Field Spotlight: Computational and Systems Biology

Thank you to all who contributed content to this spotlight!

Many of our PIs are involved in cutting-edge and innovative research in the fields of bioinformatics and computational and systems biology. Here are some examples:

The Abelson lab generates and analyzes multi-omics data with the goal to improve our current knowledge of cancer's etiology, discover new biomarkers and facilitate personalized cancer treatment approaches. In addition, the group seeks to bring sequencing methodologies and bioinformatics tools for clinical use and develop methods for cancer diagnosis, early detection and prevention. For example, the group introduced Espresso, a sequencing error suppression technique, demonstrating superior performance in detecting residual disease in the peripheral blood of patients with acute myeloid leukemia following therapy. Another development is SmMIP-tools, a computational method that enhances the detection of insertion, deletions and point mutations. The group also developed a cost-effective sequencing assay to detect clonality of the hematopoietic system. The assay is extensively used by the lab and collaborators who seek to detect clonal hematopoiesis and study its clinical association with ageing and disease. 

The Awadalla lab develops computational and bioinformatics tools/models and utilizes population genomics and big data to address how genetic and environmental factors influence human diseases. Ongoing research projects include nationwide molecular and statistical genomic epidemiology projects and genome sequencing of new and old-world Plasmodium falciparum and other related malarial species isolates. Previous work from the group included demonstrating how air pollution influences gene expression and disease and how clonal hematopoiesis correlates with severe COVID-19 infection in cancer patients. They are also part of the 1000 Genomes Project, the most comprehensive catalogue of human genetic variation, and developed the VCFtools software suite to store and process DNA polymorphism data.  

The Bader lab is a computational biology lab focusing on systems biology at the cell and tissue level. They have made many contributions to network and pathway analysis and interpretation of genomics data to support biological discovery and precision medicine applications. More recently, the group 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 the development, cancer and regenerative wound healing processes. 

The Campbell lab specializes in the application of machine learning, data science and statistics in biomedicine, such as single-cell and cancer genomics. The group developed the CellAssign and CloneAlign statistical programs. They also composed covariate Gaussian latent variable models, a type of probabilistic machine learning method. Currently, they are utilizing machine learning to integrate DNA and RNA sequencing data to observe how tumour clonal identity impacts gene expression. The group also develops methods to characterize tumour microenvironments automatically from single-cell RNA sequencing data. Dr. Campbell is also the Canada Research Chair in Machine Learning in Translational Biomedicine. 

The Reimand lab focuses on computational biology and cancer research. The group analyzes large pan-cancer datasets for biological discoveries and develops computational tools and machine learning methods for these analyses. Their projects are in three major areas: drivers and passengers of the cancer genome, multi-omics data integration, and discovery of biomarkers. They recently analyzed the non-coding cancer genome to find putative cancer-driving mutations in the PCAWG project and discovered non-coding RNA genes that help predict patient survival in multiple cancer types. The group also creates computational tools such as ActivePathways to detect important molecular signals across multiple omics datasets and combine these into bio-pathways and interaction networks.  

One of the specialties of the Röst lab is using mass-spectrometry-based quantitative proteomics and metabolomics to study molecular behaviour directly. The group co-developed their own proteomics method called SWATH-MS, substantially increasing accuracy and throughput. To complement this, they also developed an analysis software called OpenSWATH which extracts data from the SWATH-MS maps. More recently, the group has expanded this approach to ion-mobility enhanced mass spectrometry, increasing the number of quantified protein in a single run to unprecedented numbers. Using mass spectrometry, the group also studies small molecules and lipids and how metabolism changes in the context of diabetes, cancer and pregnancy. Using a combination of computational and experimental approaches, the Röst lab applies novel concepts from systems biology and personalized medicine to better understand human disease. Dr. Röst is also the Canada Research Chair in Integrative Computational Biology. 

The Roth lab aims to develop technologies that discover, characterize and chart genes and their variants, based on their function, pathways and medical implications. The group developed several software applications such as Varity, SILVER, ChromoZoom, MaveRegistry, MaveQuest and MaveVis. They also collaborated on the effort to assemble and characterize a comprehensive collection of codon-optimized SARS-CoV-2 coding sequences. Other examples of the group’s work include constructing a reference interactome map of human protein interactions (HuRI) and gaining insight into how specific mutations in the MTHFR gene make some more vulnerable to folate deficiency.  

The Stein lab specializes in network and pathway-based analysis of multiple cancer types and adaptive oncology. Dr. Stein himself was involved in multiple major bioinformatics projects, such as developing the first physical clone map of the human genome, the Wormbase database for C. elegans and the open-access Reactome database for reactions and pathways. Some of their publications include releasing cloud-based ChIP-Seq processing tools for analysis of mod-ENCODE (stands for Model Organism Encyclopedia of DNA Elements) data and the Phase II HapMap, which defines over 3 million human single nucleotide polymorphisms (SNPs).  

The Zhang lab utilizes bioinformatics and computational approaches to conduct projects in comparative genomics, genome evolution and microRNAs. The group is currently developing algorithms to enhance methods predicting and analyzing transcription factor binding sites and utilizing computational and experimental approaches to study noncoding segments of the genome and RNA transcripts. They also study evolutionary genomics, with a focus on repetitive elements and increase in gene families in vertebrates, the impact of horizontal gene transfer, and the evolution of paralog genes due to whole-genome duplication.