What are you interested in?

Here is a summary of the available projects for the 2019 program.


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 (not currently accepting students).


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: 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. Learn more.


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: 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. Learn more.


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. Learn more.


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. Learn more.


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. Learn more.


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. Learn more.


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. Learn more.