A novel structure of the Smith-Waterman Algorithm for efficient sequence alignment

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.

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

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.

Efficient biological sequence (proteins or DNA) alignment is an important and challenging task in bioinformatics. It is similar to string matching in the context of biological data and is used to infer the evolutionary relationship between a set of protein or DNA sequences. An accurate alignment can provide valuable information for experimentation on the newly found sequences. It is indispensable in basic research as well as in practical applications such as pharmaceutical development, drug discovery, disease prevention and criminal forensics. Many algorithms and methods, such as, dot plot (Gibbs & McIntyre, 1970),Needleman-Wunsch (N-W) (Needleman & Wunsch, 1970), Smith-Waterman (S-W) (Smith & Waterman, 1981), FASTA (Pearson & Lipman, 1985), BLAST (Altschul et al., 1990), HMMER (Eddy, 1998) and ClustalW (Thompson et al., 1994) have been proposed to perform and accelerate sequence alignment activities. An overview of these methods is given in (Hasan et al., 2007). Out of these, S-W algorithm is an optimal sequence alignment method, but its computational cost makes it inappropriate for practical purposes. To develop efficient and optimal sequence alignment solutions, the S-W algorithm has recently been implemented on emerging accelerator platforms such as Field Programmable Gate Arrays (FPGAs), Cell Broadband Engine (Cell/B.E.) and Graphics Processing Units (GPUs) (Buyukkur & Najjar, 2008; Hasan et al., 2010; Liu et al., 2009; 2010; Lu et al., 2008). This chapter aims at providing a broad overview of sequence alignment in general with particular emphasis on the classification and discussion of available methods and their comparison. Further, it reviews in detail the acceleration approaches based on implementations on different platforms and provides a comparison considering different parameters. This chapter is organized as follows: The remainder of this section gives a classification, discussion and comparison of the available methods and their hardware acceleration. Section 2 introduces the S-W algorithm which is the focus of discussion in the succeeding sections. Section 3 reviews CPU-based acceleration. Section 4 provides a review of FPGA-based acceleration. Section 5 overviews GPU-based acceleration. Section 6 presents a comparison of accelerations on different platforms, whereas Section 7 concludes the chapter.

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.

Inverse parametric sequence alignment

Sequence alignment has been widely utilized in biological computing science. To obtain the optimal alignment results many algorithms adopts dynamic programming method to achieve this goal. Smith-Waterman algorithm is the famous in the sequence alignment approach. However, such dynamic programming algorithms are computation-consuming. It is impossible to use these algorithms to compare query sequence with a sequence database such as GenBank and PDB. Recently, GPU computing has been applied in many sequence alignment algorithms to enhance the performance. In this paper, we proposed a GPU-based Smith-Waterman algorithm by combining the CPU and GPU computing capabilities to accelerate alignments on a sequence database. In the proposed algorithm, a filtration mechanism using frequency distance is used to decrease the number of compared sequences. We implemented the Smith-Waterman alignments by CUDA on the NVIDIA Tesla C2050. The experimental results show that the highest speedup ratio is about 80 to 90 times over CPU-based Smith-Waterman algorithm.

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.

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.

Background: Due to the growing amount of biological data, it is often necessary to select the most optimal estimation method for DNA sequence alignment across livestock species. One of the most important benches of genomics is to modelling homology between considered DNA sequences. A multiple sequence alignment is a potent tool for molecular and evolutionary biology, and there are several programs and algorithms applicable for this purpose. The purpose of this paper was to study the most commonly used DNA alignment algorithms to select the optimal tool dedicated for short sequences.Methods: Four steps of bioinformatics pipelines were considered to benchmark the algorithms for multiple DNA sequence alignment across livestock species: 1) selection of reference genome sequences of ARS1.2 for cattle, EquCab3.0 for horse and vicPac2 for alpaca with a low E-value using TBLASTn 2) removing gaps for these sequences 3) alignment of obtained sequences using examined algorithms 4) matching the quality of aligned sequences with sequences of reference genomes by more software. The time of computation was archived for the whole analysis. The seven programs were utilized, each based on different alignment algorithms, namely: ClustalO, ClustalW, Kalign, MAFFT, MUSCLE, Probcons and T-Coffee.Results: The result obtained in this study showed that the fastest is progressive algorithms such as Kalign or MUSCLE-FAST. Moreover, the iterative algorithms like MAFFT and MUSCLE revealed a higher quality of the alignment. The T-Coffee and Probcons programs were computational cost-effective; simultaneously, they were generating a medium-quality calculation in a relatively long time. The best quality of alignment was shown by iterative variants of the MAFFT program; however, the speed of the calculations was relatively low. The fastest algorithm was Kalign, making alignment much faster than the competitors, but achieving average results in the quality of the alignment. The average speed ratio concerning the quality of the analyzed algorithms was obtained by the progressive version of MAFFT, NS1.Conclusions: We conclude that the results of this study can be used to re-alignment of variant primers in new livestock genome releases.

SummaryIn this paper, we present GSWABE, a graphics processing unit (GPU)‐accelerated pairwise sequence alignment algorithm for a collection of short DNA sequences. This algorithm supports all‐to‐all pairwise global, semi‐global and local alignment, and retrieves optimal alignments on Compute Unified Device Architecture (CUDA)‐enabled GPUs. All of the three alignment types are based on dynamic programming and share almost the same computational pattern. Thus, we have investigated a general tile‐based approach to facilitating fast alignment by deeply exploring the powerful compute capability of CUDA‐enabled GPUs. The performance of GSWABE has been evaluated on a Kepler‐based Tesla K40 GPU using a variety of short DNA sequence datasets. The results show that our algorithm can yield a performance of up to 59.1 billions cell updates per second (GCUPS), 58.5 GCUPS and 50.3 GCUPS for global, semi‐global and local alignment, respectively. Furthermore, on the same system GSWABE runs up to 156.0 times faster than the Streaming SIMD Extensions (SSE)‐based SSW library and up to 102.4 times faster than the CUDA‐based MSA‐CUDA (the first stage) in terms of local alignment. Compared with the CUDA‐based gpu‐pairAlign, GSWABE demonstrates stable and consistent speedups with a maximum speedup of 11.2, 10.7, and 10.6 for global, semi‐global, and local alignment, respectively. Copyright © 2014 John Wiley & Sons, Ltd.

Phthiocerol dimycocerosates (PDIMs) and phenolic glycolipids (PGLs) are structurally related lipids noncovalently bound to the outer cell wall layer of Mycobacterium tuberculosis, Mycobacterium leprae, and several opportunistic mycobacterial human pathogens. PDIMs and PGLs are important effectors of virulence. Elucidation of the biosynthesis of these complex lipids will not only expand our understanding of mycobacterial cell wall biosynthesis, but it may also illuminate potential routes to novel therapeutics against mycobacterial infections. We report the construction of an in-frame deletion mutant of tesA (encoding a type II thioesterase) in the opportunistic human pathogen Mycobacterium marinum and the characterization of this mutant and its corresponding complemented strain control in terms of PDIM and PGL production. The growth and antibiotic susceptibility of these strains were also probed and compared with the parental wild-type strain. We show that deletion of tesA leads to a mutant that produces only traces of PDIMs and PGLs, has a slight growth yield increase and displays a substantial hypersusceptibility to several antibiotics. We also provide a robust model for the three-dimensional structure of M. marinum TesA (TesAmm) and demonstrate that a Ser-to-Ala substitution in the predicted catalytic Ser of TesAmm renders a mutant that recapitulates the phenotype of the tesA deletion mutant. Overall, our studies demonstrate a critical role for tesA in mycobacterial biology, advance our understanding of the biosynthesis of an important group of polyketide synthase-derived mycobacterial lipids, and suggest that drugs aimed at blocking PDIM and/or PGL production might synergize with antibiotic therapy in the control of mycobacterial infections.

The level of conservation between two homologous sequences often varies among sequence regions; functionally important domains are more conserved than the remaining regions. Thus, multiple parameter sets should be used in alignment of homologous sequences with a stringent parameter set for highly conserved regions and a moderate parameter set for weakly conserved regions. We describe an alignment algorithm to allow dynamic use of multiple parameter sets with different levels of stringency in computation of an optimal alignment of two sequences. The algorithm dynamically considers various candidate alignments, partitions each candidate alignment into sections, and determines the most appropriate set of parameter values for each section of the alignment. The algorithm and its local alignment version are implemented in a computer program named GAP4. The local alignment algorithm in GAP4, that in its predecessor GAP3, and an ordinary local alignment program SIM were evaluated on 257 716 pairs of homologous sequences from 100 protein families. On 168 475 of the 257 716 pairs (a rate of 65.4%), alignments from GAP4 were more statistically significant than alignments from GAP3 and SIM.

This paper present the design and analysis of 8-bit Smith Waterman (SW) based DNA sequence alignment accelerator's core on ASIC design flow. The objective of the project is to construct and analyse the core module that can perform the Smith Waterman algorithm's operations, which are comparing, scoring and back tracing, using the technique used in (1,2) on ASIC design flow. Nowadays, the DNA and protein databases are increasing rapidly and these add new challenges to the current computing resources. New techniques, algorithms, designs, hardware and software that can maximize the computational speed, minimize the power and energy consumption, and boost the throughput need to be developed in order to meet the current and future requirements. In DNA sequence alignment process, the DNA sequences are compared using different alignment requirement techniques such as global alignment, local alignment, motif alignment and multiple sequence alignment. Moreover, there are several algorithms used to perform the sequence alignment process such as Needleman- Wunch algorithm, Smith Waterman algorithm, FASTA, BLAST and so on. For this paper, the focus is on local alignment using Smith Waterman algorithm. The design was modelled using Verilog and the functionality was verified using Xilinx and VCS. The RTL codes was mapped and synthesized to technology based logics using Design Compiler (DC). The core's layout was implemented using Place and Route tool, IC Compiler (ICC). Based on the results, the core design area was 2108.937620 um 2 .The maximum time constraints were 6.85 ns and 6.93 ns in ICC and PT. The minimum time constraints were 0.28 ns and 0.30 ns in ICC and PT respectively. In conclusion, the design had been successfully implemented on ASIC design flow. Moreover, the results showed that the design can be further optimized to work at faster speeds.

The Smith-Waterman (SW) algorithm is a typical technique for local sequence alignment in computational biology. However, the SW algorithm does not consider the local behaviours of the amino acids, which may result in loss of some useful information. Inspired by the success of Markov Edit Distance (MED) method, this paper therefore proposes a novel Markov pairwise protein sequence alignment (MPPSA) method that takes the local context dependencies into consideration. The numerical results have shown its superiority to the SW for pairwise protein sequence comparison.