Time and Space Efficient Algorithms for Constrained Sequence Alignment

Constrained sequence alignment has been proposed as a way for incorporating biologists' knowledge about common structures or functions into the alignment process. For alignment of protein sequences, several studies have suggested taking into account the motifs (a restricted regular expression) from the PROSITE database to guide alignments. The regular expression constrained sequence alignment has been introduced for this purpose. An alignment satisfies the constraint if part of it matches a given regular expression in each dimension (i.e. in each sequence aligned). There is a method that rewards the alignments that include a region matching the given regular expression. This method does not always guarantee the satisfaction of the constraint. Another method constructs a weighted finite automaton from the given regular expression, and presents a dynamic programming solution that simulates copies of this automaton to find an alignment with maximum score satisfying the regular expression constraint. We propose a new algorithm for the regular expression constrained multiple sequence alignment problem. Our algorithm considers two layers each of which corresponds to part of the dynamic programming matrix for the alignment of the given sequences. We compute each layer differently using dynamic programming. We propose the following modification in the definition of the problem: the region satisfying the constraint does not contribute to the total score. This modification is not necessary for the correctness and the performance in certain cases such as the constraint involves only one motif or motif-matching regions span short distance in each sequence but we believe that with this modification we achieve the same goal by doing less work in practice. Our algorithm is much more efficient than a previously proposed algorithm that uses weighted automata, and its performance in practice is comparable to (and under certain conditions even better than) that of the ordinary (unconstrained) multiple sequence alignment algorithm. Our experiments on real biological sequences, and regular expressions each composed of a sequence of motifs verify this

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%

Inverse parametric sequence alignment

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

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.

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.

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.

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.

The emergence of bioinformatics has led to many new discoveries in living organisms. These discoveries would not have been possible without the developments made in the sequence alignment techniques. Many sequence alignment algorithms were developed to make the alignment process fast and accurate. However, the more precise algorithms take longer than their less precise counterparts. Researchers came with innovative approaches to combat the time consuming constraint. Their aim was to speed up the computational process by using more efficient implementations of the algorithms using state-of-the-art hardware platforms. Smith Waterman (SW) algorithm, being the most accurate in the alignment process, has been implemented on various high performance computing platforms for the same purpose. However, the intrinsic structure of the algorithm has got little attention. In this paper, we present a novel structure of the SW algorithm that takes less number of cycles at the cost of utilizing a minimal amount of extra hardware resources as compared to its existing form. The newly proposed architecture achieves up to 25% performance gain.

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.

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.

Over the recent decades, bioinformatics has acquired a major concern due to the rapid growth in biological data that includes protein structures and genome sequences. Many considerable efforts have been conducted by computer scientists, mathematicians and biologists to coup with complex biological problems such as sequence alignment problem, using several techniques to formulate/model the targeted biological problems as computational problems and design algorithms to solve them in an accurate and efficient manner. Needleman-Wunsch algorithm as well as other alignment algorithms have been the subject of many studies to improve their performance due to their importance and the large scale of the data they have to handle (e.g., aligning strings of hundreds of thousands of characters). Approaches included a mixture of different parallel implementations using specialized hardware such as Graphical Processing Units (GPUs) and a vectorized approach of reading and processing the input data. In this work, a parallel implementation of NW algorithm is presented using GPU due to its efficiency and high speed, to solve the slowness problem associated with this algorithm when processing large data sets, as well as to enhance the performance of the algorithm especially when processing vectors of adjacent cells parallel to the matrix miner diagonal. The experiments show that the proposed implementation improves the performance of the algorithm by 99%.

This article presents an immune inspired algorithm to tackle the Multiple Sequence Alignment (MSA) problem. MSA is one of the most important tasks in biological sequence analysis. Although this paper focuses on protein alignments, most of the discussion and methodology may also be applied to DNA alignments. The problem of finding the multiple alignment was investigated in the study by Bonizzoni and Vedova and Wang and Jiang, and proved to be a NP-hard (non-deterministic polynomial-time hard) problem. The presented algorithm, called Immunological Multiple Sequence Alignment Algorithm (IMSA), incorporates two new strategies to create the initial population and specific ad hoc mutation operators. It is based on the ‘weighted sum of pairs’ as objective function, to evaluate a given candidate alignment. IMSA was tested using both classical benchmarks of BAliBASE (versions 1.0, 2.0 and 3.0), and experimental results indicate that it is comparable with state-of-the-art multiple alignment algorithms, in terms of quality of alignments, weighted Sums-of-Pairs (SP) and Column Score (CS) values. The main novelty of IMSA is its ability to generate more than a single suboptimal alignment, for every MSA instance; this behaviour is due to the stochastic nature of the algorithm and of the populations evolved during the convergence process. This feature will help the decision maker to assess and select a biologically relevant multiple sequence alignment. Finally, the designed algorithm can be used as a local search procedure to properly explore promising alignments of the search space.

Multiple alignment of biological sequences is an interesting area of research for application of parallel processing algorithms. For multiple alignment of sequences, parallelism can be introduced at different levels to reduce the overall time. This paper is concerned with the comparison of an MPI-based multiple sequence alignment algorithm by using single and multiple guide trees. In this work, we will look at the application of Divide-and-Conquer algorithm for multiple sequence alignment and discuss two different implementations on MPI. We compare the speed up of the two approaches and we contrast the quality of the alignments for these two approaches. These two approaches differ in using one single tree for alignment vs. multiple trees for alignment that we further discuss in this paper.

Multiple sequence alignment (MSA) methods infer the homologous regions within DNA and protein sequences. Among the other algorithms for MSA, the center star method (CSM) is useful when processing a large number of sequences. In this method, the partially conserved regions among the non-center sequences tend to hide in the alignment. Therefore, existence of subsets of similar sequences in the input set leads to a significant accuracy loss. As a solution to the above problem, this research introduces an algorithm for MSA based on a solution proposed by D. Gusfield in 1991. In this research, the sequences are first grouped into subsets, aligned separately using CSM and finally these alignments are merged by applying progressive alignment procedure. The detailed solution for this problem is presented in the following section.