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Design and Analysis of DNA String Matching by Optical Parallel Processing
- 1. Design and Analysis of DNA String Matching by Optical Parallel Processing Presenter: Hossein Babashah Supervisors: Dr. Kavehvash, Dr. Koohi, and Dr. Khavasi Internal referee: Dr. Memarian External referee: Dr. Shahabadi September 2017
- 2. 1 Introduction 3D Optical Neural Network & Moiré Matching Technique Metamaterial based Optical Integrator Windows size based Optical Correlator On-Chip Temporal Optical Computing Conclusion 2 3 4 5 6 7 Previous Methods DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Agenda 1/45
- 3. Bioinformatics Compare DNA sequences to detect mutations for tackling disease A: Adenine C: Cytosine G: Guanine T: Thymine DNA strings are very long1 2 3 4 5 Public sequencing databases double in size every 18 months Searching DNA databases is becoming too expensive using HPC Consuming vast amounts of energy for power and cooling Predict cancer in human before symptoms appear DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 2/45
- 4. Optical Computing Perform these extensive searches using optical computing At a fraction of the energy consumption and cost 1 2 3 4 Able to perform the calculations in parallel Affordable HPC At speeds of magnitudes faster DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 3/45
- 5. 1 Introduction 3D Optical Neural Network & Moiré Matching Technique Metamaterial based Optical Integrator Windows size based Optical Correlator On-Chip Temporal Optical Computing Conclusion 2 3 4 5 6 7 Previous Methods DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Agenda 4/45
- 6. Dynamic Programming1 2 3 4 Previous Methods DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Artificial Neural Network Moiré matching technique Joint Fourier transform correlator 5/45
- 7. Dynamic Programming table [1] Dynamic Programming DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Ability to find best match1 2 3 4 Exact mismatch location High power consumption Inherent serial processing 5 Slow speed 6/45
- 8. Artificial neural network [2] Artificial Neural Network DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Similarity with a score1 2 3 4 Training requirement Relative low power Consumption Parallel processing potential 5 High speed 7/45
- 9. Moiré Matching technique [3] Moiré Matching Technique DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Spatial coding1 2 3 4 Complex output & supervisor requirement Exact mismatch location low power consumption 5 Parallel processing 6 High speed 8/45
- 10. JFT correlator [4] Joint Fourier Transform Correlator DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Similarity with a peak value1 2 3 4 Unable to find exact location Low power consumption High speed5 Parallel processing potential 9/45
- 11. Previous Methods Comparison Method Simple & Incomprehensible Output Mutation Location Detection Parallel Processing Potential High Speed Dynamic Programming Artificial Neural Network Moiré Matching Technique Optical Correlator DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Goal: Improve Moiré using correlator and ANN 10/45
- 12. 1 Introduction 3D Optical Neural Network & Moiré Matching Technique Metamaterial based Optical Integrator Windows size based Optical Correlator On-Chip Temporal Optical Computing Conclusion 2 3 4 5 6 7 Previous Methods DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Agenda 11/45
- 13. Executive Summary Problem: Incomprehensible & noisy output of Moiré Matching technique Our Goal: DNA sequence alignment using optical parallel processing to detect mutation locations in a comprehensible output Observation: Comprehensible output of parallel processors such as artificial neural network & correlator and mutation location detection in Moiré Matching technique Key Idea: Using Moiré output as optical neural network input for a comprehensible output Result: A comprehensible output while using parallel processing DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 12/45
- 14. Optical Correlator Optical Correlator DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Big data handling1 2 3 4 Speed of data manipulation low power Consumption Sequences of 150 character long 13/45
- 15. Neural Network Structure DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Coding First Step Optical 3D Neural Network Third Step Mutation location detection Output Moiré Matching Technique Second Step Two string with identical long Input 14/45
- 16. Optical Simulation of Neural Network DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 15/45
- 18. Pros And Cons DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Pros Cons • Parallel processing • High speed • Mutation location detection • Comprehensible output • Training time • Expensive & Complex structure Solution: Metamaterial based improved optical integrator 17/45
- 19. 1 Introduction 3D Optical Neural Network & Moiré Matching Technique Metamaterial based Optical Integrator Windows size based Optical Correlator On-Chip Temporal Optical Computing Conclusion 2 3 4 5 6 7 Previous Methods DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Agenda 18/45
- 20. Executive Summary Problem: Complex and expensive structure Our Goal: DNA sequence alignment using optical parallel processing to detect mutation locations in less complex structure Observation: Previous use of integration in electrical DNA matching Key Idea: Improving optical integrator using a feedback Result: A simple output using optical parallel processing DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Problem: Truncation Key Idea: Metamaterial based optical integrator for DNA Observation: Mathematical solution to the problem 19/45
- 21. 3 4 1 2 Analog Optical Computing Mathematical Operations Permeability & permittivity for TF Operation in Fourier Domain Metamaterial equivalent DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 20/45
- 22. Problem DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 𝐻(𝑢) = 1, 𝑖𝑓 |𝑢| < 𝑡 𝑡 𝑖𝑢 , 𝑖𝑓 |𝑢| > 𝑡 21/45
- 25. DNA Sequence Alignment using Integration DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 24/45
- 26. Proposed Optical Implementation Structure DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 25/45
- 28. Pros And Cons DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Pros Cons • Parallel processing • High speed • Mutation location detection • Relative simple structure • Mutation type Solution: Windows size based optical correlator 27/45
- 29. 1 Introduction 3D Optical Neural Network & Moiré Matching Technique Metamaterial based Optical Integrator Windows size based Optical Correlator On-Chip Temporal Optical Computing Conclusion 2 3 4 5 6 7 Previous Methods DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Agenda 28/45
- 30. Executive Summary Problem: Mutation type detection in optical integrator Our Goal: DNA sequence alignment using optical parallel processing with high ability in detecting location and type of mutations Observation: Correlation of short sequences can determine their place Key Idea: Dividing the read sequence into windows Result: Mutation type and location detection DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 29/45
- 31. Problem DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 21 Without deletion With deletion Which PeakO.K. Peak 30/45
- 33. Optical Implementation Structure DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 32/45
- 35. Comparison With BLAST DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 1 2 Search 303 read sequences of length 15char in a reference sequence of 1000 character length Accuracy in different noise level 3 Time Comparison in different noise level 34/45
- 36. Pros And Cons DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Pros Cons • Parallel processing • High speed • Mutation location detection • Relative simple structure • Mutation type • Relative practical bulky structure Solution: On-chip temporal optical computing 35/45
- 37. 1 Introduction 3D Optical Neural Network & Moiré Matching Technique Metamaterial based Optical Integrator Windows size based Optical Correlator On-Chip Temporal Optical Computing Conclusion 2 3 4 5 6 7 Previous Methods DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Agenda 36/45
- 38. 3 4 1 2 On-Chip Temporal Optical Computing Mathematical Operations Optical modulator Dispersive Fourier Transform On-Chip Photonic Signal Processor DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 37/45
- 39. Dispersive Fourier Transform DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 38/45
- 40. Linearly Chirp Modulation Effect on DFT DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 39/45
- 43. 1 Introduction 3D Optical Neural Network & Moiré Matching Technique Metamaterial based Optical Integrator Windows size based Optical Correlator On-Chip Temporal Optical Computing Conclusion 2 3 4 5 6 7 Previous Methods DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Agenda 42/45
- 44. Conclusion DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing Goal: DNA Sequence Alignment using Optical Processing Optical Integrator Neural Network Window Correlator Temporal Computing Improved Integration DNA Integration 43/45
- 45. References [1] M. Sniedovich, “Dijkstra’s algorithm revisited: the dynamic programming connexion,” Control Cybern., vol. 35, no. 3, p. 599, 2006. [2] C. H. Wu, “Artificial neural networks for molecular sequence analysis,” Comput. Chem., vol. 21, no. 4, pp. 237–256, 1997. [3] A. K. Alqallaf and A. K. Cherri, “DNA sequencing using optical joint Fourier transform,” Opt. J. Light Electron Opt., vol. 127, no. 4, pp. 1929–1936, 2016. [4] J. Tanida, K. Nitta, and A. Yahata, “Spatially coded Moire matching technique for genome information visualization,” in Photonics Asia 2002, 2002, pp. 26–33. [5] A. Silva, F. Monticone, G. Castaldi, V. Galdi, A. Alù, and N. Engheta. Performing mathematical operations with metamaterials. Science, 343(6167):160–163, 2014. [6] W. Liu, M. Li, R. S. Guzzon, E. J. Norberg, J. S. Parker, M. Lu, L. A. Coldren, and J. Yao. A fully reconfigurable photonic integrated signal processor. Nature Photonics, 10(3):190–195, 2016. DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 44/45
- 46. Thank You “There are no secrets to success. It is the result of preparation, hard work, and learning from failures.” DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 45/45
- 47. Detail for Optical Neural Netwrok DesignandAnalysisofDNAStringMatchingbyOpticalParallelProcessing 𝑓(𝑌) = 𝑎 + 𝛾 𝑒−𝛽𝑌+𝛿 𝑎 = 0.43, 𝛾 = 23.1, 𝛽 = 1.4, 𝛿 = 4.57 46
- 48. Detail for Optical Integrator 47