Inferring gene regulatory networks


A major challenge in current post-genome molecular biology and bioinformatics research is to fully understand gene regulation: how and when each and every gene is switched on and off. Gene regulation is essential to organism survival, and deciphering how genes are regulated is critical for understanding genetic misregulation, a major cause of disease and developmental malformations. The aim of our project is to develop a novel machine learning system that combines diverse recently-generated biological evidence to infer transcriptional networks. Expected outcomes associated with this research program are:


  1. 1)to improve our understanding of how cells control gene expression, which plays a key role in human disease;

  2. 2)to uncover previously unknown regulatory interactions;

  3. 3)to develop an enabling technology for biologists to further analyze their own data; and

  4. 4)to develop novel computer programs to model gene regulation.

Protein functional characterization in non-model organisms such as Atlantic Cod and Atlantic Salmon


Genomic studies of Atlantic cod and salmon (and potentially other fish species) can help the aquaculture industry by enabling the identification of genetic markers for commercially-desirable traits, and by assessing the genetic consequences of interbreeding between farmed and wild populations. However, although the gene sequences of fish such as Atlantic cod and Atlantic salmon are known, the biological role of most of their genes is unknown. Knowing the biological role of genes is crucial for interpreting the results of large-scale genomic studies, particularly expression profiling experiments. For example, researchers may identify in a expression profiling study a list of genes involved in fish response to changes in water temperature. Lack of information about the biological function of most of those identified genes hinders follow-up experimentation and analysis, limiting the amount of information obtained from large-scale fish genomic studies.


Our research aims at improving the understanding of the biological function of fish genes and to facilitate the biological interpretation of genomic studies in fish by developing computational tools to improve the functional and structural annotation of poorly-annotated fish species. This research builds upon fish genomics infrastructure (such as cDNA libraries, EST databases, and  fish cDNA microarrays) that is already in place in Canada and specifically in Newfoundland. Expected outcomes associated to this research are:


  1. 1)increasing our knowledge of the biological role of fish genes;

  2. 2)improving our understanding of evolutionary relationships between fish and other organisms;

  3. 3)enhancing the interpretation of genomic studies in fish; and

  4. 4)facilitating the identification of genetic markers useful in aquaculture genetics and stock breeding, as well as in the management of natural populations of fish species.

Integrative pathway analysis of heterogeneous whole-genome data in Diabetes Mellitus type 1


Type 1 diabetes mellitus (T1DM) is a common disease with a strong autoimmune mechanism that destroys the insulin-producing cells of the pancreas. The onset of T1DM occurs commonly in childhood, and its cause is not completely understood. The goal of this project is to integrate genomic and genetic data to decipher the molecular basis of T1DM. Expected outcomes associated to this project are:


  1. 1)increasing our understanding of the pathogenesis of T1DM;

  2. 2)identifying biochemical pathways altered in the development of T1DM;

  3. 3)improving our understanding of the relationships between genetic variations and transcriptional profiles in T1DM; and

  4. 4)prioritization of biochemical pathways and genes associated to T1DM for deeper investigations.