Gotowe bibliografie tematyczne / Gene set enrichment analyses

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  2. Rozprawy doktorskie
  3. Części książek
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Gotowa bibliografia na temat „Gene set enrichment analyses”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Gene set enrichment analyses"

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Alexeyenko, Andrey, Woojoo Lee, Maria Pernemalm, et al. "Network enrichment analysis: extension of gene-set enrichment analysis to gene networks." BMC Bioinformatics 13, no. 1 (2012): 226. http://dx.doi.org/10.1186/1471-2105-13-226.

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Irizarry, Rafael A., Chi Wang, Yun Zhou, and Terence P. Speed. "Gene set enrichment analysis made simple." Statistical Methods in Medical Research 18, no. 6 (2009): 565–75. http://dx.doi.org/10.1177/0962280209351908.

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Backes, C., A. Keller, J. Kuentzer, et al. "GeneTrail--advanced gene set enrichment analysis." Nucleic Acids Research 35, Web Server (2007): W186—W192. http://dx.doi.org/10.1093/nar/gkm323.

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Saxena, Vishal, Dennis Orgill, and Isaac Kohane. "Absolute enrichment: gene set enrichment analysis for homeostatic systems." Nucleic Acids Research 34, no. 22 (2006): e151-e151. http://dx.doi.org/10.1093/nar/gkl766.

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Tragante, Vinicius, Johannes M. I. H. Gho, Janine F. Felix, et al. "Gene Set Enrichment Analyses: lessons learned from the heart failure phenotype." BioData Mining 10, no. 1 (2017): 18. https://doi.org/10.1186/s13040-017-0137-5.

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<strong>Background: </strong>Genetic studies for complex diseases have predominantly discovered main effects at individual loci, but have not focused on genomic and environmental contexts important for a phenotype. Gene Set Enrichment Analysis (GSEA) aims to address this by identifying sets of genes or biological pathways contributing to a phenotype, through gene-gene interactions or other mechanisms, which are not the focus of conventional association methods.<strong>Results: </strong>Approaches that utilize GSEA can now take input from array chips, either gene-centric or genome-wide, but are
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Tan, Yan, Felix Wu, Pablo Tamayo, W. Nicholas Haining, and Jill P. Mesirov. "Constellation Map: Downstream visualization and interpretation of gene set enrichment results." F1000Research 4 (June 24, 2015): 167. http://dx.doi.org/10.12688/f1000research.6644.1.

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Summary: Gene set enrichment analysis (GSEA) approaches are widely used to identify coordinately regulated genes associated with phenotypes of interest. Here, we present Constellation Map, a tool to visualize and interpret the results when enrichment analyses yield a long list of significantly enriched gene sets. Constellation Map identifies commonalities that explain the enrichment of multiple top-scoring gene sets and maps the relationships between them. Constellation Map can help investigators take full advantage of GSEA and facilitates the biological interpretation of enrichment results. A
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Hukku, Abhay, Corbin Quick, Francesca Luca, Roger Pique-Regi, and Xiaoquan Wen. "BAGSE: a Bayesian hierarchical model approach for gene set enrichment analysis." Bioinformatics 36, no. 6 (2019): 1689–95. http://dx.doi.org/10.1093/bioinformatics/btz831.

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Abstract Motivation Gene set enrichment analysis has been shown to be effective in identifying relevant biological pathways underlying complex diseases. Existing approaches lack the ability to quantify the enrichment levels accurately, hence preventing the enrichment information to be further utilized in both upstream and downstream analyses. A modernized and rigorous approach for gene set enrichment analysis that emphasizes both hypothesis testing and enrichment estimation is much needed. Results We propose a novel computational method, Bayesian Analysis of Gene Set Enrichment (BAGSE), for ge
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Prummer, Michael. "Enhancing gene set enrichment using networks." F1000Research 8 (January 30, 2019): 129. http://dx.doi.org/10.12688/f1000research.17824.1.

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Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of
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Prummer, Michael. "Enhancing gene set enrichment using networks." F1000Research 8 (July 16, 2019): 129. http://dx.doi.org/10.12688/f1000research.17824.2.

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Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of
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Schmid, Florian, Matthias Schmid, Christoph Müssel, et al. "GiANT: gene set uncertainty in enrichment analysis." Bioinformatics 32, no. 12 (2016): 1891–94. http://dx.doi.org/10.1093/bioinformatics/btw030.

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Rozprawy doktorskie na temat "Gene set enrichment analyses"

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Paszkowski-Rogacz, Maciej, Frank Buchholz, Mikolaj Slabicki, and Maria Teresa Pisabarro. "PhenoFam-gene set enrichment analysis through protein structural information." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-176848.

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Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, Phen
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Paszkowski-Rogacz, Maciej, Frank Buchholz, Mikolaj Slabicki, and Maria Teresa Pisabarro. "PhenoFam-gene set enrichment analysis through protein structural information." BioMed Central, 2010. https://tud.qucosa.de/id/qucosa%3A28875.

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Background With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, Phen
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Li, Wei. "Analyzing Gene Expression Data in Terms of Gene Sets: Gene Set Enrichment Analysis." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/79.

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The DNA microarray biotechnology simultaneously monitors the expression of thousands of genes and aims to identify genes that are differently expressed under different conditions. From the statistical point of view, it can be restated as identify genes strongly associated with the response or covariant of interest. The Gene Set Enrichment Analysis (GSEA) method is one method which focuses the analysis at the functional related gene sets level instead of single genes. It helps biologists to interpret the DNA microarray data by their previous biological knowledge of the genes in a gene set. GSEA
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Kodysh, Yuliya. "Using co-expression to redefine functional gene sets for gene set enrichment analysis." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41661.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>Includes bibliographical references (p. 89-90).<br>Manually curated gene sets related to a biological function often contain genes that are not tightly co-regulated transcriptionally. which obscures the evidence of coordinated differential expression of these gene sets in relevant experiments. To address this problem, we explored strategies to refine the manually curated subcollection of the Molecular Signatures Database (MSigDB) for use with Gene Set Enrichment Analysis (GSE
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Jadhav, Trishul. "Knowledge Based Gene Set analysis (KB-GSA) : A novel method for gene expression analysis." Thesis, University of Skövde, School of Life Sciences, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-4352.

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<p>Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes a
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Lu, Yingzhou. "Multi-omics Data Integration for Identifying Disease Specific Biological Pathways." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83467.

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Pathway analysis is an important task for gaining novel insights into the molecular architecture of many complex diseases. With the advancement of new sequencing technologies, a large amount of quantitative gene expression data have been continuously acquired. The springing up omics data sets such as proteomics has facilitated the investigation on disease relevant pathways. Although much work has previously been done to explore the single omics data, little work has been reported using multi-omics data integration, mainly due to methodological and technological limitations. While a single om
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SARTOR, MAUREEN A. "TESTING FOR DIFFERENTIALLY EXPRESSED GENES AND KEY BIOLOGICAL CATEGORIES IN DNA MICROARRAY ANALYSIS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195656673.

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Yu, Mengyao. "Exploitation des données issues d'études d'association pangénomiques pour caractériser les voies biologiques associées au risque génétique du prolapsus de la valve mitrale GWAS-driven gene-set analyses, genetic and functional follow-up suggest GLIS1 as a susceptibility gene for mitral valve prolapse Up-dated genome-wide association study and functional annotation reveal new risk loci for mitral valve prolapse." Thesis, Sorbonne Paris Cité, 2019. https://wo.app.u-paris.fr/cgi-bin/WebObjects/TheseWeb.woa/wa/show?t=2203&f=17890.

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Le prolapsus de la valve mitrale (MVP) est une valvulopathie fréquente qui touche près de 1 personne sur 40 dans la population générale. Il s'agit de la première indication de réparation et / ou de remplacement de la valve. De nombreux gènes comme FLNA, DCHS1 pour les formes familiales et TNS1 et LMCD1 pour les formes sporadiques ont récemment été décrit comme associés au MVP. Cependant, les défauts génétiques touchant ces gènes n'expliquent pas tous les cas du MVP. De plus, les mécanismes biologiques expliquant la susceptibilité génétique au MVP, notamment pour les formes sporadiques les plus
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DADOUSIS, CHRISTOS. "From milk to cheese: genomic background, biological pathways and latent phenotypes of bovine cheese-related traits." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3424728.

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The aim of this PhD thesis was the study of the genomic, biological and phenotypic background of bovine cheese-related traits. The primary goal of this PhD thesis was to unravel the genomic background of bovine milk technological and cheese-related traits to specific chromosomic regions (CHAPTERS 1 to 3). To achieve this, the cow’s ability to produce cheese was decomposed into 11 milk coagulation (MCP) and curd-firming properties (CFt), and 7 cheese yield and milk component recoveries into the curd (REC) traits. Besides, to tackle the problem of the large number of variables required to descri
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Martini, Paolo. "Dissecting the transcriptome complexity with bioinformatics tools." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422923.

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Bioinformatics has acquired a lot of importance especially with the advent of genomic approaches. The large amount of data produced by ``omics'' experiments requires appropriate frameworks to handle, store and mine the information and to derive appropriate work hypotheses. Transcriptome is defined as the whole amount of RNA molecules produced by a cell that provides the bridge between the genome and proteins. RNA molecules can be divided in two major classes: protein coding RNAs or messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs). While the first class has been the most studied in the last
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Części książek na temat "Gene set enrichment analyses"

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Tilford, Charles A., and Nathan O. Siemers. "Gene Set Enrichment Analysis." In Methods in Molecular Biology. Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60761-175-2_6.

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Gentleman, R., M. Morgan, and W. Huber. "Gene Set Enrichment Analysis." In Bioconductor Case Studies. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77240-0_13.

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Hung, Jui-Hung. "Gene Set/Pathway Enrichment Analysis." In Methods in Molecular Biology. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-107-3_13.

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Stiglic, Gregor. "Gene Set Enrichment Meta-Learning Analysis." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1755.

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Bayá, Ariel E., Mónica G. Larese, Pablo M. Granitto, Juan Carlos Gómez, and Elizabeth Tapia. "Gene Set Enrichment Analysis Using Non-parametric Scores." In Advances in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73731-5_2.

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Falcon, S., and R. Gentleman. "Hypergeometric Testing Used for Gene Set Enrichment Analysis." In Bioconductor Case Studies. Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-77240-0_14.

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Zhu, Min, Xiaolai Li, Shujie Wang, Wei Guo, and Xueling Li. "Characterization of Radiotherapy Sensitivity Genes by Comparative Gene Set Enrichment Analysis." In Intelligent Computing Theories and Application. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_25.

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Špendl, Martin, Jaka Kokošar, Ela Praznik, Luka Ausec, and Blaž Zupan. "Ranking of Survival-Related Gene Sets Through Integration of Single-Sample Gene Set Enrichment and Survival Analysis." In Artificial Intelligence in Medicine. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-34344-5_39.

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Wen, Fayuan, Namita Kumari, and James G. Taylor Vi. "RNA-Seq and Gene Set Enrichment Analysis (GSEA) in Peripheral Blood Mononuclear Cells (PBMCs)." In Methods in Molecular Biology. Springer US, 2025. https://doi.org/10.1007/978-1-0716-4276-4_8.

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Karagiannaki, Ioulia, Yannis Pantazis, Ekaterini Chatzaki, and Ioannis Tsamardinos. "Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data." In Discovery Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_17.

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Abstract Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (e.g., high dimensional data). However, there exist lower-dimensional representations that retain the useful information. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL’s latent space has a relatively straight-forward biologic
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Streszczenia konferencji na temat "Gene set enrichment analyses"

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Sharma, Mohita, Joshua Handy, Dongshan An, Gerrit Voordouw, and Lisa M. Gieg. "Characterization of Microbiologically Influenced Corrosion Potential in Nitrate Injected Produced Waters." In CORROSION 2019. NACE International, 2019. https://doi.org/10.5006/c2019-13198.

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Abstract Microorganisms are notorious for being involved in serious metal infrastructure damage, popularly known as microbiologically influenced corrosion (MIC). Long term corrosion incubations (~2 years) with carbon steel (CS) beads were established using produced water collected from a Canadian oilfield where nitrate was routinely used for souring mitigation. Experiments were set up under methanogenic, sulfate-reducing, and nitrate-reducing conditions to stimulate electrical MIC (EMIC) with iron present as the sole electron donor. Microbial community analysis, chemical measurements, metal we
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Clark, Neil R., Maciej Szymkiewicz, Zichen Wang, Caroline D. Monteiro, Matthew R. Jones, and Avi Ma'ayan. "Principle Angle Enrichment Analysis (PAEA): Dimensionally reduced multivariate gene set enrichment analysis tool." In 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2015. http://dx.doi.org/10.1109/bibm.2015.7359689.

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Lin, Frank, Miew Keen Choong, and Guy Tsafnat. "Using Multiple gene set enrichment analyses to support knowledge discovery in cancer transcriptome data." In Annual International Conference on BioInformatics and Computational Biology & Annual International Conference on Advances in Biotechnology. Global Science and Technology Forum, 2011. http://dx.doi.org/10.5176/978-981-08-8119-1_bicb10.

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Yidong Chen, Fan Yang, and Paul S. Meltzer. "Application of gene set enrichment method to ChIP-chip data analysis." In 2008 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2008. http://dx.doi.org/10.1109/gensips.2008.4555684.

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WANG, YONGJIA, STANLEY J. WATSON, and FAN MENG. "EXPLORING IMPORTANT ISSUES IN THE IMPLEMENTATION OF GENE SET ENRICHMENT ANALYSIS." In Proceedings of the International Conference. WORLD SCIENTIFIC, 2005. http://dx.doi.org/10.1142/9789812702098_0007.

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Kim, Jaeyoung, Hyungmin Lee, and Miyoung Shin. "Identifying Biologically Significant Pathways by Gene Set Enrichment Analysis Using Fisher's Criterion." In 2008 Second International Conference on Future Generation Communication and Networking (FGCN). IEEE, 2008. http://dx.doi.org/10.1109/fgcn.2008.212.

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Jones, Matthew R. "Abstract B1-35: Enrichr2: Next generation gene set enrichment analysis web-based tool." In Abstracts: AACR Special Conference: Computational and Systems Biology of Cancer; February 8-11, 2015; San Francisco, CA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.compsysbio-b1-35.

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Praveen Kumar, A., AJ Kovatich, A. Biancotto, et al. "Abstract P4-09-14: Analysis of breast cancer recurrence using gene set enrichment analysis." In Abstracts: 2017 San Antonio Breast Cancer Symposium; December 5-9, 2017; San Antonio, Texas. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1538-7445.sabcs17-p4-09-14.

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Kumar, Ashwani, and Tiratha Raj Singh. "Systems biology approach for gene set enrichment and topological analysis of Alzheimer's disease pathway." In 2016 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2016. http://dx.doi.org/10.1109/bsb.2016.7552132.

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"CisCross web service: a gene set enrichment analysis to predict the upstream regulators for Arabidopsis thaliana." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-374.

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Raporty organizacyjne na temat "Gene set enrichment analyses"

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Li, Li, Joseph Burger, Nurit Katzir, Yaakov Tadmor, Ari Schaffer, and Zhangjun Fei. Characterization of the Or regulatory network in melon for carotenoid biofortification in food crops. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7594408.bard.

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The general goals of the BARD research grant US-4423-11 are to understand how Or regulates carotenoid accumulation and to reveal novel strategies for breeding agricultural crops with enhanced β-carotene level. The original objectives are: 1) to identify the genes and proteins in the Or regulatory network in melon; 2) to genetically and molecularly characterize the candidate genes; and 3) to define genetic and functional allelic variation of these genes in a representative germplasm collection of the C. melo species. Or was found by the US group to causes provitamin A accumulation in chromoplas
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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, 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 t
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Steffenson, B. J., I. Mayrose, Gary J. Muehlbauer, and A. Sharon. ing and comparative sequence analysis of powdery mildew and leaf rust resistance gene complements in wild barley. United States-Israel Binational Agricultural Research and Development Fund, 2021. http://dx.doi.org/10.32747/2021.8134173.bard.

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Our overall, long-term goal is to exploit the genetic diversity present in cereal wild relatives for the development of cultivars with durable disease resistance. Our specific objectives for this proposal were to: 1) Utilize Association Genetics Resistance Gene Enrichment Sequencing (AgRenSeq) to identify and clone powdery mildew and leaf rust resistance gene complements in wild barley and 2) Conduct comparative sequence analyses of the cloned resistance genes to elucidate the basis of their specificity and evolution. The deployment of resistant cultivars is the most effective, economically ef
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Zhang, Hongbin B., David J. Bonfil, and Shahal Abbo. Genomics Tools for Legume Agronomic Gene Mapping and Cloning, and Genome Analysis: Chickpea as a Model. United States Department of Agriculture, 2003. http://dx.doi.org/10.32747/2003.7586464.bard.

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The goals of this project were to develop essential genomic tools for modern chickpea genetics and genomics research, map the genes and quantitative traits of importance to chickpea production and generate DNA markers that are well-suited for enhanced chickpea germplasm analysis and breeding. To achieve these research goals, we proposed the following research objectives in this period of the project: 1) Develop an ordered BAC library with an average insert size of 150 - 200 kb (USA); 2) Develop 300 simple sequence repeat (SSR) markers with an aid of the BAC library (USA); 3) Develop SSR marker
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Tucker, Mark L., Shimon Meir, Amnon Lers, Sonia Philosoph-Hadas, and Cai-Zhong Jiang. Elucidation of signaling pathways that regulate ethylene-induced leaf and flower abscission of agriculturally important plants. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7597929.bard.

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The Problem: Abscission is a highly regulated process, occurring as a natural terminal stage of development, in which various organs are separated from the parent plant. In most plant species, the process is initiated by a decrease in active auxin in the abscission zone (AZ) and an increase in ethylene, and may be accelerated by postharvest or environmental stresses. Another potential key regulator in abscission is IDA (Inflorescence Deficient in Abscission), which was identified as an essential peptide signal for floral organ abscission in Arabidopsis. However, information is still lacking re
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Heifetz, Yael, and Michael Bender. Success and failure in insect fertilization and reproduction - the role of the female accessory glands. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7695586.bard.

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The research problem. Understanding of insect reproduction has been critical to the design of insect pest control strategies including disruptions of mate-finding, courtship and sperm transfer by male insects. It is well known that males transfer proteins to females during mating that profoundly affect female reproductive physiology, but little is known about the molecular basis of female mating response and no attempts have yet been made to interfere with female post-mating responses that directly bear on the efficacy of fertilization. The female reproductive tract provides a crucial environm
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Meir, Shimon, Michael S. Reid, Cai-Zhong Jiang, Amnon Lers, and Sonia Philosoph-Hadas. Molecular Studies of Postharvest Leaf and Flower Senescence. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7592657.bard.

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Original objectives: To understand the regulation of abscission by exploring the nature of changes of auxin-related gene expression in tomato (Lycopersicon esculatumMill) abscission zones (AZs) following organ removal, and by analyzing the function of these genes. Our specific goals were: 1) To complete the microarray analyses in tomato flower and leaf AZs, for identifying genes whose expression changes early in response to auxin depletion; 2) To examine, using virus-induced gene silencing (VIGS), the effect of silencing target genes on ethylene sensitivity and abscission competence of the lea
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Fluhr, Robert, and Maor Bar-Peled. Novel Lectin Controls Wound-responses in Arabidopsis. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7697123.bard.

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Innate immune responses in animals and plants involve receptors that recognize microbe-associated molecules. In plants, one set of this defense system is characterized by large families of TIR–nucleotide binding site–leucine-rich repeat (TIR-NBS-LRR) resistance genes. The direct interaction between plant proteins harboring the TIR domain with proteins that transmit and facilitate a signaling pathway has yet to be shown. The Arabidopsis genome encodes TIR-domain containing genes that lack NBS and LRR whose functions are unknown. Here we investigated the functional role of such protein, TLW1 (TI
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Cohen, Roni, Kevin Crosby, Menahem Edelstein, et al. Grafting as a strategy for disease and stress management in muskmelon production. United States Department of Agriculture, 2004. http://dx.doi.org/10.32747/2004.7613874.bard.

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The overall objective of this research was to elucidate the horticultural, pathological, physiological and molecular factors impacting melon varieties (scion) grafted onto M. cannonballus resistant melon and squash rootstocks. Specific objectives were- to compare the performance of resistant melon germplasm (grafted and non-grafted) when exposed to M. cannoballus in the Lower Rio Grande valley and the Wintergarden, Texas, and in the Arava valley, Israel; to address inter-species relationships between a Monosporascus resistant melon rootstock and susceptible melon scions in terms of fruit-set,
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Ueti, Massaro Wilson, and Monica Leszkowicz Mazuz. Identification, characterization and testing of geographically conserved Babesia bovis vaccine antigen candidates. United States-Israel Binational Agricultural Research and Development Fund, 2022. http://dx.doi.org/10.32747/2022.8134143.bard.

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During the development of this project, we selected four potential B. bovis antigens for a subunit vaccine to prevent the clinical signs of acute bovine babesiosis. Selection of the target antigens was based on: (1) profile of expression in parasite blood stages; (2) prediction for protein location on the parasite surface and/or on the surface of infected red blood cells; and (3) target conservation between US and Israeli strains of B. bovis. Following these criteria, the B. bovis targets BBOV_IV009170, BBOV_III007410, BBOV_II001790, and BBOV_III008720 were selected. Full-length genomic sequen
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