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' deep learning '
menemukan autisme , mutasi kanker pada daerah yang belum dijelajahi genom
uter yang telah menemukan mutasi penyebab penyakit di daerah-daerah besar genom yang sebelumnya tidak bisa dieksplorasi . Metode seeks out mutations yang menyebabkan perubahan ' splicing gen’ , dan telah mengungkapkan penentu genetik terduga dari autisme , kanker usus besar dan atrofi otot tulang belakang .....read more
'Deep learning'
finds autism, cancer mutations in unexplored regions of genome
Date:
December 18, 2014
Source:
Canadian Institute for
Advanced Research
Summary:
Scientists 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.
....................
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.
Their method works surprisingly well and has led to new discoveries. For
example, using DNA sequences from five patients with autism provided by
Scherer, the model was able to identify 39 new genes that could be implicated
in autism spectrum disorder, a 40 per cent increase from about 100 previously
known autism genes.
"Brendan's work is groundbreaking because it represents a first
serious attempt to decode the portions of that 98 per cent of the human genome
outside the genes that are typically studied in genetic disease studies,"
Scherer says. "This is particularly exciting since it is thought these
segments of DNA may contain much of the missing information that we have been
looking for in studies like autism."
Scherer and Frey began collaborating at CIFAR meetings five years ago and
they intend to use this model to analyze the genomes of 10,000 families with
autism as part of the MSSNG study. The paper also sheds light on the genetic
mechanisms that lead to spinal muscular atrophy, a leading cause of infant
death, and nonpolyposis colorectal cancer.
Frey says his involvement in two CIFAR programs was crucial in making
connections and in developing interdisciplinary expertise among his graduate
students and postdoctoral fellows, including co-authors Hui Xiong, Babak
Alipanahi, Leo Lee and Hannes Bretschneider. Also involved were Ben Blencowe of
the University of Toronto and Nebojsa Jojic of Microsoft Research.
"My participation in the Neural Computation & Adaptive Perception
program enabled my group to have access to the best techniques in deep
learning," Frey says. He adds that his interactions with members of the
Genetic Networks program challenged him to take on some of the toughest
questions in genetics.
CIFAR Senior Fellow Frederick Roth, co-director of the program in Genetic
Networks, says Drs. Frey, Scherer and Hughes have been key members of the
program and its efforts to interpret the genome. "Many of us will soon
know our complete human genome sequence, which will be like having an encyclopedic
guide to ourselves that is written in an alien language. This work promises to
interpret the impact of mutations in a broader region of our genome than has
been previously possible," he says.
Story Source:
The above story is based on materials provided by Canadian
Institute for Advanced Research. Note: Materials may be edited for content and length.
Journal Reference:
1. H. Y. Xiong, B. Alipanahi, L. J. Lee, H.
Bretschneider, D. Merico, R. K. C. Yuen, Y. Hua, S. Gueroussov, H. S.
Najafabadi, T. R. Hughes, Q. Morris, Y. Barash, A. R. Krainer, N. Jojic, S. W.
Scherer, B. J. Blencowe, B. J. Frey. The human splicing code reveals
new insights into the genetic determinants of disease. Science,
2014; DOI: 10.1126/science.1254806
