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

2. Short Biology Introduction Current Subject

3. Introduction to Bioinformatics3 of 25 DNA Microarrays Short Biology Introduction

4. Introduction to Bioinformatics4 of 25 Viruses Short Biology Introduction

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

6. Introduction to Bioinformatics6 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

7. Introduction to Bioinformatics7 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

8. Introduction to Bioinformatics8 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

9. Introduction to Bioinformatics9 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

10. Introduction to Bioinformatics10 of 25 The SARS Identification March 15 th - WHO generate global alert March 22 th – 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

11. Introduction to Bioinformatics11 of 25 SARS Identification Identification using microarray FamilyVirus CoronaIBV AAAA CoronaIBVA Corona Bovine corona AAAA CoronaHuman 229EA AstroTurkey astroA AstroOvine astroA Astro Avian nephritisA AstroHuman astroA

12. Introduction to Bioinformatics12 of 25 SARS Identification Identification using microarray FamilyVirus CoronaIBV AAAA CoronaIBVA Corona Bovine corona AAAA CoronaHuman 229EA AstroTurkey astroA AstroOvine astroA Astro Avian nephritisA AstroHuman astroA

13. Introduction to Bioinformatics13 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

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

15. Introduction to Bioinformatics15 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

16. Introduction to Bioinformatics16 of 25 E-Predict Algorithm Outline The E-Predict Algorithm

17. Introduction to Bioinformatics17 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 (H 0 ) Distribution The E-Predict Algorithm

18. Introduction to Bioinformatics18 of 25 Significance Estimation The E-Predict Algorithm

19. Introduction to Bioinformatics19 of 25 E-Predict Results – HPV18 The E-Predict Algorithm

20. Introduction to Bioinformatics20 of 25 E-Predict Results – FluA The E-Predict Algorithm

21. Introduction to Bioinformatics21 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

22. Introduction to Bioinformatics22 of 25 Serotype Discrimination The E-Predict Algorithm

23. Introduction to Bioinformatics23 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

24. Introduction to Bioinformatics24 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

25. Thank You The SARS Case