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OptCAM: An ultra-fast all-optical architecture for DNA variant discovery.

Ehsan Maleki1, Somayyeh Koohi1, Zahra Kavehvash2

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

Journal of Biophotonics
|August 10, 2019
PubMed
Summary

This study introduces OptCAM, an optical processing method for DNA sequence alignment. OptCAM leverages optical correlation for faster, parallel DNA variant discovery compared to electrical methods.

Keywords:
DNADNA mutational analysiscomputational biologyoptical computingoptical devicesoptics and photonicssequence alignmentsequence analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Optical Computing

Background:

  • Accelerated genetic data growth strains current electrical DNA sequence alignment algorithms.
  • Efficient and accurate DNA variant discovery requires real-time parallel processing solutions.

Purpose of the Study:

  • To present an optical processing approach for local DNA sequence alignment.
  • To address the speed and parallelism limitations of electrical implementations.

Main Methods:

  • The OptCAM method uses optical correlation for global alignment and signal properties (amplitude, wavelength) for mutation detection.
  • An all-optical implementation of OptCAM is proposed with three distinct processing units.
  • Numerical simulations using a gold standard benchmark validate the method's accuracy and efficiency.

Main Results:

  • OptCAM demonstrates significantly higher processing speeds compared to electrical counterparts.
  • The method achieves high-throughput processing via photonic devices, overcoming electrical limitations.
  • Validation confirms the accuracy and efficiency of the proposed optical alignment approach.

Conclusions:

  • OptCAM offers a promising, high-speed, and parallelizable solution for local DNA sequence alignment.
  • The optical implementation of OptCAM overcomes the inherent limitations of current electrical systems.
  • This approach accelerates DNA variant discovery, crucial for managing large genetic datasets.