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Current Subject Viral Identification Using Microarray Introduction to Bioinformatics Dudu Burstein Current Subject Short Biology Introduction Short Biology Introduction DNA Microarrays Introduction to Bioinformatics 3 of 25 Short Biology Introduction Viruses Introduction to Bioinformatics 4 of 25 The SARS Case Round 1: Viral Identification Using DNA Microarrays Identification using microarray Previous Identification Techniques Similar gene amplification Antibody recognition (immunoscreening of cDNA (degenerate PCR) Libraries) Drawbacks: Limited candidates Biased Time consuming Introduction to Bioinformatics 6 of 25 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 7 of 25 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 8 of 25 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 9 of 25 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 10 of 25 Identification using microarray SARS Identification Family Virus Corona IBV A A Corona IBV A A Corona Bovine corona A A Corona Human 229E A A Astro Turkey astro A A Astro Ovine astro A A Astro Avian nephritis A A Astro Human astro A A Introduction to Bioinformatics 11 of 25 Identification using microarray SARS Identification Family Virus Corona IBV A A Corona IBV A A Corona Bovine corona A A Corona Human 229E A A Astro Turkey astro A A Astro Ovine astro A A Astro Avian nephritis A A Astro Human astro A A Introduction to Bioinformatics 12 of 25 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 13 of 25 The E-Predict Algorithm Round 2: The E-Predict Algorithms 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 15 of 25 The E-Predict Algorithm E-Predict Algorithm Outline Introduction to Bioinformatics 16 of 25 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 17 of 25 The E-Predict Algorithm Significance Estimation Introduction to Bioinformatics 18 of 25 The E-Predict Algorithm E-Predict Results – HPV18 Introduction to Bioinformatics 19 of 25 The E-Predict Algorithm E-Predict Results – FluA Introduction to Bioinformatics 20 of 25 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 21 of 25 The E-Predict Algorithm Serotype Discrimination Introduction to Bioinformatics 22 of 25 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 23 of 25 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 24 of 25 The SARS Case Thank You