High-speed all-optical DNA local sequence alignment based on a three-dimensional artificial neural network

HELIOS: High-speed sequence alignment in optics

In response to the imperfections of current sequence alignment methods, originated from the inherent serialism within their corresponding electrical systems, a few optical approaches for biological data comparison have been proposed recently. However, due to their low performance, raised from their inefficient coding scheme, this paper presents a novel all-optical high-throughput method for aligning DNA, RNA, and protein sequences, named HELIOS. The HELIOS method employs highly sophisticated operations to locate character matches, single or multiple mutations, and single or multiple indels within various biological sequences. On the other hand, the HELIOS optical architecture exploits high-speed processing and operational parallelism in optics, by adopting wavelength and polarization of optical beams. For evaluation, the functionality and accuracy of the HELIOS method are approved through behavioral and optical simulation studies, while its complexity and performance are estimated through analytical computation. The accuracy evaluations indicate that the HELIOS method achieves a precise pairwise alignment of two sequences, highly similar to those of Smith-Waterman, Needleman-Wunsch, BLAST, MUSCLE, ClustalW, ClustalΩ, T-Coffee, Kalign, and MAFFT. According to our performance evaluations, the HELIOS optical architecture outperforms all alternative electrical and optical algorithms in terms of processing time and memory requirement, relying on its highly sophisticated method and optical architecture. Moreover, the employed compact coding scheme highly escalates the number of input characters, and hence, it offers reduced time and space complexities, compared to the electrical and optical alternatives. It makes the HELIOS method and optical architecture highly applicable for biomedical applications.

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

In this study, optical technology is considered as SA issues' solution with the potential ability to increase the speed, overcome memory-limitation, reduce power consumption, and increase output accuracy. So we examine the effect of bio-data encoding and the creation of input images on the pattern-recognition error-rate at the output of optical Vander-lugt correlator. Moreover, we present a genetic algorithm-based coding approach, named as GAC, to minimize output noises of cross-correlating data. As a case study, we adopt the proposed coding approach within a correlation-based optical architecture for counting k-mers in a DNA string. As verified by the simulations on Salmonella whole-genome, we can improve sensitivity and speed more than 86% and 81%, respectively, compared to BLAST by using coding set generated by GAC method fed to the proposed optical correlator system. Moreover, we present a comprehensive report on the impact of 1D and 2D cross-correlation approaches, as-well-as various coding parameters on the output noise, which motivate the system designers to customize the coding sets within the optical setup.

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

Nowadays, the accelerated expansion of genetic data challenges speed of current DNA sequence alignment algorithms due to their electrical implementations. Essential needs of an efficient and accurate method for DNA variant discovery demand new approaches for parallel processing in real time. Fortunately, photonics, as an emerging technology in data computing, proposes optical correlation as a fast similarity measurement algorithm; while complexity of existing local alignment algorithms severely limits their applicability. Hence, in this paper, employing optical correlation for global alignment, we present an optical processing approach for local DNA sequence alignment to benefit both high-speed processing and operational parallelism, inherently exist in optics. The proposed method, named as OptCAM, utilizes amplitude and wavelength of the optical signals, to accurately locate mutations through three main procedures. Furthermore, an all-optical implementation of the OptCAM method is proposed consisting of three units, corresponding to the three OptCAM procedures. Performing considerably fast processes by passing optical signals through high-throughput photonic devices, OptCAM avoids various limitations of electrical implementations. Accuracy and efficiency of the OptCAM method and its optical implementation are validated through numerical simulation by a gold standard simulation benchmark. The results indicate the proposed method is significantly faster than its electrical counterparts, in both single node and grid computation.

All-optical DNA variant discovery utilizing extended DV-curve-based wavelength modulation

This paper presents a novel optical processing approach for exploring genome sequences built upon an optical correlator for global alignment and the extended dual-vector-curve (DV-curve) method for local alignment. To overcome the problem of the traditional DV-curve method for presenting an accurate and simplified output, we propose the hybrid amplitude wavelength polarization optical DV-curve (HAWPOD) method, built upon the DV-curve method, to analyze genome sequences in three steps: DNA coding, alignment, and post-analysis. For this purpose, a tunable graphene-based color filter is designed for wavelength modulation of optical signals. Moreover, all-optical implementation of the HAWPOD method is developed, while its accuracy is validated through numerical simulations in LUMERICAL FDTD. The results express that the proposed method is much faster than its electrical counterparts.