Energy efficiency of sequence alignment tools—Software and hardware perspectives

Scientific collaboration is critical in high performance computational (HPC) environments. Computer security incidents, however, are a constant threat in today's interconnected computational environments. Many sites struggle with finding a balance between the needs of users and the threat from unauthorized usage or access. Understanding the constantly changing battlefield is critical for effective and optimal deployment of computer security resources.This tutorial will provide an underlying knowledge of the current state of computer security, its effects on HPC environments and mitigation strategies. Topics we will address include:1.HPC Security Fundamentals2.HPC Security Protection Fundamentals3.Intrusion Detection Using Bro4.The Changing Computer Protection Environment5.Cross-site Implications of Collaborative Environments6.Keeping One Step Ahead7.Risk Assessment, Mitigation and Compliance8.Components of a Good Protection ProgramWe will also show how described techniques are in use at SC06 to protect SCinet.

While pairwise sequence alignment (PSA) by dynamic programming is guaranteed to generate one of the optimal alignments, multiple sequence alignment (MSA) of highly divergent sequences often results in poorly aligned sequences, plaguing all subsequent phylogenetic analysis. One way to avoid this problem is to use only PSA to reconstruct phylogenetic trees, which can only be done with distance-based methods. I compared the accuracy of this new computational approach (named PhyPA for phylogenetics by pairwise alignment) against the maximum likelihood method using MSA (the ML+MSA approach), based on nucleotide, amino acid and codon sequences simulated with different topologies and tree lengths. I present a surprising discovery that the fast PhyPA method consistently outperforms the slow ML+MSA approach for highly diverged sequences even when all optimization options were turned on for the ML+MSA approach. Only when sequences are not highly diverged (i.e., when a reliable MSA can be obtained) does the ML+MSA approach outperforms PhyPA. The true topologies are always recovered by ML with the true alignment from the simulation. However, with MSA derived from alignment programs such as MAFFT or MUSCLE, the recovered topology consistently has higher likelihood than that for the true topology. Thus, the failure to recover the true topology by the ML+MSA is not because of insufficient search of tree space, but by the distortion of phylogenetic signal by MSA methods. I have implemented in DAMBE PhyPA and two approaches making use of multi-gene data sets to derive phylogenetic support for subtrees equivalent to resampling techniques such as bootstrapping and jackknifing.

98P - Standardized and compliant bio-informatics pipeline for neoepitope analysis in oncology clinical trials

This chapter introduces foundational algorithms that enable pairwise alignment of protein sequences using substitution matrices. It explains the sliding window approach, which facilitates the visualisation of sequence properties, such as GC-rich regions or hydrophobic protein segments and supports dot plot generation to reveal similarities between sequences. The chapter details the Needleman–Wunsch global alignment algorithm and its modified version, both of which align sequences across their entire lengths using scoring matrices and gap penalties. It also outlines the ends-free and Smith–Waterman local alignment algorithms, which detect partial matches, particularly in modular proteins. Finally, the chapter explores coding strategies and algorithm complexity, highlighting efficiency considerations in sequence alignment programs.

Ths paper aims to find similarity of Hepatitis B virus (HBV) and Hepatocelluler Carcinoma (HCC) DNA sequences.The similarity of sequence allignments indicates that they have similarity of chemical and physical properties. Mutation of the virus DNA in X region has potential role in HCC. It is observed using pairwise sequence alignment of genotype-A in HBV. This paper is to purpose the modified method of Needleman Wunsch algorithm for optimum global DNA sequence alignment. The main idea is to optimize filling matrix and backtracking proccess of DNA components, so that there is reduction of computational time and space complexity. This research is applied to DNA sequence of 858 hepatitis B virus and 12 carcinoma patient. There are 10,296 pairwise of DNA sequences to be aligned globally using the modified method. As a result, it is achieved high similarity of 96.547% and validity of 99.854%. There is reduction of computational time as 34.6% and space complexity as 42.52%

A group of 16-kDa proteins, synthesized and secreted by rat pancreatic acinar cells and composed of pancreatic stone protein (PSP/reg) and isoforms of pancreatitis-associated protein (PAP), show structural homologies, including conserved amino acid sequences, cysteine residues, and highly sensitive N-terminal trypsin cleavage sites, as well as conserved functional responses in conditions of pancreatic stress. Trypsin activation of recombinant stress proteins or counterparts contained in rat pancreatic juice (PSP/reg, PAP I and PAP III) resulted in conversion of 16-kDa soluble proteins into 14-kDa soluble isoforms (pancreatic thread protein and pancreatitis-associated thread protein, respectively) that rapidly polymerize into insoluble sedimenting structures. Activated thread proteins show long lived resistance to a wide spectrum of proteases contained in pancreatic juice, including serine proteases and metalloproteinases. In contrast, PAP II, following activation with trypsin or pancreatic juice, does not form insoluble structures and is rapidly digested by pancreatic proteases. Scanning and transmission electron microscopy indicate that activated thread proteins polymerize into highly organized fibrillar structures with helical configurations. Through bundling, branching, and extension processes, these fibrillar structures form dense matrices that span large topological surfaces. These findings suggest that PSP/reg and PAP I and III isoforms consist of a family of highly regulated soluble secretory stress proteins, which, upon trypsin activation, convert into a family of insoluble helical thread proteins. Dense extracellular matrices, composed of helical thread proteins organized into higher ordered matrix structures, may serve physiological functions within luminal compartments in the exocrine pancreas.

The efficient implementation of a diffraction tomography method for medical imaging is addressed within the framework of High Performance Computing (HPC) environment. A non-linear optimization method for the solution of the inverse scattering problem is implemented on a shared memory model computer. Linear speed-up and significant reduction in the total execution time is achieved when the program is executed in parallel, enabling the feasibility of the method for realistic medical imaging applications.

In this paper, we investigate the feasibility of computing DNA patterns on FPGAs by utilising high-performance and parallel computing. In particular, the report analyses and contrasts the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the dynamic and heuristic alternatives that are currently on the market. In this research project, we investigate the use of parallel and high-performance computing to determine how DNA patterns can be computed on FPGAs. In particular, the report details the findings of a study that compared the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the algorithm's current dynamic and heuristic programming methods. The study was carried out by comparing these three aspects of the algorithm. The traditional methods of sequence alignment that are software-implied are not adequate to meet the actual data rate requirements. A strategy that is based on the use of hardware makes it possible to process multiple new databases in parallel, which enables a high degree of scalability. A finite state machine serves as the primary data processor in our demonstration of how to organize the sequences of amino acids in a protein (FSM). We also investigate the effectiveness of bit-based sequence alignment algorithms and present the inner stage pipelined FPGA architecture for implementing sequence alignment. Both of these are presented in this paper. Synchronized controllers are utilized in the programming techniques of the algorithm for the purpose of parallel sequence alignment. Inference is the foundation of traditional software-based methods for sequence alignment, but these methods are unable to keep up with the required data rates. A strategy that is based on hardware allows for the processing of parallel tasks with a large number of new databases and provides excellent scalability. Our explanation of the classification of protein sequences is based on a Finite State Machine, which serves as the primary processing unit (FSM). In addition to this, we investigate how well bit-based sequence alignment algorithms work and present the inner stage pipelined Field Programmable Gate Array architecture that can be used to implement sequence alignment. Synchronized controllers are utilized in the process of performing sequence alignment in parallel. The architecture as a whole was made to be scalable and fast, with a small number of computations and the ability to perform parallel processing in hardware using field-programmable gate arrays to compare bits in patterns. Finally, we use FPGA hardware synthesis to show off impressive performance and low resource consumption.

Abstract. Most Earth system models (ESMs) are running under different high-performance computing (HPC) environments. This has several advantages, from allowing different groups to work with the same tool in parallel to leveraging the burden of ensemble climate simulations, but it also offers alternative solutions in the case of shutdown (expected or not) of any of the environments. However, for obvious scientific reasons, it is critical to ensure that ESMs provide identical results under changes in computing environment. While strict bit-for-bit reproducibility is not always guaranteed with ESMs, it is desirable that results obtained under one computing environment are at least statistically indistinguishable from those obtained under another environment, which we term a “replicability” condition following the metrology nomenclature. Here, we develop a protocol to assess the replicability of the EC-Earth ESM. Using two versions of EC-Earth, we present one case of non-replicability and one case of replicability. The non-replicable case occurs with the older version of the model and likely finds its origin in the treatment of river runoff along Antarctic coasts. By contrast, the more recent version of the model provides replicable results. The methodology presented here has been adopted as a standard test by the EC-Earth consortium (27 institutions in Europe) to evaluate the replicability of any new model version across platforms, including for CMIP6 experiments. To a larger extent, it can be used to assess whether other ESMs can safely be ported from one HPC environment to another for studying climate-related questions. Our results and experience with this work suggest that the default assumption should be that ESMs are not replicable under changes in the HPC environment, until proven otherwise.

Pairwise sequence alignment is a fundamental compute-intensive problem in bioinformatics that has helped researchers analyse biological sequences. The analysis has helped biologists detect pathogens, develop drugs, and identify common genes. The biological sequence database has been growing rapidly due to new sequences being discovered. This has brought many new challenges including sequence database searching and aligning long sequences. To solve these problems, many sequence alignment algorithms have been developed. These algorithms employ various techniques to efficiently find optimal or nearly-optimal alignments. In this paper, we present the popular past and recent work on both local and global pairwise sequence alignment algorithms. In addition to identifying the techniques used, the advantages and limitations of the algorithms are also presented.

Purpose The present paper aims to address the description of a numerical optimization procedure, based on mesh morphing, and its application for the improvement of the aerodynamic performance of an industrial glider which suffers of a large separation occurring in the wing–fuselage junction region at high incidence angles. Design/methodology/approach Shape variations were applied to the baseline configuration through a mesh morphing technique founded on the mathematical framework of radial basis functions (RBF). The aerodynamic solutions were obtained coupling an RANS code with the mesh morphing tool RBF Morph™. Two shape modifiers were set up to generate a parametric numerical model. An optimization procedure, based on a design of experiment sampling, was set up implementing the fully automated workflow within a high performance computing (HPC) environment. The optimal candidates maximizing the aerodynamic efficiency were identified by means of a cubic RBF response surface approach. Findings The separation was significantly reduced, modifying the local geometry of fuselage and fairing and maintaining the wing aerofoil unchanged. A relevant aerodynamic efficiency improvement was finally gained. Practical implications The developed procedure proved to be a very powerful and efficient tool in facing aerodynamic design problems. However, it might be computationally very expensive if a large number of design variables are adopted and, in those cases, the method can be suitably used only within the HPC environment. Originality/value Such an optimization study is part of an explorative set of analyses that focused on better addressing the numerical strategies to be used in the development of the EU FP7 Project RBF4AERO.

With an increase in the number of known biological functions of non-coding RNAs, the importance of RNA sequence alignment has risen. RNA sequence alignment problem has been investigated by many researchers as a mono-objective optimization problem where contributions from sequence similarity and secondary structure are taken into account through a single objective function. Since there is a trade-off between these two objective functions, usually we cannot obtain a single solution that has both the best sequence similarity score and the best structure score simultaneously. Multi-objective optimization is a widely used framework for the optimization problems with conflicting objective functions. So far, no one has examined how good alignments we can obtain by applying multi-objective optimization to structural RNA sequence alignment problem. We developed a pairwise RNA sequence alignment program, Cofolga2mo, based on multi-objective genetic algorithm (MOGA). We tested Cofolga2mo with a benchmark dataset which includes sequence pairs with a wide range of sequence identity, and we obtained at most 100 alignments for each inputted RNA sequence pair as an approximate set of weak Pareto optimal solutions. We found that the alignments in the approximate set give benchmark results comparable to those obtained by the state-of-the-art mono-objective RNA alignment algorithms. Moreover, we found that our algorithm is efficient in both time and memory usage compared to the other methods. Our MOGA programs for structural RNA sequence alignment can be downloaded at http://rna.eit.hirosaki-u.ac.jp/cofolga2mo/.

Containers in High Performance Computing (HPC) provide a flexible and efficient alternative to traditional modular software. They allow researchers and engineers to package applications and their dependencies in a single file, ensuring portability, reproducibility and consistent performance across different computing environments. With the emergence of AI, industrial users and SMEs in HPC, containers have become even more convenient to use, as they can also meet the security requirements for isolating user workloads. As the HPC community embraces container technology, it is ushering in a new era of more efficient, repeatable and scalable research computing. However, adoption of containers in HPC has been relatively slow, largely due to misconceptions about how they work and concerns about potential performance degradation, energy inefficiency and security risks. This article looks at the security considerations when using containers and uses practical examples to show that, with the right configuration and tools, containers can be as secure as any other system application.

SummaryThe wide range of RNA-seq applications and their high-computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project-oriented pipeline for processing of RNA-seq data in high-performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high-performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing and sequence variant calling. aRNApipe is project-oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis.Availability and documentationhttps://github.com/HudsonAlpha/aRNAPipe; DOI: 10.5281/zenodo.202950Supplementary informationSupplementary data are available at Bioinformatics online.

Abstract: This chapter explores the design and development of photonic processors for ultra-fast data processing in high-performance computing (HPC) environments. By leveraging light instead of electricity for computation, photonic processors offer significant advantages in terms of speed, bandwidth, and energy efficiency. The chapter covers advancements in photonic materials, device engineering, and optical interconnects that enable scalable photonic systems for real-world applications, including AI, machine learning, and quantum computing. Key topics include the integration of photonic processors with quantum communication networks, the role of photonic multiplexing in parallel data processing, and innovations such as all-optical neurons for neuromorphic computing. The chapter also addresses current challenges, including phase sensitivity and the development of photonic quantum gates, offering solutions for scaling photonic computing to meet the demands of HPC and quantum networks. Keywords: photonic processors, high-performance computing, ultra-fast data processing, optical interconnects, photonic materials, quantum communication, AI, machine learning, photonic multiplexing, neuromorphic computing, phase sensitivity, quantum gates, scalable systems.