February 21, 2019

New step-by-step protocol for common omics analyses fills 10-year gap in training resources

Juri Reimand

Expert group develops comprehensive guide for the interpretation and visualization of gene lists, replacing outdated, decade-old protocols

The importance of understanding biological pathways – or how our genes work together – is becoming increasingly evident, but pathway analysis remains a major challenge for many basic and biomedical researchers. Current computational tools can help simplify this analysis, but there is no established guide or standard for using these tools in practice.

To fill this gap, a team of experts from OICR and the Bader Lab at the University of Toronto recently published a comprehensive, step-by-step guide to pathway enrichment analysis that brings together their highly-recommended tools into one protocol. The complete protocol, which is now published in Nature Protocols, can be performed in less than five hours and can be used by researchers with no prior training in bioinformatics or computational biology.

“These days, almost every omics study needs to include pathway enrichment analysis, but it has been over a decade since a comprehensive protocol for these analyses has been published,” says Dr. Jüri Reimand, Principal Investigator at OICR and co-lead author of the protocol. “Our new methods are designed to guide researchers through their analyses and serve as a practical resource for their studies.”

Each step of the protocol is supported with detailed instructions and valuable troubleshooting information, which were designed in large part by Ruth Isserlin, co-lead author and Senior Bioinformatics Analyst in the Bader Lab.

Recently, the methods were used to identify a therapeutic target for ependymoma, a prevalent type of childhood brain cancer that is notoriously difficult to treat. The pathway analysis, as described in Nature, led to a better understanding of why most ependymoma treatments are not effective and revealed a new treatment option that could stop the progression of the disease.

“Future cancer research discoveries rely on our understanding of biological pathways,” says Reimand. “This protocol provides a resource from which we can build our understanding and explore previously uncharted relationships between our genes.”

August 3, 2018

Open source in open science: Accelerating cancer research (Part 2)

Part 2 of Open source software

Find part 1 here: Open source in open science: Accelerating cancer research

OICR researchers have contributed to major open source projects available to the global research community in order to accelerate cancer research. Click the link below to read about more of OICR’s open source software projects.

Continue reading – Open source in open science: Accelerating cancer research (Part 2)

August 1, 2018

Open source in open science: Accelerating cancer research (Part 1)

Open source

In the effort to bring better disease prevention and treatment to patients faster, cancer researchers are thinking more creatively about ways to conduct high-quality scientific research. Concerns about the quality, efficiency and reproducibility of research have motivated the open science movement – the growing trend of making data, methods, software and research more accessible to the greater scientific community.

Open source software (OSS), a major component of open science, enables research groups to reduce redundant efforts in software engineering by sharing software code and methods. In addition to improving efficiency, OSS promotes high-quality research by enabling collaboration, and helps make research easier to reproduce by making it more transparent.

Continue reading – Open source in open science: Accelerating cancer research (Part 1)

December 8, 2015

For Dr. Jüri Reimand, data tells a story

Dr. Jüri ReimandThere are many ways to tell a story, but Dr. Jüri Reimand likes to tell stories in a different way – with data. Reimand, a new OICR Investigator in the Informatics and Bio-computing Program, is a computer scientist by training who developed a keen interest in human biology and disease.

While growing up in Estonia, Reimand always had an interest in computers and understanding how they work. This led him to the University of Tartu to pursue a degree in computer science. When nearing the end of his undergraduate studies Reimand was looking for a thesis topic when he happened to meet a “young, kind of cool professor” who was working to set up a bioinformatics program from scratch. Reimand joined his team and worked to interpret large-scale data and gene lists.

Continue reading – For Dr. Jüri Reimand, data tells a story