Graduate position: SouthDakotaStateU.EvolutionaryGenomics

Master’s Student Opportunity at the CBFenster Lab, SDSU

Project: Predict mutational effects using comparative genomic approaches

Research Area: Evolutionary Genomics of mutation at Arabidopsis thaliana

Location: South Dakota State University, Department of Biology and
Microbiology/Department of Mathematics and Statistics, Brookings, SD

Mutations, the ultimate source of all genetic variation, provide the
substrate that fuels evolution.  However, most mutational input to genetic
variation is subsequently eliminated by selection or drift in natural
populations.  Why some mutations are eliminated and others preserved or
fixed in natural populations and whether there is a correlation between
the preservation of a given mutation and the magnitude of the mutation
effect are key questions in biology.

Equipped with the most comprehensive mutation profile of a plant species,
Arabidopsis thaliana, the CBFenster lab (
in collaboration with Xijin Ge’s lab (, both
at South Dakota State University, provides a great opportunity for
graduate students to study spontaneous mutations using computational
tools.  The collaboration reflects a joint mentoring opportunity
from biological and mathematical/statistical perspectives and will
include mentoring by Dr. Mao-Lun Weng, a postdoc on the project
( Sequence data reflect a joint
collaboration among the Fenster (SDSU), Rutter (CoC), Weigel (Max Planck)
and Wright (U of Toronto) labs, funded by NSF.

The prospective student will investigate the effect of mutation at
protein-coding genes from protein structure and gene network perspectives.
Given an observed spontaneous mutation in mutation accumulation study,
the student will: (1)Use protein structure prediction algorithms
to simulate the protein structure from the mutated sequence and test
whether the mutation has strong effects on protein structure stability.
(2)Using a gene expression network investigate whether the mutation has
a potentially large effect on network connectivity.

We hypothesize deleterious mutations will detrimentally change protein
structure or be associated with proteins having high network connectivity.
We can validate these hypotheses by comparing the mutated protein-coding
genes in A. thaliana to other related species. If the mutated position
in the protein-coding gene also shows sequence variation among
related species, it suggests that this mutation did not have strong
effects, i.e. less deleterious.  Furthermore, we can compare overlap
of these mutations in the mutation accumulation study and in natural
populations. If mutations are deleterious, as predicted by protein
structure stability, they are less likely to be present in natural

This is a bioinformatics oriented project. The prospective student will
obtain skills of computational approaches to study protein structure
and gene network, and learn phylogenetic and population genetic theories
on mutations.

Students can begin as early as January 2018, but more likely summer or
fall 2018.

Funding will include teaching assistantship support and NSF funded
summer salary.

Please email all mentors if you are interested in the project: