The Wheat lab is looking for Master’s students interested in learning how to use and evaluate bioinformatic tools critically. The vast majority of bioinformatic tools for genomic analysis are designed to work with human data, which is a problem when these tools are used for species having much higher levels of genetic variation. High levels of heterozygosity in datasets can be interpreted as errors and lead to high false positives or incorrect results in analyses where these effects are considered.

An example of some of our work is shown in Hornett and Wheat 2012 BMC Genomics. We were looking at the ability to generate high-quality RNA-Seq insights using data from Homo sapiens, along with sets of genes of increasing divergence from Homo sapiens. We effectively documented a robust way to quantify gene expression in species where traditional genomic tools did not exist (currently, this paper has 88 citations).

We are looking for bioinformatic students interested in running analyses on simulated and real data, to assess the effects increasing levels of heterozygosity has on the performance of tools involved in:

  • Genome assembly
  • Read mapping for RNA-Seq analysis
  • Re-sequencing mapping for SNP identification and population genetic analyses

Previous programming experience not required, but an interest to learn command line Linux scripting is a minimum.

We have our own in-house servers with extensive computational power to facilitate high throughput / parallel analyses.

Our goal will be to both train students and generate high-quality data that will result in published work and a high-quality Master’s thesis.

For more information, please contact Chris at:
Read more about the lab: here!