Local Sequence Alignment Algorithm Research Articles

An efficient and robust parallel scheduler for bioinformatics applications in a public cloud: A bigdata approach

<p>In bioinformatics, genomic sequence alignment is a simple method for handling and analysing data, and it is one of the most important applications in determining the structure and function of protein sequences and nucleic acids. The basic local alignment search tool (BLAST) algorithm, which is one of the most frequently used local sequence alignment algorithms, is covered in detail here. Currently, the NCBI's BLAST algorithm (standalone) is unable to handle biological data in the terabytes. To address this problem, a variety of schedulers have been proposed. Existing sequencing approaches are based on the Hadoop MapReduce (MR) framework, which enables a diverse set of applications and employs a serial execution strategy that takes a long time and consumes a lot of computing resources. The author, improves the BLAST algorithm based on the BLAST-BSPMR algorithm to achieve the BLAST algorithm. To address the issue with Hadoop's MapReduce framework, a customised MapReduce framework is developed on the Azure cloud platform. The experiment findings indicate that the suggested bulk synchronous parallel MapReduce-basic local alignment search tool (BSPMR-BLAST) algorithm matches bioinformatics genomic sequences more quickly than the existing Hadoop-BLAST method, and that the proposed customised scheduler is extremely stable and scalable.</p>

A biological sub-sequences detection using integrated BA-PSO based on infection propagation mechanism: Case study COVID-19

The longest common consecutive subsequences (LCCS) play a vital role in revealing the biological relationships between DNA/RNA sequences especially the newly discovered ones such as COVID-19. FLAT is a Fragmented local aligner technique which is an accelerated version of the local pairwise sequence alignment algorithm based on meta-heuristic algorithms. The performance of FLAT needs to be enhanced since the huge length of biological sequences leads to trapping in local optima. This paper introduces a modified version of FLAT based on improving the performance of the BA algorithm by integration with particle swarm optimization (PSO) algorithm based on a novel infection mechanism. The proposed algorithm, named BPINF, depends on finding the best-explored solution using BA operators which can infect the agents during the exploitation phase using PSO operators to move toward it instead of moving toward the best-exploited solution. Hence, moving the solutions toward the two best solutions increase the diversity of generated solutions and avoids trapping in local optima. The infection can be propagated through the agents where each infected agent can transfer the infection to other non-infected agents which enhances the diversification of generated solutions. FLAT using the proposed technique (BPINF) was validated to detect LCCS between a set of real biological sequences with huge lengths besides COVID-19 and other well-known viruses. The performance of BPINF was compared to the enhanced versions of BA in the literature and the relevant studies of FLAT. It has a preponderance to find the LCCS with the highest percentage (88%) which is better than other state-of-the-art methods.

Sequence alignment detection of TCP-targeted synchronous low-rate DoS attacks

TCP-targeted Low-rate Denial of Service (LDoS) attack launches periodic flows at a sufficiently low average rate that completely spread into large network traffic at the attack end. Hence, LDoS attack traffic has significant periodicity, low transmission rate, and wide distribution. This paper investigates the characteristics of LDoS attacks from a sequence matching perspective. Based on the analysis of sequence similarity between distributed LDoS attack pulses at the victim end, we find that the technique of sequence alignment detection in bioinformatics can be used to detect LDoS attack flows. We presented an approach of LDoS attack detection by using the Smith-Waterman local sequence alignment algorithm that obtaining the similarity score of two sequences. Through this approach, the locally constructed detection sequence is compared with the background flow, especially the three characteristics of the pulse period, length and amplitude of the constructed detection sequence are used as benchmarks to accurately estimate the characteristic parameters of the LDoS attack. A double threshold rule is designed and compared with the calculated maximum similarity score value. Thereby achieving the purpose of detecting an LDoS attack. We build a simulation platform and test-bed to validate the proposed approach. We designed the simulation platform and built the test-bed separately to test and verify the performances of the proposed approach. Experimental results show that this proposed approach has high accuracy and F1 score.

Dynamics based clustering of globin family members.

A methodology to cluster proteins based on their dynamics’ similarity is presented. For each pair of proteins from a dataset, the structures are superimposed, and the Anisotropic Network Model modes of motions are calculated. The twelve slowest modes from each protein are matched using a local mode alignment algorithm based on the local sequence alignment algorithm of Smith–Waterman. The dynamical similarity distance matrix is calculated based on the top scoring matches of each pair and the proteins are clustered using a hierarchical clustering algorithm. The utility of this method is exemplified on a dataset of protein chains from the globin family and a dataset of tetrameric hemoglobins. The results demonstrate the effect of the quaternary structure of globin members on their intrinsic dynamics and show good ability to distinguish between different states of hemoglobin, revealing the dynamical relations between them.

Accelerating the Smith-Waterman algorithm with interpair pruning and band optimization for the all-pairs comparison of base sequences.

BackgroundThe Smith-Waterman algorithm is known to be a more sensitive approach than heuristic algorithms for local sequence alignment algorithms. Despite its sensitivity, a greater time complexity associated with the Smith-Waterman algorithm prevents its application to the all-pairs comparisons of base sequences, which aids in the construction of accurate phylogenetic trees. The aim of this study is to achieve greater acceleration using the Smith-Waterman algorithm (by realizing interpair block pruning and band optimization) compared with that achieved using a previous method that performs intrapair block pruning on graphics processing units (GPUs).ResultsWe present an interpair optimization method for the Smith-Waterman algorithm with the aim of accelerating the all-pairs comparison of base sequences. Given the results of the pairs of sequences, our method realizes efficient block pruning by computing a lower bound for other pairs that have not yet been processed. This lower bound is further used for band optimization. We integrated our interpair optimization method into SW#, a previous GPU-based implementation that employs variants of a banded Smith-Waterman algorithm and a banded Myers-Miller algorithm. Evaluation using the six genomes of Bacillus anthracis shows that our method pruned 88 % of the matrix cells on a single GPU and 73 % of the matrix cells on two GPUs. For the genomes of the human chromosome 21, the alignment performance reached 202 giga-cell updates per second (GCUPS) on two Tesla K40 GPUs.ConclusionsEfficient interpair pruning and band optimization makes it possible to complete the all-pairs comparisons of the sequences of the same species 1.2 times faster than the intrapair pruning method. This acceleration was achieved at the first phase of SW#, where our method significantly improved the initial lower bound. However, our interpair optimization was not effective for the comparison of the sequences of different species such as comparing human, chimpanzee, and gorilla. Consequently, our method is useful in accelerating the applications that require optimal local alignments scores for the same species. The source code is available for download from http://www-hagi.ist.osaka-u.ac.jp/research/code/.

Global and local sequence alignment with a bounded number of gaps

Pairwise sequence alignment techniques have gained renewed interest in recent years, primarily due to their applications in re-sequencing—the assembly of a genome directed by a reference sequence.In this article, we show that adding the flexibility of bounding the number of gaps inserted in an alignment strengthens the classical sequence alignment scheme of scoring matrices and affine gap penalty scores. We present GapsMis, an algorithm for pairwise global sequence alignment with a variable, but bounded, number of gaps. It is based on computing a variant of the traditional dynamic programming matrix for global sequence alignment. We also present GapsMis-L, the analogous algorithm for pairwise local sequence alignment with a variable, but bounded, number of gaps.To test the accuracy of GapsMis and GapsMis-L we performed millions of pairwise sequence alignments under realistic conditions, based on the properties of real full-length genomes. The results show that GapsMis and GapsMis-L can increase the accuracy of extending short-read alignments compared to the traditional approaches. The importance of our contribution is underlined by the fact that the provided algorithms may be seamlessly integrated into any biological pipeline. The open-source code of our implementation is freely available at http://www.inf.kcl.ac.uk/research/projects/gapmis/.

Algorithms for global protein–protein interaction network alignment

Protein–protein interaction creates a complex relation among molecular organs. The pivotal functionalities among cells depend on these interactions. In the light of computational biology (CB), it is possible to retrieve the exact information regarding these relationships. The global alignments among proteins are more important. Toward the development of the PPI network analysis, various methods such as heuristics, evolutionary, probabilistic, semi-probabilistic, spectral graph analysis and mapping methods have been developed and this is an ongoing process of progress. Some remarkable contributions to the PPI global network alignments are Common neighbors-based global GRAph (C-GRAAL), GRAph ALigner (GRAAL), Hungarian algorithm-based GRAAL (H-GRAAL), Matching-based GRAph aligner (M-GRAAL), IsoRankN, IsoRank, Scalable Global alignment algorithm, SMETENA, (software package) and GraphCrunch 2 (software package). All the mentioned algorithms and software package mentioned above have tried to address the best illustration and mapping between protein networks for global protein network alignment. For simplicity we avoid local sequence alignment methods and algorithms. For experimental data analysis, five eukaryotic species such as C. elegans (CE), D. melanogaster (DM), S. cerevisiae (SC), H. sapiens (HS) and M. musculus (MM) have been considered.

An integrated algorithm for local sequence alignment

Local sequence alignment (LSA) is an essential part of DNA sequencing. LSA helps to identify the facts in biological identity, criminal investigations, disease identification, drug design and research. Large volume of biological data makes difficulties to the performance of efficient analysis and proper management of data in small space has become a serious issue. We have subdivided the data sets into various segments to reduce the data sets as well as for efficient memory use. The integration of dynamic programming (DP) and Chapman–Kolmogorov equations (CKE) makes the analysis faster. The subdivision process is named data reducing process (DRP). DRP is imposed before DP and CKE. This approach needs less space compared with other methods and the time requirement is also improved.

Coarse Grain Load Balance Algorithm for Detecting Similar Regions in DNA and Proteins Sequences

The tremendous quantity and quality of data obtained by conformations of DNA and protein sequences makes their analysis very time consuming, complex, expensive and impractical. Therefore, the feasible way to identify new sequences is to compare them with well-known sequences available in established genetic databank. Comparing sequences may reveal functional, structural, and evolutionary analogies between sequences. Needleman and Wunsch (NW), and Smith and Waterman (SW) algorithms are pioneers in dynamic programming matrix for comparing two sequences with gap penalty function. However, for long sequences both methods contribute toward time and space complexity. FASTA and BLAST are heuristic method based on hitting techniques for fast detection of similar region; unfortunately they produced results with no sensitivity. There remains a need for an efficient method(s) that can detect similar regions in two sequences with accurate results and reasonable time. In this paper, we extend an existing approach to develop an efficient parallel algorithm for pairwise local sequence alignment. Our method is based on load-balancing algorithm with the CPU scheduling technique in order to accelerate the calculation of data-dependency problem in sequences alignment. Using X86-based PC with eight logical processors we able to apply 4 MBP on the proposed algorithm with a speedup of 33% increase compared to the original SW algorithm.

Dynamic Smith-Waterman Algorithm: A High-Performance Grid-Enabled Application Integrated with Globus, GridSphere Portal Framework and CoG Workflow for Performing Biological Local Sequence Alignment

Smith-Waterman algorithm is one of the most significantly used algorithm and a well known approach to gain information about unknown genes and proteins for biological research. As execution time and accuracy is of great significance as handling large-scale dataset, a more reliable high-throughput and efficient parallelism can be achieved with the adaptation of grid environment. Adapting SmithWaterman algorithm with the grid environment brings several concerns regarding fault tolerance, variability in resource performance and workload distribution, application availability etc. The work presented here aims at the development of a Dynamic Smith-Waterman algorithm metascheduler that handles all the specifications of job submission on the grid to the end user for local alignment search. Additionally a web based portal using GridSphere portal framework integrated with Globus 4 and Java Commodity Grid Kit is developed that reduces the complexity to the end users in accessing, managing and manipulating the grid resources and applications. The main contribution towards Dynamic Smith-Waterman algorithm is the reduction of the total job execution time up to 52% with accuracy up to 99.99% and better resource utilization by 40%. In addition, this work can be used as a template for the development of similar applications in future. General Terms Grid, Biological local sequence alignment algorithm

Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation

BackgroundThe Smith-Waterman algorithm for local sequence alignment is more sensitive than heuristic methods for database searching, but also more time-consuming. The fastest approach to parallelisation with SIMD technology has previously been described by Farrar in 2007. The aim of this study was to explore whether further speed could be gained by other approaches to parallelisation.ResultsA faster approach and implementation is described and benchmarked. In the new tool SWIPE, residues from sixteen different database sequences are compared in parallel to one query residue. Using a 375 residue query sequence a speed of 106 billion cell updates per second (GCUPS) was achieved on a dual Intel Xeon X5650 six-core processor system, which is over six times more rapid than software based on Farrar's 'striped' approach. SWIPE was about 2.5 times faster when the programs used only a single thread. For shorter queries, the increase in speed was larger. SWIPE was about twice as fast as BLAST when using the BLOSUM50 score matrix, while BLAST was about twice as fast as SWIPE for the BLOSUM62 matrix. The software is designed for 64 bit Linux on processors with SSSE3. Source code is available from http://dna.uio.no/swipe/ under the GNU Affero General Public License.ConclusionsEfficient parallelisation using SIMD on standard hardware makes it possible to run Smith-Waterman database searches more than six times faster than before. The approach described here could significantly widen the potential application of Smith-Waterman searches. Other applications that require optimal local alignment scores could also benefit from improved performance.

A Parallel Pairwise Local Sequence Alignment Algorithm

Researchers are compelled to use heuristic-based pairwise sequence alignment tools instead of Smith-Waterman (SW) algorithm due to space and time constraints, thereby losing significant amount of sensitivity. Parallelization is a possible solution, though, till date, the parallelization is restricted to database searching through database fragmentation. In this paper, the power of a cluster computer is utilized for developing a parallel algorithm, RPAlign, involving, first, the detection of regions that are potentially alignable, followed by their actual alignment. RPAlign is found to reduce the timing requirement by a factor of upto 9 and 99 when used with the basic local alignment search tool (BLAST) and SW, respectively, while keeping the sensitivity similar to the corresponding method. For distantly related sequences, which remain undetected by BLAST, RPAlign with SW can be used. Again, for megabase-scale sequences, when SW becomes computationally intractable, the proposed method can still align them reasonably fast with high sensitivity.

Using the PGAS Programming Paradigm for Biological Sequence Alignment on a Chip Multi-Threading Architecture

The Partitioned Global Address Space (PGAS) programming paradigm offers ease-of-use in expressing parallelism through a global shared address space while emphasizing performance by providing locality awareness through the partitioning of this address space. Therefore, the interest in PGAS programming languages is growing and many new languages have emerged and are becoming ubiquitously available on nearly all modern parallel architectures. Recently, new parallel machines with multiple cores are designed for targeting high performance applications. Most of the efforts have gone into benchmarking but there are a few examples of real high performance applications running on multicore machines. In this paper, we present and evaluate a parallelization technique for implementing a local DNA sequence alignment algorithm using a PGAS based language, UPC (Unified Parallel C) on a chip multithreading architecture, the UltraSPARC T1. Keywords—Partitioned Global Address Space, Unified Parallel C, Multicore machines, Multi-threading Architecture, Sequence alignment.

Incremental window-based protein sequence alignment algorithms

Protein sequence alignment plays a critical role in computational biology as it is an integral part in many analysis tasks designed to solve problems in comparative genomics, structure and function prediction, and homology modeling. We have developed novel sequence alignment algorithms that compute the alignment between a pair of sequences based on short fixed- or variable-length high-scoring subsequences. Our algorithms build the alignments by repeatedly selecting the highest scoring pairs of subsequences and using them to construct small portions of the final alignment. We utilize PSI-BLAST generated sequence profiles and employ a profile-to-profile scoring scheme derived from PICASSO. We evaluated the performance of the computed alignments on two recently published benchmark datasets and compared them against the alignments computed by existing state-of-the-art dynamic programming-based profile-to-profile local and global sequence alignment algorithms. Our results show that the new algorithms achieve alignments that are comparable with or better than those achieved by existing algorithms. Moreover, our results also showed that these algorithms can be used to provide better information as to which of the aligned positions are more reliable--a critical piece of information for comparative modeling applications.

BALSA: Bayesian algorithm for local sequence alignment

The Smith-Waterman algorithm yields a single alignment, which, albeit optimal, can be strongly affected by the choice of the scoring matrix and the gap penalties. Additionally, the scores obtained are dependent upon the lengths of the aligned sequences, requiring a post-analysis conversion. To overcome some of these shortcomings, we developed a Bayesian algorithm for local sequence alignment (BALSA), that takes into account the uncertainty associated with all unknown variables by incorporating in its forward sums a series of scoring matrices, gap parameters and all possible alignments. The algorithm can return both the joint and the marginal optimal alignments, samples of alignments drawn from the posterior distribution and the posterior probabilities of gap penalties and scoring matrices. Furthermore, it automatically adjusts for variations in sequence lengths. BALSA was compared with SSEARCH, to date the best performing dynamic programming algorithm in the detection of structural neighbors. Using the SCOP databases PDB40D-B and PDB90D-B, BALSA detected 19.8 and 41.3% of remote homologs whereas SSEARCH detected 18.4 and 38% at an error rate of 1% errors per query over the databases, respectively.

Tsukuba BB: a branch and bound algorithm for local multiple alignment of DNA and protein sequences.

In this paper we present a branch and bound algorithm for local gapless multiple sequence alignment (motif alignment) and its implementation. The algorithm uses both score-based bounding and a novel bounding technique based on the "consistency" of the alignment. A sequence order independent search tree is used in conjunction with a technique for avoiding redundant calculations inherent in the structure of the tree. This is the first program to exploit the fact that the motif alignment problem is easier for short motifs. Indeed, for a short fixed motif width, the running time of the algorithm is asymptotically linear in the size of the input. We tested the performance of the program on a dataset of 300 E. coli promoter sequences and a dataset of 85 lipocalin protein sequences. For a motif width of 4, the optimal alignment of the entire set of sequences can be found. For the more natural motif width of 6, the program can align 21 sequences of length 100, more than twice the number of sequences which can be aligned by the best previous exact algorithm. The algorithm can relax the constraint of requiring each sequence to be aligned, and align 105 of the 300 promoter sequences with a motif width of 6. For the lipocalin dataset, we introduce a technique for reducing the effective alphabet size with a minimal loss of useful information. With this technique, we show that the program can find meaningful motifs in a reasonable amount of time by optimizing the score over three motif positions.

A new approach to sequence comparison: normalized sequence alignment.

The Smith-Waterman algorithm for local sequence alignment is one of the most important techniques in computational molecular biology. This ingenious dynamic programming approach was designed to reveal the highly conserved fragments by discarding poorly conserved initial and terminal segments. However, the existing notion of local similarity has a serious flaw: it does not discard poorly conserved intermediate segments. The Smith-Waterman algorithm finds the local alignment with maximal score but it is unable to find local alignment with maximum degree of similarity (e.g. maximal percent of matches). Moreover, there is still no efficient algorithm that answers the following natural question: do two sequences share a (sufficiently long) fragment with more than 70% of similarity? As a result, the local alignment sometimes produces a mosaic of well-conserved fragments artificially connected by poorly-conserved or even unrelated fragments. This may lead to problems in comparison of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al. (Bioinformatics, 15, 1012-1019, 1999). In this paper we propose a new sequence comparison algorithm (normalized local alignment ) that reports the regions with maximum degree of similarity. The algorithm is based on fractional programming and its running time is O(n2log n). In practice, normalized local alignment is only 3-5 times slower than the standard Smith-Waterman algorithm.