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Viral Identification Using Microarray

Current Subject. Viral Identification Using Microarray. Introduction to Bioinformatics Dudu Burstein. Current Subject. Short Biology Introduction. Short Biology Introduction. DNA Microarrays. Short Biology Introduction. Viruses. The SARS Case.

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Viral Identification Using Microarray

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

  2. Current Subject Short Biology Introduction

  3. Short Biology Introduction DNA Microarrays Introduction to Bioinformatics

  4. Short Biology Introduction Viruses Introduction to Bioinformatics

  5. The SARS Case Round 1: Viral Identification Using DNA Microarrays

  6. Identification using microarray Previous Identification Techniques • Similar gene amplification (degenerate PCR) • Antibody recognition(immunoscreening of cDNA Libraries) Drawbacks: • Limited candidates • Biased • Time consuming Introduction to Bioinformatics

  7. Identification using microarray 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” Introduction to Bioinformatics

  8. Identification using microarray 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 Introduction to Bioinformatics

  9. Identification using microarray 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 Introduction to Bioinformatics

  10. Identification using microarray 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 Introduction to Bioinformatics

  11. Identification using microarray SARS Identification Introduction to Bioinformatics

  12. Identification using microarray SARS Identification Introduction to Bioinformatics

  13. Identification using microarray 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 Introduction to Bioinformatics

  14. The E-Predict Algorithm Round 2: The E-Predict Algorithms

  15. The E-Predict Algorithm 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) Introduction to Bioinformatics

  16. The E-Predict Algorithm E-Predict Algorithm Outline Introduction to Bioinformatics

  17. The E-Predict Algorithm 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 Introduction to Bioinformatics

  18. The E-Predict Algorithm Significance Estimation Introduction to Bioinformatics

  19. The E-Predict Algorithm E-Predict Results – HPV18 Introduction to Bioinformatics

  20. The E-Predict Algorithm E-Predict Results – FluA Introduction to Bioinformatics

  21. The E-Predict Algorithm 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) Introduction to Bioinformatics

  22. The E-Predict Algorithm Serotype Discrimination Introduction to Bioinformatics

  23. The E-Predict Algorithm 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 Introduction to Bioinformatics

  24. The E-Predict Algorithm Possible Application • Pathogen detection: • clinical specimens • field isolates • Monitoring food/water contamination • Characterization of microbial communities from soil/water Introduction to Bioinformatics

  25. The SARS Case Thank You

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