DormanResearch : RotationProjects?

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Here is a listing of summer projects and rotation projects in our lab.  Please contact me external link if you are interested.  My webpage external link and wiki homepage external link.

02009 Projects

0Compare methods for identifying population structure.

Population structure is within-population inhomogeneity caused by geographic or other barriers that restrict gene flow.  In this project, you will compare the ability of several methods (STRUCTURE,  BAPS, distance methods, and our phyloclustering technique) for detecting population structure (number of clusters and cluster membership) in simulated data where population structure is known.

Possible skills needed for or learned from this project include:

Papers to read:
Chen, C.; Forbes, F. and Francois, O. FASTRUCT: model-based clustering made faster Molecular Ecology Notes, 2006, 6, 980-983.
Baccam, P.; Thompson, R. J.; Fedrigo, O.; Carpenter, S. and Cornette, J. L. PAQ: Partition Analysis of Quasispecies Bioinformatics, 2001, 17, 16-22
Corander, J.; Waldmann, P. and Sillanpa, M. J. Bayesian analysis of genetic differentiation between populations. Genetics, 2003, 163, 367-374
Corander, J.; Waldmann, P.; Marttinen, P. and Sillanpa, M. J. BAPS 2: enhanced possibilities for the analysis of genetic population structure. Bioinformatics, 2004, 20, 2363-2369
Pritchard, J. K.; Stephens, M. and Donnelly, P. Inference of population structure using multilocus genotype data. 2000, 155, 945-959

Expected results:

0MeDIP analysis using R.

MeDIP? is a technique for identifying CpG? methylation status in genomes.  Methylation status is an epigenetic (non-genetic, but sometimes heritable) change that impacts phenotype, including disease/cancer.  Here, we propose to develop methods to handle the CpG? methylation status dependence structure along the genome.  The dependence is caused by experimental (probe or sequence read overlap) and biological (sequential methylation) reasons.

Possible skills needed for or learned from this project include:

Papers to read.
Jacinto, Ballestar, and Esteller (2008)
Down, T. A.; Rakyan, V. K.; Turner, D. J.; Flicek, P.; Li, H.; Kulesha, E.; Graf, S.; Johnson, N.; Herrero, J.; Tomazou, E. M.; Thorne, N. P.; Backdahl, L.; Herberth, M.; Howe, K. L.; Jackson, D. K.; Miretti, M. M.; Marioni, J. C.; Birney, E.; Hubbard, T. J. P.; Durbin, R.; Tavare, S. & Beck, S. A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nat Biotechnol, 2008, 26, 779-785

Expected results:

0Old Projects

0Rabies Dynamics in Zimbabwe

We will use population genetics techniques to infer the past history of rabies virus.  The data are rabies sequences sampled from infected animals (dogs, jackals, etc.) in Zimbabwe.
Questions of interest:

0Detecting selection in sequences with overlapping reading frames

Methods to detect evidence of selection from sequence data have been developed and used widely. Commond methods include those of Li et al. (1985), Nei & Gojobori 1986), and Yang and Nielsen (2000). These methods have been used to show that viral sequences are subject to strong diversifying selection during infection thanks to the continuous pressure of the immune system. Unfortunately, the application of these methods to viral sequences is hampered by the fact that many regions of compact viral genomes encode overlapping reading frames. Although some have applied single frame methods like those mentioned above to study these regions, the conclusions are suspect. We have developed a simple statistical technique for evaluating selection pressures acting on overlapping reading frames. In this project, you will test the method in simulation and apply the method to several viral data sets.
Question(s) of interest:

Li, Wu, and Luo (1985) external link
Nei and Gojobori (1986) external link
Yang and Nielsen (2000) external link

0Analysis of an agent-based simulation of Leishmania infection

Computer simulations of the immune response to an infectious agent provide a novel way of understanding the relationship between pathogen and host at the system level. Our lab is developing and using an agent-based model of Leishmania infection to help understand why C3HeB/FeJ? mice are resistant to one species of the parasite, L. major, but are susceptible to another, L. amazonensis. Students will work with this model and analyze results in order to qualitatively characterize the types of simulated immune responses produced and will assess the sensitivity of simulation outcome to model parameters. This project will introduce students to agent-based models and the design and analysis of computer experiments from a statistical vantage point. Students will also have opportunities for expanding upon this model (experience in C/C++ required). Results from this project will be used for gaining insight into differences between L. major and L. amazonensis and to increase the robustness and accuracy of the simulation.


Dancik, GM, Jones, DE and Dorman, KS. An agent-based model for Leishmania infection. 2006. Manuscript submitted for publication. external link

0Detecting Dramatic Selection Events in Phylogenetic Trees

The rate of evolution varies spatially along genomes and temporally in time.  The presence of evolutionary rate variation is an informative signal that marks functional regions of genomes and historical selection events.  There exist many tests for temporal rate variation that start by partitioning sampled sequences into two or more groups and testing rate homogeneity among the groups.  We have developed a Bayesian method to infer phylogenetic trees with a divergence point, or dramatic temporal shifts in selection pressure that affect many nucleotide sites simultaneously, located at an unknown position in the tree.  There are two possibilities for this project
Questions of interest:

Dorman, K. S., Identifying dramatic selection shifts in phylogenetic trees, in BMC Evolutionary Biology (2006), Accepted download
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