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Through the computational analysis of genome-wide data, we aim to elucidate mechanisms regulating transcription and RNA processing, their interplay with genetic and epigenetic factors, and how they cause disease. Our main focus is on cancer and brain development, two areas where regulation of gene expression plays a major role, and where unprecedented amounts of data are being produced. Key components of our research are (a) the development of novel strategies to integrate genomic, epigenomic and transcriptomic data across species and disease models, (b) the modeling of dynamic changes over normal cellular differentiation to guide interpretation of disease data, and (c) the use of single-cell technologies to resolve cell-to-cell variation, identify rare cells driving disease progression, and define the effect of the microenvironment in molecular phenotypes.
David S. Guttman is a Professor in the University of Toronto Department of Cell & Systems Biology and Director of the Centre for the Analysis of Genome Evolution & Function. His research focuses on deciphering how bacteria adapt to and manipulate their hosts, emphasizing the evolution of bacterial host specificity and virulence and the dual role of secreted pathogen effectors as both virulence factors and immune elicitors. His group is particularly fascinated by the scope and impact of natural genetic diversity on these host-microbe interactions. The Guttman lab uses a multidisciplinary approach that harnesses comparative and evolutionary genomics, genetics, molecular biology, microbiology, plant biology, pathology, bioinformatics, and statistical genetics to gain insight into how pathogen evolution influences the outcome of host-pathogen interactions.
Over the past 30 years Dr. Wishart has conducted world-leading research in many areas, including bioinformatics, metabolomics, structural biology and machine learning. He has also made important contributions to medical diagnostics, agri-food research, environmental science and analytical chemistry. Dr. Wishart is considered one of the early pioneers in the field of metabolomics and has played a foundational role in the development of bioinformatics and cheminformatics in North America. Based on his many important contributions to metabolomics, Dr. Wishart was made a lifetime fellow of the Metabolomics Society in 2014, the society’s highest honour. In recognition of his outstanding accomplishments in bioinformatics, metabolomics and structural biology, he was elected as a Fellow of the Royal Society of Canada (2017), received a University of Alberta Alumni award (2018) and was appointed as a Distinguished University Professor (2018). He has developed a number of techniques based on NMR spectroscopy, mass spectrometry, liquid chromatography and gas chromatography to characterize the structures of both small and large molecules. As part of this effort, Dr. Wishart has led the “Human Metabolome Project” (HMP), a multi-university, multi-investigator project that is cataloguing all theknown chemicals in human tissues and biofluids. Using a variety of analytical chemistry techniques along with text mining and machine learning, Dr. Wishart and his colleagues have identified or found evidence for more than 250,000 metabolites in the human body. This information has been archived on a freely accessible web resource called the Human Metabolome Database (HMDB). Dr. Wishart has also been using machine learning and artificial intelligence to help create other useful chemistry databases, such as DrugBank, FooDB and ContaminantDB and software tools (such as MetaboAnalyst, CFM-ID and BioTransformer) to help with the characterization and identification of metabolites, drugs, pesticides and natural products. Over the course of his career Dr. Wishart has published more than 500 research papers in high profile journals on a wide variety of subject areas. These papers have been cited over 120,000 times.
As an advocate of artificial intelligence, I am passionate about integrating AI techniques and algorithms into my research to extract meaningful insights from vast and diverse datasets. By employing advanced bioinformatics tools, I analyze multiomic data, integrating genomics, transcriptomics, proteomics, and epigenomics, to gain a comprehensive understanding of the cannabis plant and its biological processes. Furthermore, I am well-versed in the field of genome editing, utilizing state-of-the-art techniques such as CRISPR-Cas9 to engineer precise modifications in the cannabis genome. My research in this area aims to unlock the plant’s potential for medicinal, industrial, and agricultural applications. Through my teaching endeavors, I am committed to nurturing the next generation of computational biologists, equipping them with the skills necessary to thrive in the era of big data and artificial intelligence.
Dr. Gaiti is an early career investigator at the Princess Margaret Cancer Centre, Assistant Professor in the Dept. of Medical Biophysics at the University of Toronto, and Early Career Research Affiliate at OICR. He earned his PhD in evolutionary biology and genomics from the University of Queensland (Australia) in 2017, where he focused on understanding the evolutionary origin of two major players in human gene regulation: long non-coding RNAs and chromatin marks. As a postdoctoral fellow in the laboratory of Dr. Dan Landau at Weill Cornell Medicine and New York Genome Center, he studied the epigenetic determinants of cancer evolution using novel single-cell multi-omics experimental and computational approaches in blood disorders and brain tumors. These works have been published in highly esteemed journals including Nature, Nature Genetics, and Nature Communications. His work has been further recognized by prestigious federal agencies (NIH, CIHR, NSERC) and awards, including the Emerging Leaders in Computational Oncology Award and the Ontario Institute Cancer Research Investigator Award. Dr. Gaiti research program is focused on developing and applying single-cell multi-omics approaches to answer the fundamental question of how malignant cellular states are jointly determined by genetic and epigenetic alterations, aiming to develop novel therapeutic strategies to directly anticipate and address cancer evolutionary capacity.
Finlay Maguire is a jointly appointed Assistant Professor in Community Health & Epidemiology and Computer Science at Dalhousie University and Pathogenomics Bioinformatics Lead at the Shared Hospital Laboratory. His lab primarily works on developing and applying novel microbial bioinformatics and machine learning approaches to better understand the diagnosis, evolution, and dynamics of infectious diseases. This includes active projects on antimicrobial resistance, outbreak control, and characterisation of novel zoonoses in both clinical and public health contexts. Beyond this, he also engages in a broad range of collaborative data science projects in the areas of computational social science and clinical epidemiology.
Gary Bader is a Professor at The Donnelly Centre at the University of Toronto and an expert in Computational Biology. The Bader lab is developing computational methods and an ecosystem theory of tissue function that considers cell-cell interactions, cell growth, and cell internal mechanisms, such as pathways, reactions, and causal relationships, to help understand development, cancer and regenerative wound healing processes.
Dr. Gary Van Domselaar, Ph.D. (University of Alberta, 2003) is the Section Chief for Bioinformatics at the National Microbiology Laboratory in Winnipeg, Canada and Associate Professor in the Department of Medical Microbiology at the University of Manitoba. Dr. Van Domselaar’s lab develops methods and pipelines to understand, track, and control circulating infectious diseases in Canada and globally. His research and development activities span metagenomics, infectious disease genomic epidemiology, genomic surveillance, genome annotation, population structure analysis, and microbial genome-wide association studies.
Burger is a member of the Robert-Cedergren Centre for Bioinformatics and Genomics, teacher in graduate bioinformatics education programs, and full professor in Biochemistry at the Universite de Montreal.
Gregory Butler is Professor emeritus of Computer Science and Software Engineering at Concordia University, Montreal, Canada. He is a founder of the Centre for Structural and Functional Genomics at Concordia where he directs the development of the bioinformatics platform for large-scale fungal genomics projects. His research focuses on advanced IT for knowledge-based bioinformatics, including scientific data management, algorithms, text mining, ontologies and the semantic web. Dr Butler is a founding member of the Canadian Semantic Web Interest Group.
Dr. Schwartz is a Scientist at the Princess Margaret Cancer Centre and Assistant Professor in the Department of Medical Biophysics at the University of Toronto focused on bioinformatics and computational biology. He received his Ph.D. in Biomedical Engineering at Drexel University and completed his postdoctoral fellowship at the Perelman School of Medicine in the University of Pennsylvania. His current research involves developing computational methods to understand the role of cellular diversity and evolution in cancer and leveraging this knowledge to improve diagnosis and treatment.
Dr. Bourque’s research interests are in comparative and functional genomics with a special emphasis on applications of next-generation sequencing technologies. His lab develops advanced tools and scalable computational infrastructure to enable large-scale applied research projects.