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Here is a summary of the available projects for the 2020 program. Back to program home.


PI: Jeff Anderson

Project: Students will learn to process and analyze transcriptomic, proteomic or metabolomic datasets. This project may also include hands-on experience in collecting and preparing samples in the laboratory for data analysis. Learn more.


PI: Posy Busby

Project: Students will generate and analyze fungal and bacterial amplicon (short DNA sequences that can be used as barcodes to distinguish between organisms) sequence data representing plant-associated microbial communities (e.g., leaf, wood, seed microbiomes). Learn more.


PI: Jeff Chang

Project: Students will learn to process and analyze whole genome sequence data. Tasks may include assembling genome sequences, constructing phylogenies, identifying genes of interest, and/or using genomic data to generate hypotheses on transmission and evolution. Learn more.


PI: Maude David

Project: Student will learn to process and analyze soil or gut microbiome (human or mice) sequencing data. Tasks may include 16S (short DNA sequences that can be used as barcodes to distinguish between organisms) data analysis (microbial structure) as well as genome sequence assembly, public datasets curation to apply machine learning, and/or multivariate analysis from the Human Microbiome Project, in the context of gut brain-axis. Learn more.


PI: Laurent Deluc

Project: Students will learn to process and analyze whole genome sequence data. Tasks may include assembling genome sequences (microvine), manually curating the functional annotation of gene models, designing, synthesizing, and cloning for CRIPSR experiments. Learn more.


PI: John Fowler

Project: Students will help choose from two possible projects: 1) developing approaches for automated analysis of digital images of phenotypes, i.e., ‘computer vision’; or 2) processing and analyzing whole genome sequence data to determine the genomic location of certain types of mutations that could influence plant development and reproduction. Tasks may include generation of training sets for machine learning and image analysis; genome and gene model sequence analysis; and/or using transcriptomic data to generate hypotheses regarding gene function. One recent publication from the lab exemplifies our genomic and transcriptomic work: Chettoor et al. 2014. Learn more.


PI: Ken Frost

Project: Students will learn to process and analyze amplicon sequence data to profile soil microbial communities in an agricultural field experiment or as a function of observed independent variables. Learn more.


PI: Aymeric Goyer

Project: Students will learn to process and analyze whole transcriptome and/or genome sequence data. Tasks will include mapping reads to reference genome, differential expression analysis, visualization of data, and identification of single nucleotide polymorphism (not currently accepting students).


PI: Niklaus Grunwald

Project: Students will learn to process and analyze whole genome sequences and population genetic data. Tasks may include assembling genome sequences, calling variants in populations, identifying genes of interest, and/or using genomic data to generate hypotheses on pathogen emergence. Learn more.


PI: Pankaj Jaiswal

Project: Students will learn to design an experiment on stress treatment of a crop plant, collect samples for transcriptome sequencing, generate, manage process and analyze the data. Students will also learn the Biocuration process to synthesize known prior biological knowledge about the genes, biochemical pathways and gene regulation which will help in data analysis and building hypothesis. Learn more.


PI: Sam Leiboff

Project: Students will use advanced genetic pedigrees to map and characterize mutant maize phenotypes. Mapping tasks will include experimental work to prepare DNA for sequencing and/or the bioinformatic analysis of the map data to identify potential causative mutations. Mutant characterization will include high-throughput genotyping with molecular markers, field-based measurements of plant growth, high-throughput imaging of live plant tissue followed by computational image processing and modeling to identify and model mutant vs normal plant tissue shape. Learn more.


PI: Jared LeBoldus

Project: Students will learn to process and analyze whole genome sequence data. Tasks may include calling variants, constructing phylogenies, identifying genes of interest, and/or using a variety of population genomic measures to generate and test hypotheses with respect to evolution (not currently accepting students).


PI: Aaron Liston

Project: Students will learn to process and analyze genome sequence data. Research opportunities include genetic linkage mapping of sex determination, population genetic analyses, and phylogenetic analysis of interspecific hybridization. Learn more.


PI: Molly Megraw

Project: Students will learn to process and analyze transcriptomic datasets. Tasks may include use of straightforward data mining and visualization methods to examine gene sets of interest. Learn more.


PI: Chris Mundt

Project: Students can choose one of two tracks. One track would involve the use of a computer simulation model to study factors that influence the spread of disease in space and time, and how disease control practices might limit this spread. A second track would involve genetic analysis of field data collected from molecular mapping populations exposed to plant diseases (not currently accepting students).


PI: David D. Myrold

Project: Students would learn about diversity and function of soil microbial communities by analyzing bacterial and fungal amplicon (short DNA sequences that can be used as barcodes to distinguish between organisms) sequences, and shotgun metagenome (the collection of all genomes with a community of microorganism) sequences (not currently accepting students).


PI: Lloyd Nackley

Project: Students will learn to how integrate sensing technologies to optimize plant production. Sensors will include ground-based measurements as well as aerial (drone) based sensors. The sensors will be used to quantify how changes in management practices, such as increasing or decreasing irrigation and fertilizers, will affect plant growth and performance (not currently accepting students).


PI: Sushma Naithani

Project: Students will utilize a data set available from the Plant Reactome database that contains data for ~300 pathways from 79 species to analyze evolution of metabolic pathways across the broad spectrum of plants and photoautotrophs; identify unique pathways associated with different plant families and set of universally conserved pathways. Students will also learn the biocuration of genes and pathways which will help in data analysis. Learn more.


PI: Thomas Sharpton

Project: Students will learn the interdisciplinary process of generating and analyzing gut microbiome data. Tasks may include conducting DNA extraction from samples collected from animals, sequencing DNA, using bioinformatic procedures to analyze DNA sequences, and applying statistical methods to generate hypotheses about how the microbiome’s composition relates to the health of its host. Learn more.


PI: Joey Spatafora

Project: Students will learn to process and analyze whole genome sequence data. Tasks may include assembling genome sequences, annotating genome sequences, constructing phylogenies, identifying genes of interest, and/or using genomic data to generate hypotheses on fungal organismal and genomic evolution. Learn more.


PI: Brett Tyler

Project: Students will learn to process and analyze whole genome sequence data and mRNA sequencing data. Tasks may include identifying genes of interest, checking that the computer software has correctly identified the genes, constructing phylogenies, checking when and how actively the pathogens are using the genes, and predicting the functions of genes (not currently accepting students).


PI: Kelly Vining

Project: Students will learn to process and analyze whole genome sequence data. Tasks may include assembling genome sequences and identifying genes of interest. Students may also engage in work to study gene expression and validating markers for breeding purposes (not currently accepting students).


PI: Jessie Uehling

Project: Students will learn to process and analyze whole genome sequence data. Tasks may include: isolating and sequencing fungal DNAs; assembling, and analyzing fungal genomes and transcriptomes; identifying genetic variation; phylogenomic analyses; assessing population structure and diversity; and evaluating evolutionary hypotheses using population diversity metrics from whole genome sequencing data. Learn more.


PI: Timothy Warren

Project: The REEU student will conduct analysis of behavioral data of continuous flight orientation of the spotted-wing fruit fly, an agricultural pest. The student will develop a capacity for Python programming, as well as time-series analysis. Furthermore, the student will learn to query and manipulate large data sets via Unix shell commands. There may be additional opportunities to conduct laboratory experiments with flying flies. These experiments would provide students the opportunity to use Arduino devices and the ROS programming environment for real-time experimental control. Learn more.