Viral Identification Using Microarray

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Viral Identification Using Microarray
Current Subject

• Introduction to Bioinformatics
• Dudu Burstein

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Short Biology Introduction
Current Subject
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DNA Microarrays
Short Biology Introduction
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Viruses
Short Biology Introduction
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Round 1 Viral Identification Using DNA
Microarrays
The SARS Case
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Previous Identification Techniques
Identification using microarray

• Similar gene amplification (degenerate PCR)
• Antibody recognition (immunoscreening of cDNA
  Libraries)
• Drawbacks
• Limited candidates
• Biased
• Time consuming

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The DeRisi Lab Viral Microarray
Identification using microarray

• Approx. 1,000 viruses
• Probes 70 nucleotide long
• 10 most conserved of each virus
• Amplification and hybridization
• Objective create a microarray with the
  capability of detecting the widest possible range
  of both known and unknown viruses

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The SARS Epidemic
Identification using microarray

• SARS Severe acute respiratory syndrome
• Flu-like symptoms
• Nov. 2002 first case in Gunangdong, China
• 15 Feb. 2003 Spreads to Hong-Kong
• 21 Feb. 12 infections that will spread to Hong
  Kong, Vietnam Singapore, Ireland, Germany and
  Canada

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The SARS Epidemic
Identification using microarray

• Cases in China, Hong Kong, Canada, Taiwan,
  Singapore, Vietnam, USA, Philippines, Germany,
  Mongloia, Thailand, France, Malaysia, Sweden,
  Italy, UK, India, Korea, Indonesia, South Africa,
  Kuwait, Ireland, Romania, Russia, Spain,
  Switzerland.
• Total 8,096 known cases
• 774 deaths
• Mortality rate of 9.6
• April 2004 last reported case

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The SARS Identification
Identification using microarray

• March 15th - WHO generate global alert
• March 22th samples obtained
• Amplified and Hybridized with microarray (1,000
  viruses, 10 probes of 70 nucleotides)
• Following results in less then 24 hours

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SARS Identification
Identification using microarray
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SARS Identification
Identification using microarray
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Summary (round 1)
Identification using microarray

• Microarray of conserved sequences from thousands
  of viruses
• Hybridization enable identification
• Rapid procedure
• Limited homology suffice
• Sequencing based on DNA recovered from microarray
• The SARS proof

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Round 2 The E-Predict Algorithms
The E-Predict Algorithm
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E-Predict Algorithm Challenges
The E-Predict Algorithm

• Complex hybridization pattern, still time
  consuming
• Human interpretation might be biased
• Separate closely related species
• Unanticipated cross hybridization
• Statistical significance
• Signal from dozens or hundreds of species when
  pure samples impossible to obtain (metagenomics)

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E-Predict Algorithm Outline
The E-Predict Algorithm
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Significance Estimation
The E-Predict Algorithm

• Similarity ranking ? Probability that best
  profile corresponds to virus in sample
• 1,009 independent diverse microarray data
• For every virus, most data false positive
• Used as null (H0) Distribution

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Significance Estimation
The E-Predict Algorithm
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E-Predict Results HPV18
The E-Predict Algorithm
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E-Predict Results FluA
The E-Predict Algorithm
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Serotype Discrimination
The E-Predict Algorithm

• HRV species of the Rhinovirus genus, part of
  the picornavirus family
• HRV can be divided to
• HRV group A
• HRV group B
• HRV87 (closely related to enteroviruses)
• Energy profiles of HRV89 (group A) and HRV14
  (group B)

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Serotype Discrimination
The E-Predict Algorithm
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Summary
The E-Predict Algorithm

• Results achieved very rapidly
• Minimal human interpretation no bias
• Not sensitive to noise
• Handles complex hybridization pattern
• Valid Interfamily and intrafamily separation
• Serotype separation

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Possible Application
The E-Predict Algorithm

• Pathogen detection
• clinical specimens
• field isolates
• Monitoring food/water contamination
• Characterization of microbial communities from
  soil/water

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Thank You
The SARS Case