Jenna Woody

Interdepartmental Genetics Graduate Program
College of Agriculture
Iowa State University
Ames, Iowa 50011 USA

Education

Ph.D., Interdepartmental Genetics, Iowa State University, 2012 Minor: Statistics
Major Advisors: Drs. William Beavis and Randy Shoemaker

Work Responsibilities

• Currently my focus is on ultra-high-throughput, whole-genome expression analyses and evolutionary structural genomics with a focus of statistics. A post-doctorate, Andrew Severin, and I wrote homemade scripts to align and normalize the output from next generation Solexa sequencing of RNA samples from 14 different tissues. With this data, I was given the freedom to create tools to allow us both novel and automated ways of mining such a large amount of information. As a side focus, we wanted these tools to be accessible to people with no programming knowledge. One tool I created was a GO tool that takes a list of gene models and matches these gene models to their predicted GO annotation, performs a Fisher’s Exact test on the data in comparison to the GO annotations for the whole genome, performs a Bonferroni correction and then gives you a list of the under- and over-represented GO terms. I also created a tool that takes a list of gene models and determines if and where these genes may be significantly clustered across the genome. This tool uses a bootstrap analysis and also accounts for gene density. At the moment I am working on creating a new script that performs a sliding window analysis that identifies significant clusters of similar genomic properties. The goal is to be able to identify patterns of increased/decreased properties such as gc content, recombination rates, average expression level, gene density, etc. across the genome in a visual format. My last paper focused on the correlation between expression level and expression breadth and the physical properties of genes (intron sizes, exon sizes, intergenic regions, gc content). I have now moved on to identifying and characterizing the isochore regions of soybean. The goal is to identify if there are “neighborhoods” in which the expression evolves in a coordinated manner. With this I will be looking at syntenic regions in the soybean genome as well as comparing these clusters to other species (Medicago, Arabidopsis, rice). I am working closely with Bill Beavis (my co-major professor) in developing a bayesian model to categorize the isochore regions by GC content and heterogeneity. As a side project, I am analyzing the rate of divergence in individual genes and using a multivariate model to identify any correlation between an increase in expression divergence with GC content, exon size, intron size, intron number, expression level or expression breadth. My goal is to focus on gaining new statistical tools as well as learning more about the molecular tools used to make these high-throughput datasets.
  
  

Graduate Coursework

• Statistics 401
• Statistics 402
• Biochemistry 404
• BCB 596: Genomic Data Processing
• GDCB 510: Transmission Genetics
• GCDB 511: Molecular Genetics
• EEOB 563: Molecular Phylogenetics
• Statistics 447
• Bayesian statistics
  

Awards

Won poster award at the 13th biennial Molecular and cellular biology of the soybean conference 2010.

Publications

Jenna L. Woody, Andrew J. Severin, Yung-Tsi Bolon, Bindu Joseph, Brian W. Diers, Andrew D. Farmer, Nathan Weeks, Gary J. Muehlbauer, Rex T. Nelson, David Grant, James E. Specht, Michelle A. Graham, Steven B. Cannon, Gregory D. May, Carroll P. Vance, and Randy C. Shoemaker. 2011. Gene expression patterns are correlated with genomic and genic structure in soybean. Genome 54(1):10-8.

Severin AJ, Woody JL, Bolon YT, Joseph B, Diers BW, Farmer AD, Muehlbauer GJ, Nelson RT, Grant D, Specht JE, Graham MA, Cannon SB, May GD, Vance CP, Shoemaker RC. 2010. RNA-Seq Atlas of Glycine max: a guide to the soybean transcriptome. BMC Plant Biol. 10:160.