanded under this project, and the tools created for using them will enable us, and others, to continue to employ resulting data and discoveries in future studies with benefits both in basic and applied agricultural - scientific research.
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Cohen, Yuval, Christopher A. Cullis, and Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, October 2010. http://dx.doi.org/10.32747/2010.7592124.bard.
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Date palm (Phoenix dactylifera L.) is the major fruit tree grown in arid areas in the Middle East and North Africa. In the last century, dates were introduced to new regions including the USA. Date palms are traditionally propagated through offshoots. Expansion of modern date palm groves led to the development of Tissue Culture propagation methods that generate a large number of homogenous plants, have no seasonal effect on plant source and provide tools to fight the expansion of date pests and diseases. The disadvantage of this procedure is the occurrence of off-type trees which differ from the original cultivar. In the present project we focused on two of the most common date palm off-types: (1) trees with reduced fruit setting, in which most of the flowers turn into three-carpel parthenocarpic fruits. In a severe form, multi-carpel flowers and fruitlets (with up to six or eight carpels instead of the normal three-carpel flowers) are also formed. (2) dwarf trees, having fewer and shorter leaves, very short trunk and are not bearing fruits at their expected age, compared to the normal trees. Similar off-types occur in other crop species propagated by tissue culture, like banana (mainly dwarf plants) or oil palm (with a common 'Mantled' phenotype with reduced fruit setting and occurrence of supernumerary carpels). Some off-types can only be detected several years after planting in the fields. Therefore, efficient methods for prevention of the generation of off-types, as well as methods for their detection and early removal, are required for date palms, as well as for other tissue culture propagated crops. This research is aimed at the understanding of the mechanisms by which off-types are generated, and developing markers for their early identification. Several molecular and genomic approaches were applied. Using Methylation Sensitive AFLP and bisulfite sequencing, we detected changes in DNA methylation patterns occurring in off-types. We isolated and compared the sequence and expression of candidate genes, genes related to vegetative growth and dwarfism and genes related to flower development. While no sequence variation were detected, changes in gene expression, associated with the severity of the "fruit set" phenotype were detected in two genes - PdDEF (Ortholog of rice SPW1, and AP3 B type MADS box gene), and PdDIF (a defensin gene, highly homologous to the oil palm gene EGAD). We applied transcriptomic analyses, using high throughput sequencing, to identify genes differentially expressed in the "palm heart" (the apical meristem and the region of embryonic leaves) of dwarf vs. normal trees. Among the differentially expressed genes we identified genes related to hormonal biosynthesis, perception and regulation, genes related to cell expansion, and genes related to DNA methylation. Using Representation Difference Analyses, we detected changes in the genomes of off-type trees, mainly chloroplast-derived sequences that were incorporated in the nuclear genome and sequences of transposable elements. Sequences previously identified as differing between normal and off-type trees of oil palms or banana, successfully identified variation among date palm off-types, suggesting that these represent highly labile regions of monocot genomes. The data indicate that the date palm genome, similarly to genomes of other monocot crops as oil palm and banana, is quite unstable when cells pass through a cycle of tissue culture and regeneration. Changes in DNA sequences, translocation of DNA fragments and alteration of methylation patterns occur. Consequently, patterns of gene expression are changed, resulting in abnormal phenotypes. The data can be useful for future development of tools for early identification of off-type as well as for better understanding the phenomenon of somaclonal variation during propagation in vitro.
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Idakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.
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Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
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Gur, Amit, Edward Buckler, Joseph Burger, Yaakov Tadmor, and Iftach Klapp. Characterization of genetic variation and yield heterosis in Cucumis melo. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7600047.bard.
Full textAbstract:
Project objectives: 1) Characterization of variation for yield heterosis in melon using Half-Diallele (HDA) design. 2) Development and implementation of image-based yield phenotyping in melon. 3) Characterization of genetic, epigenetic and transcriptional variation across 25 founder lines and selected hybrids. The epigentic part of this objective was modified during the course of the project: instead of characterization of chromatin structure in a single melon line through genome-wide mapping of nucleosomes using MNase-seq approach, we took advantage of rapid advancements in single-molecule sequencing and shifted the focus to Nanoporelong-read sequencing of all 25 founder lines. This analysis provides invaluable information on genome-wide structural variation across our diversity 4) Integrated analyses and development of prediction models Agricultural heterosis relates to hybrids that outperform their inbred parents for yield. First generation (F1) hybrids are produced in many crop species and it is estimated that heterosis increases yield by 15-30% globally. Melon (Cucumismelo) is an economically important species of The Cucurbitaceae family and is among the most important fleshy fruits for fresh consumption Worldwide. The major goal of this project was to explore the patterns and magnitude of yield heterosis in melon and link it to whole genome sequence variation. A core subset of 25 diverse lines was selected from the Newe-Yaar melon diversity panel for whole-genome re-sequencing (WGS) and test-crosses, to produce structured half-diallele design of 300 F1 hybrids (MelHDA25). Yield variation was measured in replicated yield trials at the whole-plant and at the rootstock levels (through a common-scion grafted experiments), across the F1s and parental lines. As part of this project we also developed an algorithmic pipeline for detection and yield estimation of melons from aerial-images, towards future implementation of such high throughput, cost-effective method for remote yield evaluation in open-field melons. We found extensive, highly heritable root-derived yield variation across the diallele population that was characterized by prominent best-parent heterosis (BPH), where hybrids rootstocks outperformed their parents by 38% and 56 % under optimal irrigation and drought- stress, respectively. Through integration of the genotypic data (~4,000,000 SNPs) and yield analyses we show that root-derived hybrids yield is independent of parental genetic distance. However, we mapped novel root-derived yield QTLs through genome-wide association (GWA) analysis and a multi-QTLs model explained more than 45% of the hybrids yield variation, providing a potential route for marker-assisted hybrid rootstock breeding. Four selected hybrid rootstocks are further studied under multiple scion varieties and their validated positive effect on yield performance is now leading to ongoing evaluation of their commercial potential. On the genomic level, this project resulted in 3 layers of data: 1) whole-genome short-read Illumina sequencing (30X) of the 25 founder lines provided us with 25 genome alignments and high-density melon HapMap that is already shown to be an effective resource for QTL annotation and candidate gene analysis in melon. 2) fast advancements in long-read single-molecule sequencing allowed us to shift focus towards this technology and generate ~50X Nanoporesequencing of the 25 founders which in combination with the short-read data now enable de novo assembly of the 25 genomes that will soon lead to construction of the first melon pan-genome. 3) Transcriptomic (3' RNA-Seq) analysis of several selected hybrids and their parents provide preliminary information on differentially expressed genes that can be further used to explain the root-derived yield variation. Taken together, this project expanded our view on yield heterosis in melon with novel specific insights on root-derived yield heterosis. To our knowledge, thus far this is the largest systematic genetic analysis of rootstock effects on yield heterosis in cucurbits or any other crop plant, and our results are now translated into potential breeding applications. The genomic resources that were developed as part of this project are putting melon in the forefront of genomic research and will continue to be useful tool for the cucurbits community in years to come.
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Brosh, Arieh, Gordon Carstens, Kristen Johnson, Ariel Shabtay, Joshuah Miron, Yoav Aharoni, Luis Tedeschi, and Ilan Halachmi. Enhancing Sustainability of Cattle Production Systems through Discovery of Biomarkers for Feed Efficiency. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7592644.bard.
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Feed inputs represent the largest variable cost of producing meat and milk from ruminant animals. Thus, strategies that improve the efficiency of feed utilization are needed to improve the global competitiveness of Israeli and U.S. cattle industries, and mitigate their environmental impact through reductions in nutrient excretions and greenhouse gas emissions. Implementation of innovative technologies that will enhance genetic merit for feed efficiency is arguably one of the most cost-effective strategies to meet future demands for animal-protein foods in an environmentally sustainable manner. While considerable genetic variation in feed efficiency exist within cattle populations, the expense of measuring individual-animal feed intake has precluded implementation of selection programs that target this trait. Residual feed intake (RFI) is a trait that quantifies between-animal variation in feed intake beyond that expected to meet energy requirements for maintenance and production, with efficient animals being those that eat less than expected for a given size and level of production. There remains a critical need to understand the biological drivers for genetic variation in RFI to facilitate development of effective selection programs in the future. Therefore, the aim of this project was to determine the biological basis for phenotypic variation in RFI of growing and lactating cattle, and discover metabolic biomarkers of RFI for early and more cost-effective selection of cattle for feed efficiency. Objectives were to: (1) Characterize the phenotypic relationships between RFI and production traits (growth or lactation), (2) Quantify inter-animal variation in residual HP, (3) Determine if divergent RFIphenotypes differ in HP, residual HP, recovered energy and digestibility, and (4) Determine if divergent RFI phenotypes differ in physical activity, feeding behavior traits, serum hormones and metabolites and hepatic mitochondrial traits. The major research findings from this project to date include: In lactating dairy cattle, substantial phenotypic variation in RFI was demonstrated as cows classified as having low RMEI consumed 17% less MEI than high-RMEI cows despite having similar body size and lactation productivity. Further, between-animal variation in RMEI was found to moderately associated with differences in RHP demonstrating that maintenance energy requirements contribute to observed differences in RFI. Quantifying energetic efficiency of dairy cows using RHP revealed that substantial changes occur as week of lactation advances—thus it will be critical to measure RMEI at a standardized stage of lactation. Finally, to determine RMEI in lactating dairy cows, individual DMI and production data should be collected for a minimum of 6 wk. We demonstrated that a favorably association exists between RFI in growing heifers and efficiency of forage utilization in pregnant cows. Therefore, results indicate that female progeny from parents selected for low RFI during postweaning development will also be efficient as mature females, which has positive implications for both dairy and beef cattle industries. Results from the beef cattle studies further extend our knowledge regarding the biological drivers of phenotypic variation in RFI of growing animals, and demonstrate that significant differences in feeding behavioral patterns, digestibility and heart rate exist between animals with divergent RFI. Feeding behavior traits may be an effective biomarker trait for RFI in beef and dairy cattle. There are differences in mitochondrial acceptor control and respiratory control ratios between calves with divergent RFI suggesting that variation in mitochondrial metabolism may be visible at the genome level. Multiple genes associated with mitochondrial energy processes are altered by RFI phenotype and some of these genes are associated with mitochondrial energy expenditure and major cellular pathways involved in regulation of immune responses and energy metabolism.
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Sherman, Amir, Rebecca Grumet, Ron Ophir, Nurit Katzir, and Yiqun Weng. Whole genome approach for genetic analysis in cucumber: Fruit size as a test case. United States Department of Agriculture, December 2013. http://dx.doi.org/10.32747/2013.7594399.bard.
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The Cucurbitaceae family includes a broad array of economically and nutritionally important crop species that are consumed as vegetables, staple starches and desserts. Fruit of these species, and types within species, exhibit extensive diversity as evidenced by variation in size, shape, color, flavor, and others. Fruit size and shape are critical quality determinants that delineate uses and market classes and are key traits under selection in breeding programs. However, the underlying genetic bases for variation in fruit size remain to be determined. A few species the Cucurbitaceae family were sequenced during the time of this project (cucumber was already sequenced when the project started watermelon and melon sequence became available during the project) but functional genomic tools are still missing. This research program had three major goals: 1. Develop whole genome cucumber and melon SNP arrays. 2. Develop and characterize cucumber populations segregating for fruit size. 3. Combine genomic tools, segregating populations, and phenotypic characterization to identify loci associated with fruit size. As suggested by the reviewers the work concentrated mostly in cucumber and not both in cucumber and melon. In order to develop a SNP (single nucleotide polymorphism) array for cucumber, available and newly generated sequence from two cucumber cultivars with extreme differences in shape and size, pickling GY14 and Chinese long 9930, were analyzed for variation (SNPs). A large set of high quality SNPs was discovered between the two parents of the RILs population (GY14 and 9930) and used to design a custom SNP array with 35000 SNPs using Agilent technology. The array was validated using 9930, Gy14 and F1 progeny of the two parents. Several mapping populations were developed for linkage mapping of quantitative trait loci (QTL) for fruit size These includes 145 F3 families and 150 recombinant inbred line (RILs F7 or F8 (Gy14 X 9930) and third population contained 450 F2 plants from a cross between Gy14 and a wild plant from India. The main population that was used in this study is the RILs population of Gy14 X 9930. Phenotypic and morphological analyses of 9930, Gy14, and their segregating F2 and RIL progeny indicated that several, likely independent, factors influence cucumber fruit size and shape, including factors that act both pre-anthesis and post-pollination. These include: amount, rate, duration, and plane of cell division pre- and post-anthesis and orientation of cell expansion. Analysis of F2 and RIL progeny indicated that factors influencing fruit length were largely determined pre-anthesis, while fruit diameter was more strongly influenced by environment and growth factors post-anthesis. These results suggest involvement of multiple genetically segregating factors expected to map independently onto the cucumber genome. Using the SNP array and the phenotypic data two major QTLs for fruit size of cucumber were mapped in very high accuracy (around 300 Kb) with large set of markers that should facilitate identification and cloning of major genes that contribute to fruit size in cucumber. In addition, a highly accurate haplotype map of all RILS was created to allow fine mapping of other traits segregating in this population. A detailed cucumber genetic map with 6000 markers was also established (currently the most detailed genetic map of cucumber). The integration of genetics physiology and genomic approaches in this project yielded new major infrastructure tools that can be used for understanding fruit size and many other traits of importance in cucumber. The SNP array and genetic population with an ultra-fine map can be used for future breeding efforts, high resolution mapping and cloning of traits of interest that segregate in this population. The genetic map that was developed can be used for other breeding efforts in other populations. The study of fruit development that was done during this project will be important in dissecting function of genes that that contribute to the fruit size QTLs. The SNP array can be used as tool for mapping different traits in cucumber. The development of the tools and knowledge will thus promote genetic improvement of cucumber and related cucurbits.
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Splitter, Gary A., Menachem Banai, and Jerome S. Harms. Brucella second messenger coordinates stages of infection. United States Department of Agriculture, January 2011. http://dx.doi.org/10.32747/2011.7699864.bard.
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Aim 1: To determine levels of this second messenger in: a) B. melitensiscyclic-dimericguanosinemonophosphate-regulating mutants (BMEI1448, BMEI1453, and BMEI1520), and b) B. melitensis16M (wild type) and mutant infections of macrophages and immune competent mice. (US lab primary) Aim 2: To determine proteomic differences between Brucelladeletion mutants BMEI1453 (high cyclic-dimericguanosinemonophosphate, chronic persistent state) and BMEI1520 (low cyclicdimericguanosinemonophosphate, acute virulent state) compared to wild type B. melitensisto identify the role of this second messenger in establishing the two polar states of brucellosis. (US lab primary with synergistic assistance from the Israel lab Aim 3: Determine the level of Brucellacyclic-dimericguanosinemonophosphate and transcriptional expression from naturally infected placenta. (Israel lab primary with synergistic assistance from the US lab). B. Background Brucellaspecies are Gram-negative, facultative intracellular bacterial pathogens that cause brucellosis, the most prevalent zoonosis worldwide. Brucellosis is characterized by increased abortion, weak offspring, and decreased milk production in animals. Humans are infected with Brucellaby consuming contaminated milk products or via inhalation of aerosolized bacteria from occupational hazards. Chronic human infections can result in complications such as liver damage, orchitis, endocarditis, and arthritis. Brucellaspp. have the ability to infect both professional and non-professional phagocytes. Because of this, Brucellaencounter varied environments both throughout the body and within a cell and must adapt accordingly. To date, few virulence factors have been identified in B. melitensisand even less is known about how these virulence factors are regulated. Subsequently, little is known about how Brucellaadapt to its rapidly changing environments, and how it alternates between acute and chronic virulence. Our studies suggest that decreased concentrations of cyclic dimericguanosinemonophosphate (c-di-GMP) lead to an acute virulent state and increased concentrations of c-di-GMP lead to persistent, chronic state of B. melitensisin a mouse model of infection. We hypothesize that B. melitensisuses c-di-GMP to transition from the chronic state of an infected host to the acute, virulent stage of infection in the placenta where the bacteria prepare to infect a new host. Studies on environmental pathogens such as Vibrio choleraeand Pseudomonas aeruginosasupport a mechanism where changes in c-di-GMP levels cause the bacterium to alternate between virulent and chronic states. Little work exists on understanding the role of c-di-GMP in dangerous intracellular pathogens, like Brucellathat is a frequent pathogen in Israeli domestic animals and U.S. elk and bison. Brucellamust carefully regulate virulence factors during infection of a host to ensure proper expression at appropriate times in response to host cues. Recently, the novel secondary signaling molecule c-di-GMP has been identified as a major component of bacterial regulation and we have identified c-di-GMP as an important signaling factor in B. melitensishost adaptation. C. Major conclusions, solutions, achievements 1. The B. melitensis1453 deletion mutant has increased c-di-GMP, while the 1520 deletion mutant has decreased c-di-GMP. 2. Both mutants grow similarly in in vitro cultures; however, the 1453 mutant has a microcolony phenotype both in vitro and in vivo 3. The 1453 mutant has increased crystal violet staining suggesting biofilm formation. 4. Scanning electron microscopy revealed an abnormal coccus appearance with in increased cell area. 5. Proteomic analysis revealed the 1453 mutant possessed increased production of proteins involved in cell wall processes, cell division, and the Type IV secretion system, and a decrease in proteins involved in amino acid transport/metabolism, carbohydrate metabolism, fatty acid production, and iron acquisition suggesting less preparedness for intracellular survival. 6. RNAseq analysis of bone marrow derived macrophages infected with the mutants revealed the host immune response is greatly reduced with the 1453 mutant infection. These findings support that microlocalization of proteins involved in c-di-GMP homeostasis serve a second messenger to B. melitensisregulating functions of the bacteria during infection of the host.