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NAViGaTOR feature
Using Appearence filters it is possible to map Features to widths, heights, colours, transparency and many other visual attributes.

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NAViGaTOR feature
In NAViGaTOR it is possible to load and manipulate networks with millions of connections.

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Help Conquer Cancer
is our own project on the World Community Grid, which will help improve the results of protein X-ray crystallography and thereby improve understanding of cancer initiation, progression and treatment.

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NAViGaTOR
is our software package for visualizing and analyzing protein-protein interaction networks. NAViGaTOR can query OPHID / I2D online databases of interaction data and display networks in 2D or 3D.

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NAViGaTOR example
Savas, S., Geraci, J., Jurisica, I., Liu, G. A comprehensive catalogue of functional genetic variations in the EGFR pathway: Protein-protein interaction analysis reveals novel genes and polymorphisms important for cancer research. Int J Cancer,125(6): 1257-65, 2009.

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NAViGaTOR example
Savas, S., Geraci, J., Jurisica, I., Liu, G. A comprehensive catalogue of functional genetic variations in the EGFR pathway: Protein-protein interaction analysis reveals novel genes and polymorphisms important for cancer research. Int J Cancer,125(6): 1257-65, 2009.

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NAViGaTOR example
Showing data from Cell paper, Fig 5 Global Sequencing of Proteolytic Cleavage Sites in Apoptosis by Specific Labeling of Protein N Termini

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NAViGaTOR example
Cox, B., Kotlyar, M., Evangelou, A., Ignatchenko, V., Ignatchenko, A., Whiteley, K., Jurisica, I., Adamson, L., Rossant, J., Kislinger, T., Comparative systems biology of human and mouse as a tool for modeling human placental pathology, Mol Sys Bio, 5, 279, 2009.
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NAViGaTOR example
Agarwal R., Jurisica, I., Cheng K.W., Mills G.B. The emerging role of the Rab25 small GTPase in cancer, Traffic, 2009. E-Pub July 23, 2009. In Press.

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News and Events

2014 Thomson Reuters' Highly Cited Researcher; Out of 117 in computer science and 3,215 world-wide in 21 fields of science.

Petschnigg, J., Groisman, B., Kotlyar, M., Taipale, M., Zheng, Y., Kurat, C., Sayad, A., Sierra, J., Mattiazzi Usaj, M., Snider, J., Nachman, A., Krykbaeva, I., Tsao, M.S., Moffat, J., Pawson, T., Lindquist, S., Jurisica, I., Stagljar, I. Mammalian Membrane Two-Hybrid assay (MaMTH): a novel split-ubiquitin two-hybrid tool for functional investigation of signaling pathways in human cells; Nat Methods, 11(5):585-92, 2014.

WCG MCM project

WCG HCC project update

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Lab Affiliations

 

 

 

 

 

 

 

 

 

Welcome to Jurisica Lab!

Igor Jurisica

Our research focuses on integrative computational biology in the context of Cancer Informatics.

It involves representation, analysis and visualization of high dimensional data generated by high-throughput biology experiments.

The laboratory is located at Princess Margaret Cancer Centre, University Health Network.

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Research Areas

Biomedical researchers use models of biological systems to integrate diverse types of information. This ranges from multiple high-throughput datasets, functional annotations and orthology data to expert knowledge about biochemical reactions and biological pathways. Such integrative systems are used to develop new hypotheses and answer complex questions such as what type of system perturbation may result in a desired change in cellular function; what factors cause disease; will patients respond to a given treatment, etc.

Precision medicine needs to be data driven and corresponding analyses comprehensive and systematic. We will not find new treatments if only testing known targets and studying characterized pathways. Thousands of human of potentially important proteins remain pathway or interactome "orphans". Computational biology methods can help fill this gap with accurate predictions, but the biological validation and further experiments are essential. Intertwining computational predicion and modeling with biological experiments will lead to more useful findings faster and more economically.

These computational predictions improved human interactome coverage relevant to both basic and cancer biology, and importantly, helped us to identify, validate and characterize prognostic signatures. Combined, these results may lead to unraveling mechanism of action for therapeutics, re-positioning existing drugs for novel use and prioritizing multiple candidates based on predicted toxicity, identifying groups of patients that may benefit from treatment and those where a given drug would be ineffective.

 

 

Visualization and Analysis of protein-protein interaction networks

Visualization and Analysis of protein-protein interaction networks
We develop novel graph theory based algorithms for visualization and systematic analysis of protein-protein interaction networks.
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High-throughput Protein Crystallography

High-throughput Protein Crystallography
We focus on image analysis methods for automatically detecting the presence of crystals in crystallization screens, data mining of resulting information and optimization.
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Genomic and Proteomic Cancer Profiles Analysis

Genomic and Proteomic Cancer Profiles Analysis
We are involved in developing and applying tools for analysis of genomic/proteomic cancer profiles including gene expression, CGH, protein expression, siRNA, miRNA.
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About Us

The primary research focus is on integrative computational biology, and representation, analysis and visualization of high-dimensional data generated by high-throughput biology experiments. Of particular interest is the use of comparative analysis in the mining of different dataset types such as protein-protein interaction, gene/protein expression profiling of cancer, and high-throughput screens for protein crystallization.

The main, overarching goal is to improve and personalize cancer treatment by making it possible to detect cancer earlier, identify high-risk patients, and to customize treatment. The first goal is to identify markers that can be used to detect cancer earlier; the second goal is to identify high-risk cancer patients; the third goal is to find markers that can predict treatment response.

Intelligent molecular medicine
Technologies to measure gene and protein expression offer the opportunity to evaluate large sets of genes and proteins in parallel, and improve our understanding of tumorigenesis and patient treatment. However, molecular screening alone is not sufficient to achieve intelligent molecular medicine. Computational advances and computing power to analyze, manage and use genomic/proteomic information is required to turn data into knowledge for hypotheses generation for further research or to render them readily comprehensible for patient outcome prediction and treatment selection. To be used effectively, molecular profiling must be applied at the individual patient level to allow personalized information-based medicine. Our focus is on algorithm and tools development, their application and evaluation.

Many techniques for the analysis of genomic/proteomic data are available, yet none offers an integrated and comprehensive approach, by combining results from gene/protein expression data in the context of protein protein interactions (PPIs). We address this bottleneck in multiple cancers by systematic, unbiased analysis and visualization of data integrated from multiple high-throughput platforms under the hypothesis that such information will create insight not appreciable from the component parts.

The results of this research will help to fathom biological mechanisms of cancer, and will be applicable to improve disease classification, diagnostic measures, therapy planning, and treatment prognosis. Improving the treatment could in turn improve quality of life for cancer patients. Using the proposed tools and methodology, physicians will have more relevant information available at the time of diagnosis and treatment planning, and the patient will have a better explanation of the disease, its origin, progression path and treatment alternatives.

Structure-function relationship in protein interaction networks
It has been established that despite inherent noise present in protein-protein interaction (PPI) data sets, systematic analysis of resulting networks uncovers biologically relevant information, such as lethality, functional organization, hierarchical structure and network-building motifs. These results suggest that PPI networks have strong structure-function relationship. We are developing novel graph theory based algorithms for systematic analysis of PPI networks (both predicted and experimentally determined). We use this information to build predictive models and to integrate this information with gene/protein expression profiles.

High-throughput protein crystallization
One of the fundamental challenges in modern molecular biology is the elucidation and understanding of the rules by which proteins adopt their three-dimensional structure. Currently, the most powerful method for protein structure determination is single crystal X-ray diffraction, although new breakthroughs in NMR and in silico approaches are growing in their importance.

Conceptually, protein crystallization can be divided into two phases: search and optimization. Approximate crystallization conditions are identified during the search phase, while the optimization phase varies these conditions to ultimately yield high quality crystals. Robotic protein crystallization screening can speedup the search phase, and has a potential to increase process quality. However, this requires an automated process for evaluating experiment results. We focus on automated image classification, data mining of resulting information when integrated with protein properties, using the information for crystallization optimization planning and screen optimization.