ESA: An efficient sequence alignment algorithm for biological database search on Sunway TaihuLight

Commercial reservoir simulators must be very robust and fast. Moreover, current hardware requires the simulators to scale over multiple number of computing nodes and for a fixed (‘strong scalability’) as well as an increasing problem size per computing node (‘weak scalability’). In most current commercial reservoir simulators, due to the different geological structures and properties of hydrocarbon reservoirs and the use of enhanced oil recovery (EOR) techniques, the governing equations are strongly nonlinear and hard to solve. The Jacobian system is solved by FGMRES preconditioned by the two-level constrained pressure residual (CPR) preconditioner. The driving force of the CPR preconditioner is the solution of the pressure equation. The industry standard for solving the pressure equation is the algebraic multigrid (AMG) solver. AMG is well known for its ‘weak scalability’. However, in these applications, AMG has unfavorable ‘strong’ scalability properties. This degradation in scalability is due to the increased level of inter-processor communication in the algorithm. In this paper, a monotone non-Galerkin AMG (MNG-AMG) method is presented. The aim of the method is to reduce the overall communication in MNG-AMG by enforcing a predefined nonzero pattern and monotonicity property (i.e., M-matrices) on each multigrid level. This paper describes the application of the MNG-AMG method in the context of reservoir simulations. We will compare the parallel scalability of the default solver with the MNG-AMG solver and discuss the optimal values for the MNG-AMG solver for a variety of test cases based on full field reservoir simulations.

Core groups stand as the tangible, but fluid repositories of knowledge, influence and power in organizations. Thus, the core group in any organization is the focal point of organizational learning throughout the organization, because people act to fulfill the perceived needs and priorities of some key group of people. An organization goes wherever its people perceive that the core group needs and wants to go. An organization becomes whatever its people perceive that the core group needs and wants it to become. Core Group Theory holds implications for how the complex of power, knowledge and influence interacts with organizational opportunities for genuine learning and creativity.

The concept of a core group of individuals who change sexual partners frequently has played an important role in the study of the epidemiology of sexually transmitted diseases (STDs). It is widely believed that the core group is important in the persistence of infection and that it provides a sensible target for control interventions. GOAL OF THIS STUDY AND STUDY DESIGN: Simple mathematical models are used to assess the significance of sexual mixing between core and noncore groups. Published data from contact tracing studies, which base the definition of the core group on gonorrhea incidence by area of residence, are used to derive estimates of the degree of mixing within and between groups. Analyses reveal patterns of high mixing within core groups in the urban study sites, which suggests that within-group transmission ensures the persistence of gonococcal infection in the community as a whole. An extension of the analysis of contact tracing data to facilitate the development of mathematical models of STD transmission is detailed.

Core pertussis transmission groups in England and Wales: A tale of two eras

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.

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.

Recent advances in sequencing technologies have stressed the critical role of sequence analysis algorithms and tools in genomics and healthcare research. In particular, sequence alignment is a fundamental building block in many sequence analysis pipelines and is frequently a performance bottleneck both in terms of execution time and memory usage. Classical sequence alignment algorithms are based on dynamic programming and often require quadratic time and memory with respect to the sequence length. As a result, classical sequence alignment algorithms fail to scale with increasing sequence lengths and quickly become memory-bound due to data-movement penalties. Processing-In-Memory (PIM) is an emerging architectural paradigm that seeks to accelerate memory-bound algorithms by bringing computation closer to the data to mitigate data-movement penalties. This work presents BIMSA (Bidirectional In-Memory Sequence Alignment), a PIM design and implementation for the state-of-the-art sequence alignment algorithm BiWFA (Bidirectional Wavefront Alignment), incorporating new hardware-aware optimizations for a production-ready PIM architecture (UPMEM). BIMSA supports aligning sequences up to 100K bases, exceeding the limitations of state-of-the-art PIM implementations. First, BIMSA achieves speedups up to 22.24× (11.95× on average) compared to state-of-the-art PIM-enabled implementations of sequence alignment algorithms. Second, achieves speedups up to 5.84× (2.83× on average) compared to the highest-performance multicore CPU implementation of BiWFA. Third, BIMSA exhibits linear scalability with the number of compute units in memory, enabling further performance improvements with upcoming PIM architectures equipped with more compute units and achieving speedups up to 9.56× (4.7× on average). Code and documentation are publicly available at https://github.com/AlejandroAMarin/BIMSA.

Inverse parametric sequence alignment

A DNA sequence alignment algorithm using quality information and a fuzzy inference method

The continuous development of sequencing technologies has enabled researchers to obtain large amounts of biological sequence data, and this has resulted in increasing demands for software that can perform sequence alignment fast and accurately. A number of algorithms and tools for sequence alignment have been designed to meet the various needs of biologists. Here, the ideas that prevail in the research of sequence alignment and some quality estimation methods for multiple sequence alignment tools are summarized.

Mathematical models have highlighted the disproportionate contribution of core group transmitters to the spread of sexually transmitted diseases. Because the effectiveness of interventions varies with time, it has been suggested...

Objectives: Disease transmission dynamics among members of a sexual network's core group make the group an ideal target for prevention for positives. Identifying the geographic territory of an HIV/AIDS core group is complicated by the numerous factors that may spatially structure cases including alcohol availability. We employ spatial analytic methods in an attempt to identify the geographic distribution of the core HIV/AIDS group. Methods: Five year HIV/AIDS detection rates were analyzed for each HIV/AIDS risk category (i.e., MSM, IDU, HRH) at the census tract level (n = 164) in New Orleans using spatial analytic techniques in multivariate models. Results: MSM was the most common risk category for newly detected HIV cases. Both MSM and IDU cases appeared to decline or enter an endemic phase. Each risk category exhibited unique spatial structure. Among IDUs and HRHs nearly all the spatial structure was explained in terms of the independent variables. However, among MSMs residual spatial structure remained after controlling for independent variables. Conclusions: Residual spatial structure in the MSM HIV/AIDS detection rates after controlling for social structure could be explained by the presence of core group members. The study's limitations are noted.

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%

In this article we investigate the performance of a multicriteria dynamic programming algorithm for pairwise global sequence alignment that maximizes the number of matches and minimizes the number of indels or gaps. We provide estimates on the number of optimal alignments for pairs of random sequences, as well as computational results in a benchmark dataset. Our empirical analysis indicates that this approach is feasible from practical point of view.

Goal: Over the decades, there have been improvements in the sequence alignment algorithm, with significant advances in various aspects such as complexity and accuracy. However, human-defined algorithms have an explicit limitation in view of developmental completeness. This paper introduces a novel local alignment method to obtain optimal sequence alignment based on reinforcement learning. Methods: There is a DQNalign algorithm that learns and performs sequence alignment through deep reinforcement learning. This paper proposes a DQN x-drop algorithm that performs local alignment without human intervention by combining the x-drop algorithm with this DQNalign algorithm. The proposed algorithm performs local alignment by repeatedly observing the subsequences and selecting the next alignment direction until the x-drop algorithm terminates the DQNalign algorithm. This proposed algorithm has an advantage in view of linear computational complexity compared to conventional local alignment algorithms. Results: This paper compares alignment performance (coverage and identity) and complexity for a fair comparison between the proposed DQN x-drop algorithm and the conventional greedy x-drop algorithm. Firstly, we prove the proposed algorithm's superiority by comparing the two algorithms’ computational complexity through numerical analysis. After that, we tested the alignment performance actual HEV and E.coli sequence datasets. The proposed method shows the comparable identity and coverage performance to the conventional alignment method while having linear complexity for the n}{}X parameter. Conclusions: Through this study, it was possible to confirm the possibility of a new local alignment algorithm that minimizes computational complexity without human intervention.