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of diverse PRRSV strains causing extreme pathogenesis and disease. Transcriptomics datasets have been generated via bulk and single-cell RNA sequencing from blood and disease-affected tissues and will be
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
involving physiological, biochemical, and molecular experiments and the use of numerous techniques such as RNA-seq, data mining of DNA and protein sequence databases, analysis of gene function via CRISPR/Cas
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
sequence databases, analysis of gene function via CRISPR/Cas-9-mediated genome editing and RNAi, heterologous protein expression, real-time qRT-PCR, and plant transformation using species such as Arabidopsis
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assays for malaria and other parasitic diseases such as Babesiosis and Chagas disease. A major part of the project involves next generation sequencing (NGS) including use of the Oxford Nanopore Technology
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DNA (eDNA) metabarcoding data. This will also generate robust, accessible workflows for both short-read sequencing and long-read (e.g. Oxford Nanopore MinION) datasets. Learning Objectives: Under
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to characterize the structure and function of plant, insect, and fungal genomes related to pecan cultivar development. Application of sequence resources from plants, pests, and pathogens will be directed towards
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Engineering, or comparable field). Degree must have been received within the past five years. Preferred skills: Knowledge of basic concepts in genomic sequence analysis and plant genetics. Unix scripting
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apply molecular biology concepts to characterize immunoglobulin-E (IgE) binding to food allergens. Learning Objectives: Under the guidance of your mentor, you will: Develop knowledge of the sequence and
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, geosciences, biology (non-medical), mathematics, engineering, computer or computational sciences, or specific areas of environmental sciences that are aligned with the mission of the Office of Science are
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of Rounds (ANOR) in alignment with occupational exposure limits (OELs) and action levels, and define algorithmic risk classifications for varying exposure scenarios. Collecting, analyzing, and interpreting