A simple grid implementation with Berkeley Open Infrastructure for Network Computing using BLAST as a model

The translational impact of bioinformatics on traditional wet lab techniques

Bioinformatics has taken a pivotal place in the life sciences field. Not only does it improve, but it also fine-tunes and complements the wet lab experiments. It has been a driving force in the so-called biological sciences, converting them into hypothesis and data-driven fields. This study highlights the translational impact of bioinformatics on experimental biology and discusses its evolution and the advantages it has brought to advancing biological research. Computational analyses make labor-intensive wet lab work cost-effective by reducing the use of expensive reagents. Genome/proteome-wide studies have become feasible due to the efficiency and speed of bioinformatics tools, which can hardly be compared with wet lab experiments. Computational methods provide the scalability essential for manipulating large and complex data of biological origin. AI-integrated bioinformatics studies can unveil important biological patterns that traditional approaches may otherwise overlook. Bioinformatics contributes to hypothesis formation and experiment design, which is pivotal for modern-day multi-omics and systems biology studies. Integrating bioinformatics in the experimental procedures increases reproducibility and helps reduce human errors. Although today's AI-integrated bioinformatics predictions have significantly improved in accuracy over the years, wet lab validation is still unavoidable for confirming these predictions. Challenges persist in multi-omics data integration and analysis, AI model interpretability, and multiscale modeling. Addressing these shortcomings through the latest developments is essential for advancing our knowledge of disease mechanisms, therapeutic strategies, and precision medicine.

Tree-Like Distributed Computation Environment with Shapp Library

Despite the rapidly growing computing power of computers, it is often insufficient to perform mass calculations in a short time, for example, simulation of systems for various sets of parameters, the searching of huge state spaces, optimization using ant or genetic algorithms, machine learning, etc. One can solve the problem of a lack of computing power through workload management systems used in local networks in order to use the free computing power of servers and workstations. This article proposes raising such a system to a higher level of abstraction: The use in the .NET environment of a new Shapp library that allows remote task execution using fork-like operations from Portable Operating System Interface for UNIX (POSIX) systems. The library distributes the task code, sending static data on which task force is working, and individualizing tasks. In addition, a convenient way of communicating distributed tasks running hierarchically in the Shapp library was proposed to better manage the execution of these tasks. Many different task group architectures are possible; we focus on tree-like calculations that are suitable for many problems where the range of possible parallelism increases as the calculations progress.

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.

The Influence of Memory-Aware Computation on Distributed BLAST

Background:

One of the pivotal challenges in nowadays genomic research domain is the fast processing of voluminous data such as the ones engendered by high-throughput Next-Generation Sequencing technologies. On the other hand, BLAST (Basic Local Alignment Search Tool), a longestablished and renowned tool in Bioinformatics, has shown to be incredibly slow in this regard.

Objective:

To improve the performance of BLAST in the processing of voluminous data, we have applied a novel memory-aware technique to BLAST for faster parallel processing of voluminous data.

Method:

We have used a master-worker model for the processing of voluminous data alongside a memory-aware technique in which the master partitions the whole data in equal chunks, one chunk for each worker, and consequently each worker further splits and formats its allocated data chunk according to the size of its memory. Each worker searches every split data one-by-one through a list of queries.

Results:

We have chosen a list of queries with different lengths to run insensitive searches in a huge database called UniProtKB/TrEMBL. Our experiments show 20 percent improvement in performance when workers used our proposed memory-aware technique compared to when they were not memory aware. Comparatively, experiments show even higher performance improvement, approximately 50 percent, when we applied our memory-aware technique to mpiBLAST.

Conclusion:

We have shown that memory-awareness in formatting bulky database, when running BLAST, can improve performance significantly, while preventing unexpected crashes in low-memory environments. Even though distributed computing attempts to mitigate search time by partitioning and distributing database portions, our memory-aware technique alleviates negative effects of page-faults on performance.

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.

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

This paper presents an optical processing approach for exploring a large number of genome sequences. Specifically, we propose an optical correlator for global alignment and an extended moiré matching technique for local analysis of spatially coded DNA, whose output is fed to a novel three-dimensional artificial neural network for local DNA alignment. All-optical implementation of the proposed 3D artificial neural network is developed and its accuracy is verified in Zemax. Thanks to its parallel processing capability, the proposed structure performs local alignment of 4 million sequences of 150 base pairs in a few seconds, which is much faster than its electrical counterparts, such as the basic local alignment search tool.