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

Abstract

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.

HELIOS: High-speed sequence alignment in optics

“…Afterward, the time and space complexities and the performance of the HELIOS optical architecture are estimated by analytical computation. For a comparative study, we consider various well-known algorithms, including BLAST [ 7 ], ClustalW [ 8 ], ClustalΩ [ 9 ], MUSCLE [ 12 ], T-Coffee [ 10 ], Kalign [ 11 ], MAFFT [ 13 ], Smith-waterman [ 5 ], Needleman-Wunsch [ 6 ] Nucmer4 [ 14 ], BLASR [ 44 ], BWA-MEM [ 45 ], Bowtie2 [ 46 ], Mauve [ 47 ], LASTZ [ 48 ], Moiré Technique [ 31 ], HAWPOD [ 30 ], and OptCAM [ 34 ]. Finally, some well-known applications are presented that can potentially benefit from the HELIOS method.…”

“…On the other hand, a few recent proposed optical approaches, such as Moiré Technique [ 31 ], HAWPOD [ 30 ], and OptCAM [ 34 ] benefit from the parallelism and high-speed processing of optics. However, due to their exclusive coding assumptions, they can be adopted for neither RNAs nor proteins alignment.…”

“…For a comprehensive comparison of the processing time and the memory requirement, the following scenarios are considered. In this manner, the processing times and memory of the HELIOS optical architecture are analytically estimated and compared to A) Nucmer4 [ 14 ], Mauve [ 47 ], LASTZ match [ 48 ], LASTZ default [ 48 ], Moiré Technique [ 31 ], HAWPOD [ 30 ], and OptCAM [ 34 ] in the case of genome-to-genome alignment, as reported in [ 14 ], B) Nucmer4 [ 14 ], BLASR [ 44 ], BWA-MEM [ 45 ], Bowtie2 [ 46 ], Moiré Technique [ 31 ], HAWPOD [ 30 ], and OptCAM [ 34 ] in the case of reads alignment with a reference sequence considering Illumina and PacBio databases for Arabidopsis [ 62 ] and Human [ 63 ] reference genomes, as reported in [ 14 ], and finally, C) Smith-waterman [ 5 ], in the case of aligning various length of sequences with the SWISS-PROT dataset [ 64 ], reported in [ 65 ]. The analytical estimation of processing time of HELIOS optical architecture is performed by considering the aperture size and switching times of the modulators, as follows, according to Eq 12 ; while the memory requirement equals the aperture size of the modulators, according to Eq 13 .…”

“…Accordingly, biophotonics develops optical techniques for biological developments [ 25 ]. In this manner, some methods have been accomplished recently, such as correlation-based methods [ 26 , 27 ], Fourier-Transform-based algorithms [ 28 , 29 ], HAWPOD [ 30 ], Moiré Technique [ 31 – 33 ], OptCAM [ 34 ], GAC [ 35 ], and SPOMF [ 36 ]. Some of them [ 26 – 29 ] address optical similarity measurement algorithms for sequence alignment by taking advantage of optical correlation and Fourier Transform.…”

­­­A novel quantum algorithm for Biological Sequence Alignment using Quantum Accelerated Mapping in Seed-and-Extend Technique.

“…Then, more advanced tools appeared like BWA [6], HISAT [7], MUMmer series [8-9], etc. However, in the era of big data, the need for even stronger alternatives led to completely unconventional ideas for hardware systems that are based on optics [10] and quantum computing technologies [11][12]. Both examples are still in an embryonic stage.…”