2024 Faculty Mentors

Jeff Anderson, PI

The Anderson lab studies chemical signaling that occurs between plants and pathogenic bacteria. We are particularly interested in how pathogenic bacteria use plant-derived chemicals as cues to begin their infection process. We study signaling responses in both plant and bacteria using a combination of genetic, biochemical, metabolomic, transcriptomic and proteomic approaches.

Lab website: https://bpp.oregonstate.edu/users/jeff-anderson 

Contact

• Email: Jeff.Anderson@oregonstate.edu 
• Phone: (541)-737-4076

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. 

Learning

Students will learn fundamental concepts in plant biotic interactions, genetics, biochemistry, cellular information flow, information flow and logic in data analysis pipelines, and statistics. Application of the scientific method to analyze data sets (i.e. hypothesis development and testing) will be emphasized throughout. Additional training in molecular biology and microbiology techniques may occur depending on the specific project.

Maude David, PI

Dr. Maude David's laboratory studies the gut-brain axis, to understand how microbes can impact our behavior, specifically in Autism Spectrum Disorder and Anxiety. She uses a crowd-sourced approach to collect lifestyle type information, diet habits, and samples. Her team is also working on identifying bottlenecks in microbial ecology and bioinformatics, bringing novel solutions to unravel microbial molecular mechanisms by optimizing new molecular methods and improving massive sequencing data annotation.

Lab website: https://microbiology.science.oregonstate.edu/maude-david

Contact

• Email: maude.david@oregonstate.edu

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.

Learning

Students will learn fundamental concepts in Soil and/or Human Gut Microbiome analysis. The laboratory mainly uses R, python and the student will help develop workflow in collaboration with other students to help the team implement new analysis and tools.

John Fowler, PI

The Fowler lab studies the molecular mechanisms that govern cellular morphogenesis and development in plants. Broadly, we are interested in how cellular processes – for example, exocytosis – are integrated into developmental systems at the organismal scale. The lab uses a variety of complementary techniques in genetics, genomics, transcriptomics, cell biology and (more recently) computational analysis of digital images to investigate these processes. The lab’s current focus is on the male gametophyte of Zea mays, including pollen and the growing pollen tube, structures required for crop plant reproduction and seed generation. Our current challenge is to utilize large scale datasets (e.g., from transcriptomics) to predict phenotypic outcomes (e.g., which genes exert the largest influence on reproductive success?).

Lab website: https://bpp.oregonstate.edu/users/john-fowler-jr

Contact

• Email: fowlerj@science.oregonstate.edu
• Phone: (541)-737-5307

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.

Learning

Students will learn fundamental concepts in plant genetics, molecular and cellular biology. Students will use genomic and/or high content digital imaging data to learn the scientific process.

Niklaus Grunwald, PI

The Grunwald lab is interested in the epidemiology, genetics and evolution of exotic and reemerging Phytophthora species. The genus Phytophthora contains some of the most destructive plant pathogens that affect agricultural and forest crops. Important examples include P. ramorum, a devastating exotic pathogen causing sudden oak death, and P. infestans, known as the cause of the Irish potato famine. Much of our work focuses on translational applications towards improving disease management in agriculture. Our team combines basic tools from genomics, epidemiology and bioinformatics, with translational research approaches to strategically address some of the fundamental challenges posed by plant diseases caused by the genus Phytophthora. We have and continue to sequence whole genomes of important Phytophthora pathogens.

Lab website: http://grunwaldlab.cgrb.oregonstate.edu/

Contact

• Email: grunwaln@oregonstate.edu
• Phone: (541)-738-4049

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. Select publications exemplifying our research approaches include:

• https://doi.org/10.3389/fgene.2018.00123

• https://doi.org/10.1094/MPMI-10-17-0258-R

• https://doi.org/10.1094/MPMI-10-17-0258-R

Learning

Students will learn fundamental concepts in characterizing genomes and populations using high throughput sequencing data and computational approaches. Students will use genomic data to learn the scientific process.

Brent Kronmiller, PI

The Center for Quantitative Life Sciences Bioinformatics and Data Science group conducts bioinformatics research and consulting across Oregon State University.  Eight bioinformatics analysts and trainers work on research projects such as RNAseq analysis, genome assembly, gene prediction, environmental sequence analysis, COVID research, and bioinformatics programming in a wide range of scientific domains.

Lab website: https://cqls.oregonstate.edu/bioinformatics

Contact

• Email: brent.kronmiller@cqls.oregonstate.edu
• Phone: (541)737-3611

Project

A variety of projects are available. Broadly, students can learn to use biological computing to test hypotheses, develop computational methods to process and/or manage large datasets, or test efficacy of computational methods that are currently under development.

Learning

Students will learn fundamental concepts in biological computing, best practices for working on shared, large computing infrastructure, and skills for collaborating within research teams. Application of the scientific method to analyze large data sets.

Sam Leiboff, PI

We use mutant plant varieties to understand the genes necessary to make a normal, healthy corn plant. We use 1) next generation sequencing, long-read Oxford Nanopore, and chromatin structural sequencing to identify the genetic changes that cause mutations while we 2) capture comprehensive image-based measurements and fit mathematical models to compare normal and mutant development. Our work is split 60/40 between indoor laboratory work and field-based sampling in our nearby corn nursery .

Lab website: Sam Leiboff

Contact

• Email: leiboffs@oregonstate.edu

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.

Learning

Students will design and execute hypothesis-driven research with a variety of techniques. Over the summer, students will learn and deploy basic to advanced concepts in genetics and plant development, including mutant analysis of organ production and mapping-by-sequencing.

• Molecular training will include DNA extraction, PCR, restriction digest, Sanger sequencing, with potential preparation of Illumina sequencing libraries.
• Computational training will include the design or deployment of analytical pipelines to align genomic DNA sequences to a refence genome, identify sequence variants, map mutations using variant allele frequency, automated image processing, and statistical shape description with R.
• Field training will include pedigree management, sampling logistics, and fundamentals of maize propagation.

Joey Spatafora, PI

Our research is focused on evolutionary biology of fungi with emphases in phylogenetics and comparative genomics across a diversity of taxonomic and ecological systems.  There are currently four main focus areas, all of which seek to use genome-scale data and phylogenetic methodology to address questions in fungal evolutionary biology. 1) The Zygomycetes Genealogy of Life (ZyGoLife) - the Conundrum of Kingdom Fungi seeks to understand numerous questions involving organismal and genomic evolution of fungi hypothesized to represent some of the earliest lineages to colonize land. 2) 1000 Fungal Genomes (1KFG) Project is designed to use genome data from across the Kingdom Fungi to address numerous questions regarding major fungal ecologies and nutritional modes. 3) Evolution of Insect Pathogenic Fungi has been a focus of our lab for more than 20 years.  Much of our recent research seeks to understand patterns and processes that have facilitated host jumps and evolution of novel ecologies. 4) Systematics and Population Biology/Ecology of Ectomycorrhizal Fungi is one of the more active areas of research in mycology. We are using genome scale data to understand patterns between Rhizopogon, a type of truffle, and its host trees. 

Lab website: https://joeyspataforalab.weebly.com/

Contact

• Email: spatafoj@oregonstate.edu
• Phone: (541)-737-8134

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.  Recent publication that exemplify the types of work the student will do are in Bushley et al. (2013), Quandt et al. (2015, 2018), Mujic et al. (2019) and Chang et al. (2019); these are provided in the Spatafora lab website under publications.

Learning

Students will learn fundamental concepts in fungal biology, information flow, and genomics. Students will use genomic data to learn the scientific process. Students will work closely with postdoctoral researchers and graduate students.

Alexandra Weisberg, PI

The Weisberg lab studies the interactions between bacteria and plants. We use whole genome sequencing of bacterial pathogens to study the evolution and ecological bases for plant-microbe interactions. We also study the "flexible" genome of pathogens; these are dynamic regions that can be swapped between very diverse bacteria and potentially add or alter traits.

Lab website: https://weisberglab.com/

Contact

• Email: Alexandra.Weisberg@oregonstate.edu

Project

Students will learn to process and analyze genomic data of plant pathogenic bacteria. Tasks may include assembling genome sequences, constructing phylogenies, identifying genes of interest, and using genomic data to generate hypotheses on the evolution of pathogens.

Learning

Students will learn fundamental concepts in bacterial evolution, plant-microbe interactions, and genomics.

Jessie Uehling, PI