A postdoctoral research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Stored Product Insect and Engineering Research Unit in Manhattan, Kansas through ORISE (Oak Ridge Institute for Science and Education).  

Stored product insects are an evolutionarily diverse group of organisms that are attracted to similar food volatiles, yet very little is known about the identity or function of the chemosensory receptors that these insects use to detect the odors of potential food resources. Better knowledge of the underlying genetics and physiology of chemoreception in pest insect species could lead to novel control methods to reduce the approximately $2.5 billion dollars of post-harvest losses to U.S. agriculture that these insects contribute to every year. Under the guidance of a mentor, the fellow will take part in comparative genomics analysis of chemosensory genes in stored product beetle genomes to determine whether convergent adaptations in closely related taxa primed these insects for attraction to similar pheromones and food volatiles. The fellow will have access to genomes of 15 stored product insect species for this project, accompanying RNA-Seq data, and the ability to generate new population resequencing data, RNA-Seq data, or de novo genome assemblies to address this topic. Chemosensory genes are inherently difficult to predict and annotate due to their unconventional gene structure with long introns and short exons. The fellow will also have the ability to explore new machine learning algorithms to predict protein-coding sequences and substrate preferences. The fellow will also collaborate with members of the USDA ARS Ag100Pest team to apply these tools to the genomes of other pest insects that have been sequenced by the USDA. (Ag100Pest is an initiative to sequence the genomes of over 100 economically important pest insect species.)

The participant will learn about processes and procedures for analyzing high-throughput sequencing data, including genome assembly, RNA-Seq analysis of complete samples, ab initio gene prediction, and population genetics analysis. Skills in Python, R, and the Unix command line will be emphasized, and leadership and collaborative skills will be learned through participation in SCINet working groups and participating in collaborative research. In addition to gaining experience with publishing peer-reviewed papers and presenting research for a scientific audience, the fellow will also have the opportunity to take self-paced online courses to learn and improve their skills in R, Python, or other data analysis tools.

Applications may be submitted through Zintellect:

https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2024-0299