People
My research involves developing, improving and applying statistical methods for genetic, genomic and high dimensional data. My over 200 publications include both theoretical developments and applied collaborative projects, collectively cited over 18,000 times. I am Senior Investigator at the Lady Davis Institute for Medical Research (www.ladydavis.ca). At McGill University, I hold a James McGill Professorship, I am co-Director of the Ludmer Centre for Neuroinformatics and Mental Health (ludmercentre.ca), and also the inaugural and current Graduate Program Director of the interdisciplinary PhD in Quantitative Life Sciences (www.mcgill.ca/qls). I am a former president of the International Genetic Epidemiology Society (www.geneticepi.org) and received their Leadership Award in 2022.
Dr. Chad Laing is a Research Scientist and the Head of Bacteriology Research at the National Centre for Animal Diseases, Lethbridge Laboratory, within the Canadian Food Inspection Agency. His research focuses on predictive genomics of bacterial pathogens to improve the health of animals and humans, as well as the safety and security of Canadians. He has dealt extensively with molecular biology, bacterial genomics, bioinformatics, machine learning, and software development. His current research focuses on combining large-scale genomic and metagenomic sequencing with machine learning to enable more rapid identification and characterization of microbial pathogens, including antimicrobial resistance and virulence. He has supervised many graduate and undergraduate students, and is an adjunct professor in the Department of Biological Sciences at the University of Lethbridge.
Chaitra is a computational biologist with experience in software development. During her PhD, she applied mathematical modelling, network analysis and multi-omics integration to study complex diseases. She has contributed to open-source toolboxes (openCOBRA) and developed softwares (EFMviz & ComMet) to analyse genome-scale metabolic models. She currently works in the Bader lab’s MODiL team (Multi Omics Data Integration and Analysis) and with groups at PMCC, where she develops pipelines to analyse various omics data types and discover new drug targets in cancer.
I’m an Assistant Professor in the Division of Oncology, where my focus is on developing and applying computational tools to provide insight into the origins and progression of cancer. I earned Bachelor degrees in Biology and Computer Science from Truman State University and my PhD in Computational Biology from Baylor College of Medicine.
My core research interests include understanding the clonal architecture of tumors and how they evolve in response to therapy, with a special focus on hematologic cancers. I also study effective design and targeting of cancer immunotherapies, developing open-source software for interpreting and visualizing genomic data, and integrative analysis that translates multi-dimensional genomic data into both functional and actionable contexts.
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 works in Guillaume Bourque’s lab on software solutions in bioinformatics for organizing, visualizing and analyzing datasets produced by large-scale projects such as the International Human Epigenome Consortium (IHEC), which maps human epigenomes for a broad spectrum of cell types and diseases. He is also involved in the development of GenAP, a platform that leverages Compute Canada infrastructure to make bioinformatics analysis more accessible to non-bioinformaticians, and reduces data processing bottlenecks.
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. Eduardo Taboada is an internationally recognized expert on the molecular epidemiology and genomics of Campylobacter jejuni. In 1999 he completed a Ph.D. in molecular genetics at the University of Ottawa and joined the National Research Council, to work on C. jejuni genomics. Since joining the Public Health Agency of Canada’s as a Research Scientist in 2006, he has developed a research programme focusing on bacterial comparative genomics, genome dynamics and the application of genomics approaches towards the study of the molecular surveillance and epidemiology of priority food- and water-borne bacterial pathogens. He leads the Campylobacter Genomics Laboratory at the National Microbiology Laboratory and is head of the Genomic Epidemiology Research Unit. In addition of being a co-principal investigator of a Genome Alberta-funded project on large-scale sequencing on Campylobacter in the Canadian poultry chain, he is a co-investigator on a Genome Canada-funded project on AMR emergence, transmission and ecology and a work package leader in the Government of Canada’s Genomics Research Development Initiative interdepartmental project on AMR.
Emma Griffiths is a research associate at the Centre for Infectious Disease Genomics and One Health (CIDGOH) in the Faculty of Health Sciences at Simon Fraser University in Vancouver, Canada. Her work focuses on developing and implementing ontologies and data standards for public health and food safety genomics to help improve data harmonization and integration. She is a member of the Standards Council of Canada and leads the Public Health Alliance for Genomic Epidemiology (PHA4GE) Data Structures Working Group.