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(Toronto, ON – December 17, 2010) Ten years ago, scientists succeeded in deciphering the human genome—a process known as DNA sequencing that required reading the chains of more than three billion bases (i.e., nucleotides) that comprise our chromosomal DNA.
A precise and extensive task, sequencing the genome necessitated new analytical methods created by the field of bioinformatics, which includesidentifying all the genes in the genome and associating them with specific functions (the field of genomics), predicting the functions of the proteins they code (the field of proteomics), and comparing the roles of certain genes with those of other species.  
The National Institutes of Health defines bioinformatics as:
“Research, development, or application of computational tools and approaches for expanding the use of biological, medical, behavioural or health data, including those to acquire, store, organize, archive, analyze or visualize such data.”
As a whole, bioinformatics helps researchers ask increasingly complex questions in a systems-wide approach, allowing for the analysis of complex networks and pathways of protein interactions.  
“Over the past decade, there has been an explosion in genomic data, which offers an unprecedented view of natural processes and how they are subverted by disease,” says Dr. Jim Woodgett, Director of Research at the Lunenfeld. “However, our capacity to make sense of this wealth of data is largely dependent upon devising completely new computational tools to filter out the noise and, hence, reveal the underlying processes.”
For example, cancer researchers are using bioinformatic tools to understand how communication between cells and proteins goes awry in cancer; stem cell biologists rely on these technologies to organize data that sheds light on the process of generating stem cells; geneticists can better understand which genes confer risk of chronic inheritable illnesses, and; pathologists can aggregate multiple tests to assess which is the optimal therapy for treating a specific patient.
Researchers at the Lunenfeld are using bioinformatics to further their research into cancer and other illnesses. For example:
In the course of their research published earlier this year, Drs. Anne-Claude GingrasandMike Tyers also created an innovative computer tool called ProHits, for storing and analyzing mass spectrometry data, as well as a novel statistical method called SAINT for the analysis of protein interaction data. These bioinformatic research tools will allow researchers globally to conduct genome-wide studies of protein interactions and communication pathways in cells faster and more efficiently, helping to further their studies of cancer and other illnesses. “These databases and analyses platforms for protein interactions help make the entire scientific literature accessible to all researchers, for the generation of new ideas and hypotheses about how cells function in health and disease,” says Dr. Tyers. 
In 2009, Dr. Jeff Wrana and his team unveiled a new technology tool called DyNeMo that analyzes breast cancer tumours to determine a patient’s best treatment options. The tool can predict with more than 80 per cent accuracy a patient’s chance of recovering from breast cancer. The DyNeMo technology analyzes networks of proteins in cancer cells, and can be used to predict the outcome in a newly diagnosed breast cancer patient. In the future, this tool may be used to analyze other types of cancer and could be used to predict an individual’s response to particular drugs. “Cancers aren’t diseases of single genes, but a network,” says Dr. Wrana. “These differences, and through the use of technologies such as DyNeMo, allow us to predict the prognosis of a newly diagnosed breast cancer patient.”
Dr. Laurence Pelletier is using bioinformatic tools for image processing and analysis. He explains that computational technologies help accelerate or fully automate the processing, quantification and analysis of vast quantities of biomedical imagery— including pristine views of the cell, as well as its proteins and complex structures. “Modern image analysis systems help us make measurements from a large or complex set of images, by improving accuracy, objectivity or speed,” says Dr. Pelletier. Biomedical imaging is becoming increasingly important for both diagnostics and research.
Together, these developments in informatics tools are helping researchers ask increasingly complex questions out how our bodies should work as well as how we might rectify damages caused by diseases.

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