Daily Archives: December 18, 2014

Canadian researchers have developed a computer program that could prove the Rosetta Stone of genome researchand one day translate the building blocks of everything from cancer to autism.

Scientists have had a map of the genome essentially the recipe behind each humans DNA since 2003, when the multi-billion-dollar global Human Genome Project wrapped up. But theres still no proven method to read the entire genome. Scientists have mostly understood bits and pieces that affect one specific disease until now.

Most people focus on a single disease, said Brendan Frey, a University of Toronto engineering professor and lead researcher of the new study, in an interview. Our approach is to figure out how to read the genome. If you can read the genome, then you can understand all diseases.

Babak Alipanahi, Hannes Bretschneider, Brendan Frey and Hui Xiong discuss how the splicing code is used to understand disease mutations. (Jennifer Wilson/University of Toronto)

That research, titled The human splicing code reveals new insights into the genetic determinants of disease, appears today in the journal Science, but Canada.com was provided a sneak peak of the potentially groundbreaking discovery.

Before the Rosetta Stone, ancient historians could read a couple hieroglyphs or Egyption words; after it was discovered, they could understand all of it. This discovery is similar: Frey describes the genome like a recipe book and his work as a translator to help understand whether the mix-up occurred in the ingredients or the instructions so it could be prevented or better treated down the line.

Freys use of machine learning (essentially a form of artificial intelligence that makes predictive findings based on existing data) could revolutionize how researches parse the genome and how we think about and treat disease.

To prove that the computation model works, his team applied it to investigate the causes autism, colon cancer and spinal muscular atrophy, the leading cause of infant mortality. What they found not only proved the theory that the whole genome must be examined to understand its parts, but the researchers already made new breakthroughs. They discovered 39 new genes that contribute to or cause autism.

This is a very significant paper in showing that tools like this can be used to find relevant variation in the genome, said Robert Ringle of Autism Speaks, an organization that raises funds for autism research. The way researchers looked at the genome previously only looked at small parts of it. Technology has really limited the field for a number of years. Now, whole genome sequencing allows researchers to look at, in a cost effective way, every letter in the genome, Ringle said, referring to the new research released today. In that code there are going to be answers to what causes diseases, but you need something to decode that to understand.

And thats precisely what Freys team sought to achieve.

Original post:
Canadians reveal 'Rosetta Stone' for human genome

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University of Toronto researchers from Engineering, Biology and Medicine teach computers to read the human genome and rate likelihood of mutations causing disease, opening vast new possibilities for medicine

Newswise (Toronto, ON Dec. 18) In the decade since the genome was sequenced in 2003, scientists and doctors have struggled to answer an all-consuming question: Which DNA mutations cause disease?

A new computational technique developed at the University of Toronto may now be able to tell us.

A Canadian research team led by professor Brendan Frey has developed the first method for ranking genetic mutations based on how living cells read DNA, revealing how likely any given alteration is to cause disease. They used their method to discover unexpected genetic determinants of autism, hereditary cancers and spinal muscular atrophy, a leading genetic cause of infant mortality.

Their findings appear in todays issue of the leading journal Science.

Think of the human genome as a mysterious text, made up of three billion letters. Over the past decade, a huge amount of effort has been invested into searching for mutations in the genome that cause disease, without a rational approach to understanding why they cause disease, says Frey, also a senior fellow at the Canadian Institute for Advanced Research.

This is because scientists didnt have the means to understand the text of the genome and how mutations in it can change the meaning of that text. Biologist Eric Lander of the Massachusetts Institute of Technology captured this puzzle in his famous quote: Genome. Bought the book. Hard to read.

What was Freys approach? We know that certain sections of the text, called exons, describe the proteins that are the building blocks of all living cells. What wasnt appreciated until recently is that other sections, called introns, contain instructions for how to cut and paste exons together, determining which proteins will be produced. This splicing process is a crucial step in the cells process of converting DNA into proteins, and its disruption is known to contribute to many diseases.

Continued here:
Machine Learning Reveals Unexpected Genetic Roots of Cancers, Autism and Other Disorders

PUBLIC RELEASE DATE:

18-Dec-2014

Contact: Lindsay Jolivet lindsay.jolivet@cifar.ca 416-971-4876 Canadian Institute for Advanced Research @cifar_news

Scientists and engineers have built a computer model that has uncovered disease-causing mutations in large regions of the genome that previously could not be explored. Their method seeks out mutations that cause changes in 'gene splicing,' and has revealed unexpected genetic determinants of autism, colon cancer and spinal muscular atrophy.

CIFAR Senior Fellow Brendan Frey (University of Toronto) is the lead author on a paper describing this work, which appears in the Dec. 18 edition of Science Express. The paper was co-authored by CIFAR senior fellows Timothy Hughes (University of Toronto) and Stephen Scherer (The Hospital for Sick Children and the University of Toronto) of the Genetic Networks program. Frey is appointed to the Genetic Networks program, and the Neural Computation & Adaptive Perception program. The research combines the latter groups' pioneering work on deep learning with novel techniques in genetics.

Most existing methods examine mutations in segments of DNA that encode protein, what Frey refers to as low-hanging fruit. To find mutations outside of those segments, typical approaches such as genome wide association studies take disease data and compare the mutations of sick patients to those of healthy patients, seeking out patterns. Frey compares that approach to lining up all the books your child likes to read and looking for whether a particular letter occurs more frequently than in other books.

"It doesn't work, because it doesn't tell you why your kid likes the book," he says. "Similarly, genome-wide association studies can't tell you why a mutation is problematic."

But looking at splicing can. Splicing is important for the vast majority of genes in the human body. When mutations alter splicing, genes may produce no protein, the wrong one or some other problem, which could lead to disease.

Frey's team, which includes researchers from engineering, biology and medicine, developed a computer model that mimics how the cell directs splicing by detecting patterns within DNA sequences, called the 'splicing code'. They then used their system to examine mutated DNA sequences and determine what effects the mutations would have, effectively scoring each mutation. Unlike existing methods, their technique provides an explanation for the effect of a mutation and it can be used to find mutations outside of segments that code for protein.

To develop the computer model, Frey's team fed experimental data into machine learning algorithms, so as to teach the computer how to examine a DNA sequence and output the splicing pattern.

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'Deep learning' finds autism, cancer mutations in unexplored regions of the genome