Advisor: Dr. Stefan Keller
Co-advisor: Dr. Dan Ashlock
Research: The adaptive immune response plays a key role in recognizing pathogens and determines health and disease in vertebrates. The protection mechanism of adaptive response is mediated by the antigen receptors (ARs) on the surface of lymphocytes. Specifically, an individual’s AR repertoire helps understand, diagnose and treat diseases. The data are from previously collected Canine immune repertoire sequencing data under normal and pathological conditions. The data analysis for this research will be confined to clustering of AR sequences (clonotypes) and a network map that displays the functional association of clonotypes within a given data set will be constructed. Finally we hope a database that integrates sequencing data from various projects based on variables such as species, disease, tissue, etc.. would be created.
Advisor: Dr. Rod Merrill
Co-advisor: Dr. Stefan Kremer
Research: Bacterial mono-ADP-ribosyltransferase (mART) toxins are a large group of protein toxins that contribute to disease in plants, animals and humans. These toxins have a negative impact on both clinical health and agricultural industries. The decline in antibiotic effectiveness is driving the characterization of virulence factors such as mART toxins as targets for the development of new pharmaceuticals. Bioinformatics search tools represent a more effective and time/cost effective approach to mART toxin discovery, which will accelerate downstream research efforts to find treatments for bacterial disease and infections. The Merrill lab has developed and honed an in-silico strategy for identifying new mART toxins from the genomes of pathogenic bacteria. The search combines fold recognition strategies with a pattern-based primary sequence search, which reduces reliance on sequence similarity and advances us toward true structure-based mART toxin family expansion. Previously, M. Binf. student, Sara Latour, created an automated pipeline for mART toxin searches called “automART”. The main purpose of the pipeline was to search and filter recently available bacteria genomes to identify new mART toxins. My project objective is to enhance automART as follows: (i) add a solubility test application; (ii) make automART easier to use by transitioning it into a web service; and (iii) apply automART to mine new mART toxins from recently sequenced bacterial genomes.
Advisor: Dr. Lewis Lukens
Research: My research focuses on RNA-dependent DNA methylation (RdDM) in maize, a process with significant implications for epigenetic variation and special importance in suppression of transposable elements, which make up ~85% of the maize genome. I am examining the difference in methylation patterns and expression levels between wild-type plants and those with a knocked out RNA-dependent RNA polymerase RdR2.
Min Ru (Vicky) Lin
Melissa Mac Leod-Bigley
Advisor: Dr. Elizabeth Boulding
Research: My research focuses on creating a high-density consensus genetic linkage map for North American Atlantic salmon (Salmo salar) using a custom 50K SNP chip. This project involves creating linkage maps for several large full-sibling families of S. salar, in which SNPs are placed relative to one another based on how likely they are to be inherited together. This kind of map allows for better interpretation of how informative SNPs and their associated traits may be inherited. Linkage maps from different families are then "merged" together to find an overall consensus location for each marker to improve the accuracy of the map. My research takes advantage of highly informative data sets in order to create a larger and more comprehensive linkage map for North American S. salar than has previously been published.
Advisors: Dr. Elizabeth Lee and Dr. Andy Robinson
Research: My research uses bioinformatics tools to determine ancestry of maize germplasm pools.
Advisor: Dr. Mehrdad Hajibabaei
Co-advisor: Dr. Brian Golding (McMaster University)
Research: I am developing a framework to assess the transcriptomic response of species to environmental toxins. This framework relies on curated toxicogenomics studies and incorporates a comparative and a machine learning approach to identify evolutionarily conserved genes and pathways among the input organisms responding to the target toxic stressors.
Advisors: Dr. Zeny Feng, Dr. Peter Kim
Research: My research is on the association between bacteriophage engraftment following fecal microbiota transplantation (FMT) and resolution of Clostridium difficile infection (CDI) in human patients. CDI frequently recurs in patients after traditional antibiotic therapies, so FMT has come to the fore as an effective alternative treatment. However, the exact mechanism behind the effectiveness of FMTs as well as their longterm effects require further study, so I am investigating possible correlations between the bacteriophages transferred from donors to patients and the patients' treatment outcomes using metagenomic sequencing, bioinformatic analysis pipelines, and machine learning techniques.
Advisor: Dr. Sarah Adamowicz
Research: My Masters research is focused primarily on developing software pipelines for the analysis of DNA barcoding data from BOLD. Current research focuses on:
- Informatics approaches to studying the relationship between rates of molecular evolution and the latitudinal biodiversity gradient using geographical coordinate data and COI barcoding data.
- Informatics approaches to studying Arctic Dipteran bio-geography and community similarity across the regions of Canada, Greenland and Norway.
- Development and field validation of software used in primer design of in-situ qPCR assays of Trichopteran species in Churchill, Mb.
Advisors: Dr. Steffen Graether, Dr. Dan Ashlock
Research: My research involves determining the evolutionary origins of dehydrins, which are intrinsically disordered plant proteins associated with osmotic stress.
Advisor: Dr. Dan Ashlock
Research: Investigating alternatives to previous hierarchical clustering methods in an attempt to improve the stability of output trees. This includes exploring the new Bubble Clustering algorithm developed for my Masters as well as exploring possible alternative methods for measuring the association between points.