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Optical pattern generator for efficient bio-data encoding in a photonic sequence comparison architecture.

Saeedeh Akbari Rokn Abadi1, Negin Hashemi Dijujin1, Somayyeh Koohi1

  • 1Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.

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|January 15, 2021
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Summary
This summary is machine-generated.

Optical technology offers solutions for speed and accuracy in data processing. A new genetic algorithm coding approach significantly enhances DNA sequence analysis speed and sensitivity compared to existing methods.

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Area of Science:

  • Optoelectronics
  • Bioinformatics
  • Computational Biology

Background:

  • Optical technology presents a promising avenue for addressing limitations in speed, memory, power consumption, and accuracy in scientific computing.
  • The Vander Lugt correlator is a key optical architecture for pattern recognition tasks.

Purpose of the Study:

  • To investigate the impact of bio-data encoding and input image generation on pattern recognition error rates in optical correlators.
  • To develop and evaluate a genetic algorithm-based coding approach (GAC) for minimizing noise in cross-correlated data.
  • To apply this optical approach to the specific problem of counting k-mers in DNA sequences.

Main Methods:

  • Examined the effect of bio-data encoding and input image creation on pattern recognition error rates.
  • Developed a genetic algorithm-based coding approach (GAC) to reduce cross-correlation noise.
  • Implemented and simulated the GAC method within an optical Vander Lugt correlator for k-mer counting in DNA.
  • Compared the performance against BLAST using simulations on the Salmonella whole-genome.

Main Results:

  • The GAC method, when integrated with the optical correlator, significantly improved sensitivity by over 86% and speed by over 81% compared to BLAST.
  • Simulations provided a comprehensive analysis of the influence of 1D and 2D cross-correlation methods and coding parameters on output noise.
  • The study demonstrated the potential of optical correlators with optimized coding for high-throughput biological data analysis.

Conclusions:

  • Optical technology, particularly with advanced coding strategies like GAC, offers a powerful solution for accelerating and improving the accuracy of bioinformatics tasks.
  • The developed GAC approach effectively minimizes noise in optical correlation, leading to substantial performance gains in DNA sequence analysis.
  • Findings provide valuable insights for designing and customizing optical systems for efficient processing of large-scale biological data.