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Identification Using Microarray Introduction to Bioinformatics Dudu Burstein Current Subject
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Viral Identification

Using Microarray

Introduction to Bioinformatics

Dudu Burstein

Current Subject

Short Biology Introduction

Current Subject

Introduction to Bioinformatics 3 of 25

DNA Microarrays

Short Biology Introduction

Introduction to Bioinformatics 4 of 25

Viruses

Short Biology Introduction

Round 1: Viral Identification Using DNA Microarrays

The SARS Case

Introduction to Bioinformatics 6 of 25

Previous Identification Techniques

Similar gene amplification (degenerate PCR)

Antibody recognition (immunoscreening of cDNA Libraries)

Drawbacks: Limited candidates Biased Time consuming

Identification using microarray

Introduction to Bioinformatics 7 of 25

The DeRisi Lab Viral 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”

Identification using microarray

Introduction to Bioinformatics 8 of 25

The SARS Epidemic

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

Identification using microarray

Introduction to Bioinformatics 9 of 25

The SARS Epidemic

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

Identification using microarray

Introduction to Bioinformatics 10 of 25

The SARS Identification

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

Identification using microarray

Introduction to Bioinformatics 11 of 25

SARS Identification

Identification using microarray

FamilyVirus

CoronaIBVAA

CoronaIBVAA

CoronaBovine corona

AA

CoronaHuman 229EAA

AstroTurkey astroAA

AstroOvine astroAA

AstroAvian nephritis

AA

AstroHuman astroAA

Introduction to Bioinformatics 12 of 25

SARS Identification

Identification using microarray

FamilyVirus

CoronaIBVAA

CoronaIBVAA

CoronaBovine corona

AA

CoronaHuman 229EAA

AstroTurkey astroAA

AstroOvine astroAA

AstroAvian nephritis

AA

AstroHuman astroAA

Introduction to Bioinformatics 13 of 25

Summary (round 1)

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

Identification using microarray

Round 2: The E-Predict Algorithms

The E-Predict Algorithm

Introduction to Bioinformatics 15 of 25

E-Predict Algorithm Challenges

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)

The E-Predict Algorithm

Introduction to Bioinformatics 16 of 25

E-Predict Algorithm Outline

The E-Predict Algorithm

Introduction to Bioinformatics 17 of 25

Significance Estimation

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

The E-Predict Algorithm

Introduction to Bioinformatics 18 of 25

Significance Estimation

The E-Predict Algorithm

Introduction to Bioinformatics 19 of 25

E-Predict Results – HPV18

The E-Predict Algorithm

Introduction to Bioinformatics 20 of 25

E-Predict Results – FluA

The E-Predict Algorithm

Introduction to Bioinformatics 21 of 25

Serotype Discrimination

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)

The E-Predict Algorithm

Introduction to Bioinformatics 22 of 25

Serotype DiscriminationThe E-Predict Algorithm

Introduction to Bioinformatics 23 of 25

Summary

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

separation Serotype separation

The E-Predict Algorithm

Introduction to Bioinformatics 24 of 25

Possible Application

Pathogen detection: clinical specimens

field isolates

Monitoring food/water contamination Characterization of microbial communities

from soil/water

The E-Predict Algorithm

Thank You

The SARS Case


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