We chatted with Dr. Brenda Oppert, research molecular biologist at the USDA ARS Center for Grain and Animal Health Research in Manhattan, Kansas, about her work in the ares of coleopteran storage pests and functional genomics.

Tell us about your work!

I work at the Stored Product Insects and Engineering Research Unit at the ARS Center for Grain and Animal Health Research, Manhattan, KS. Our mission is to find better, safer ways to control insect pests found in and around food storage areas, including grain storage and processing facilities and warehouses. I use a functional genomics approach to identify vulnerabilities in stored product pests that can be exploited for the development of control products based on biological pesticides (proteins, RNAi, etc). Our primary focus is on coleopteran storage pests, including the red flour beetle (the genetic model for coleopterans), yellow mealworm (a biochemical model), and lesser grain borer. We have a sequenced genome for the red flour beetle, and we are working with Australian collaborators to sequence the lesser grain borer genome. We have transcriptome projects in all three insects. Our challenges have been how to store, transfer, and analyze data with minimal resources.

How has DNASTAR software helped you?
 We’ve developed a computer infrastructure based on in-house MacPros and iPlant for data storage and transfer (and some analysis programs). However, our workhorses have been ArrayStar for transcriptome analysis (differential gene expression), and Lasergene Genomics Suite for genome assembly. We’ve also used MegAlign for evolutionary analysis and Lasergene’s Structural Biology Suite for tertiary structure predictions of proteins lacking a crystal structure. We find that the customer support is unparalleled and has been critical in designing workflows and working within our existing computer infrastructure.

What does DNASTAR software do best, in your opinion?
 As I said, ArrayStar has been instrumental in providing detailed analysis of differentially expressed genes in a transcriptome analysis. With minimal effort, the output immediately provides you with scatter graphs, tables, and statistical analysis, and options of such features like Venn diagrams and heatmaps, all publication quality. I was able to analyze a dataset and compare it to an analysis by a bioinformaticist and determine that genes of interest were not included in the latter analysis. Basically I know what I am looking for and can vary the parameters to more accurately reflect the biological processes in the insect. We’ve also found that SeqMan NGen, the assembler in Lasergene, provides better assemblies with data from some platforms, including Ion Torrent PGM.

Can you speak to DNASTAR’s support for you and your work?
 The technical support is instrumental to our success in analyzing transcriptome and genomic data. In the beginning we needed more support in project design, “tweaking” analysis, or data interpretation. For example, with assembly projects, we match our data to the amount of RAM and processors; sometimes it is necessary to either split data into multiple files, or prefilter using ArrayStar (Thank you Matt, who taught me how to do this). Now, we share that information with other lab members and colleagues. We find that support is quick, usually the same day.

Is there anything else you’d like to share?
Without DNASTAR, we would not have been able to progress in the analysis of our high throughput sequencing data. Resources for bioinformatics are not yet available through ARS. We have purchased multiple licenses and usually have them fully engaged. Thank you for this valuable resource!