Category Archives: Genome

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

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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.

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

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Can the Subaltern Genome Code? Rethinking race, science, and subjectivity - Ruha Benjamin
The Johannesburg Workshop in Theory and Criticism (under the umbrella of WISER) and the Seminar in Experimental Critical Theory (UCHRI) joined forces to organize a two-week Workshop on ...

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AncestryDNA Reconstructs Partial Genome of Person Living 200 Years Ago
Imagine if you could go back in time and see your ancestors. Would you see a part of yourself in one of them? Genetics is starting to answer questions about what a long ago ancestor may have...

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PUBLIC RELEASE DATE:

16-Dec-2014

Contact: Sue McGreevey smcgreevey@partners.org 617-724-2764 Massachusetts General Hospital @MassGeneralNews

Massachusetts General Hospital (MGH) investigators have developed a method of detecting, across the entire genome of human cells, unwanted DNA breaks induced by use of the popular gene-editing tools called CRISPR-Cas RNA-guided nucleases (RGNs). Members of the same team that first described these off-target effects in human cells describe their new platform, called Genome-wide Unbiased Indentification of DSBs Evaluated by Sequencing (GUIDE-seq), in a report being published online in Nature Biotechnology.

"GUIDE-seq is the first genome-wide method of sensitively detecting off-target DNA breaks induced by CRISPR-Cas nucleases that does not start with the assumption that these off-target sites resemble the targeted sites," says J. Keith Joung, MD, PhD, associate chief for Research in the MGH Department of Pathology and senior author of the report. "This capability, which did not exist before, is critically important for the evaluation of any clinical use of CRISPR-Cas RGNs."

Used to cut through a double strand of DNA in order to introduce genetic changes, CRISPR-Cas RGNs combine a bacterial gene-cutting enzyme called Cas9 with a short RNA segment that matches and binds to the target DNA sequence. In a 2013 Nature Biotechnology paper, Joung and his colleagues reported finding that CRISPR-Cas RGNs could also induce double-strand breaks (DSBs) at sites with significant differences from the target site, including mismatches of as many as five nucleotides. Since such off-target mutations could potentially lead to adverse effects, including cancer, the ability to identify and eventually minimize unwanted DSBs would be essential to the safe clinical use of these RGNs, the authors note.

The method they developed involves use of short, double-stranded oligonucleotides that are taken up by DSBs in a cell's DNA, acting as markers of off-target breaks caused by the use of CRISPR-Cas. Those tags allow the identification and subsequent sequencing of those genomic regions, pinpointing the location of off-target mutations. Experiments with GUIDE-seq showed it was sensitive enough to detect off-target sites at which CRISPR RGNs induced unwanted mutations of a gene that occur with a frequency of as little as 0.1 percent in a population of cells. These experiments also revealed that, since many such mutations took place at sites quite dissimilar from the targeted site, no easy rules would predict the number or location of off-target DSBs.

Two existing tools designed to predict off-target mutations by analysis of the target sequence were much less effective than GUIDE-seq in predicting confirmed off-target sites and also misidentified sites that did not prove to have been cut by the enzyme. Comparing GUIDE-seq with a tool called ChIP-seq - which identifies sites where proteins bind to a DNA strand - confirmed that ChIP-seq does not provide a robust method for identifying CRISPR-Cas-induced DSBs.

GUIDE-seq was also able to identify breakpoint hotspots in control cell lines that were not induced to express the CRISPR RGNs. "Various papers have described fragile genomic sites in human cells before," Joung notes, "but this method may be the first to identify these sites without the addition of drugs that enhance the occurrence of such breaks. We also were surprised to find those breaks occurred largely at different sites in the two cell lines used in this study. The ability to capture these RGN-independent breaks suggests that GUIDE-seq could be a useful tool for studying and monitoring DNA repair in living cells."

In addition, GUIDE-seq was able to verify that an MGH-developed approach for improving the accuracy of CRISPR-Cas by shortening the guiding RNA segment reduced the number of DSBs throughout the genome. Joung also expects that GUIDE-seq will be useful in identifying off-target breaks induced by other gene-editing tools. Along with pursuing that possibility, he notes the importance of investigating the incidence and detection of off-target mutations in human cells not altered to create cell lines - a process that transforms them into immortalized cancer cells. Understanding the range and number of off-target mutations in untransformed cells will give a better picture of how CRISPR-Cas RGNs and other tools would function in clinical applications.

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New method identifies genome-wide off-target cleavage sites of CRISPR-Cas nucleases

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