Mining DNA Sequences Based on Spatially Coded Technique Using Spatial Light Modulator

TL;DR

This paper introduces an optical computing technique using moire patterns and spatial light modulation to efficiently analyze DNA sequences, enabling visual detection of genetic variations and improving processing speed and accuracy.

Contribution

The paper presents a novel optical method combining moire fringes and cylindrical lens mapping for DNA sequence analysis, enhancing visualization and processing efficiency.

Findings

Effective visualization of DNA sequence variations

Improved processing gain and SNR in experiments

Successful simulation and experimental validation

Abstract

In this paper, we present an optical computing method for string data alignment applicable to genome information analysis. By applying moire technique to spatial encoding patterns of deoxyribonucleic acid (DNA) sequences, association information of the genome and the expressed phenotypes could more effectively be extracted. Such moire fringes reveal occurrence of matching, deletion and insertion between DNA sequences providing useful visualized information for prediction of gene function and classification of species. Furthermore, by applying a cylindrical lens, a new technique is proposed to map two-dimensional (2D) association information to a one-dimensional (1D) column of pixels, where each pixel in the column is representative of superposition of all bright and dark pixels in the corresponding row. By such a time-consuming preprocessing, local similarities between two intended patterns can readily be found by just using a 1D array of photodetectors and postprocessing could be performed on specified parts in the initial 2D pattern. We also evaluate our proposed circular encoding adapted for poor data alignment condition. Our simulation results together with experimental implementation verify the effectiveness of our dynamic proposed methods which significantly improve system parameters such as processing gain and signal to noise ratio (SNR).