Biological Sequence Alignment Research Articles

Multicore SIMD ASIP for Next-Generation Sequencing and Alignment Biochip Platforms

Targeting the development of new biochip platforms capable of autonomously sequencing and aligning biological sequences, a new multicore processing structure is proposed in this manuscript. This multicore structure makes use of a shared memory model and multiple instantiations of a novel application-specific instruction-set processor (ASIP) to simultaneously exploit both fine and coarse-grained parallelism and to achieve high performance levels at low-power consumption. The proposed ASIP is built by extending the instruction set architecture of a synthesizable processor, including both general and special-purpose single-instruction multiple-data instructions. This allows an efficient exploitation of fine-grained parallelism on the alignment of biological sequences, achieving over $30\times $ speedup when compared with sequential algorithmic implementations. The complete system was prototyped on different field-programmable gate array platforms and synthesized with a 90-nm CMOS process technology. Experimental results demonstrate that the multicore structure scales almost linearly with the number of instantiated cores, achieving performances similar to a quad-core Intel Core i7 3820 processor, while using $25\times $ less energy.

An Efficient Scheduling Technique for Biological Sequence Alignment

Computing alignment matrix score to search for regions of homology between biological sequences is time consuming task. This is due to the recursive nature of the dynamic programming-based algorithms such as the Smith-Waterman and the Needleman-Wunsch algorithmns. Typical FPGA-based protein sequencer comprises of two main logic blocks. One for computing alignment scores i.e. the processing element (PE), while another logic block for configuring the PE with coefficients. During alignment matrix computation, the logic block for configuring the PE are left unused until the time consuming alignment matrix computation finished. Therefore, a new technique, known as overlap computation and configuration (OCC) is proposed to minimize the time overhead for performing biological sequence alignment. The OCC technique simultaneously updating substitution matrix in a processing element (PE) systolic array, while computing alignment matrix scores. Results showed that, the sequencer achieves more than two order of magnitude speed-up higher compared to the state of the art, at negligible area overhead, if any.

A cascaded pairwise biomolecular sequence alignment technique using evolutionary algorithm

In computational biology, biological sequence alignment is an important and challenging task for sequence analysis. In this paper, we propose a new sequence alignment technique based on a genetic algorithm (GA) for determining the optimal alignment score for a pair of sequences that could be either DNA or protein sequences. The search space requirement of the proposed genetic-based method, named Cascaded Pairwise Alignment with Genetic Algorithm (CPAGA), is reduced by breaking a large space into smaller subspaces. This is performed by decomposing the sequence pair into multiple segments before starting the alignment procedure. Such decomposition enhances the ability of the search process to reach the global or a near-global optimal solution even for the longer sequences. The method was tested using several DNA/protein sequence pairs. We also compared the alignment score of the CPAGA with that of some well-known and relevant alignment techniques. The performance of the CPAGA method and other relevant techniques was assessed by a set of non-parametric statistical approaches, which suggest a superior performance of CPAGA over the other alignment procedures.

Source Code Plagiarism Detection Using Biological String Similarity Algorithms

Source code plagiarism is easy to commit but difficult to catch. Many approaches have been proposed in the literature to automate its detection; however there is little consensus on what works best. In this paper, we propose two new measures for determining the accuracy of a given technique and describe an approach to convert code files into strings which can then be compared for similarity in order to detect plagiarism. We then compare several string comparison techniques, heavily utilised in the area of biological sequence alignment, and compare their performance on a large collection of student source code containing various types of plagiarism. Experimental results show that the compared techniques succeed in matching a plagiarised file to its original files upwards of 90% of the time. Finally, we propose a modification for these algorithms that drastically improves their runtimes with little or no effect on accuracy. Even though the ideas presented herein are applicable to most programming languages, we focus on a case study pertaining to an introductory-level Visual Basic programming course offered at our institution.

Population Genetic Structure of red mullet (Mullus barbatus L.) in Turkish Sea Based on Mitochondrial DNA

Aim: Mullus barbatus (red mullet) is a commercial fish species naturally distributed from Eastern Atlantic: British Isles to Dakar, Senegal, Canary Islands, Mediterranean and Black Sea. There is no study in our knowledge aimed to determine population genetic structuring and genetic stocks of M. barbatus species in territorial waters of Turkey. Only a few studies have been carried out on their genetics in Turkey which are limited to determination of phylogenetic relationships between species in familia of Mullidae. In this study population genetic structure and genetic diversity of red mullet (Mullus barbatus L.) in Turkish Seas was determined using sequence data of mitochondrial DNA control region.

Evaluating the specificity of primers employed for PCR-based diagnostics of Leishmaniases using multiple alignment analysis

The purpose of this study was to assess the specificity of distinct primers used in the molecular diagnosis for Leishmania detection by using biological sequence search and alignment tools. Four primer pairs routinely employed in the PCR-based diagnostics for Leishmaniasis detection were evaluated through the software Primer-BLAST, which compares nucleotide sequences against user-selected database to avoid primer pairs cause non-specific amplifications. The LU5A-LB3C primer pair showed good specificity among all primers analyzed, generating alignments exclusively against distinct sequences of Leishmania species and no matches were found with other parasite species. Whereas the primer pairs designated MP3H - MP1L, B1-B2 and 13A-13B demonstrated matches against distinct sequences of Leishmania strains and other species, such as Rhesus monkey (Macaca mulatta) and starlet sea anemone (Nematostella vectensis). These findings emphasize the importance of selecting suitable primers for diagnosis of molecular diseases by conducting previous screenings in order to infer their specificity and identity against target templates within biological sequence annotation database.

Structural and Functional Evolution of Positively Selected Sites in Pine Glutathione S-Transferase Enzyme Family*

Phylogenetic analyses have identified positive selection as an important driver of protein evolution, both structural and functional. However, the lack of appropriate combined functional and structural assays has generally hindered attempts to elucidate patterns of positively selected sites and their effects on enzyme activity and substrate specificity. In this study we investigated the evolutionary divergence of the glutathione S-transferase (GST) family in Pinus tabuliformis, a pine that is widely distributed from northern to central China, including cold temperate and drought-stressed regions. GSTs play important roles in plant stress tolerance and detoxification. We cloned 44 GST genes from P. tabuliformis and found that 26 of the 44 belong to the largest (Tau) class of GSTs and are differentially expressed across tissues and developmental stages. Substitution models identified five positively selected sites in the Tau GSTs. To examine the functional significance of these positively selected sites, we applied protein structural modeling and site-directed mutagenesis. We found that four of the five positively selected sites significantly affect the enzyme activity and specificity; thus their variation broadens the GST family substrate spectrum. In addition, positive selection has mainly acted on secondary substrate binding sites or sites close to (but not directly at) the primary substrate binding site; thus their variation enables the acquisition of new catalytic functions without compromising the protein primary biochemical properties. Our study sheds light on selective aspects of the functional and structural divergence of the GST family in pine and other organisms.

An Extra Peptide within the Catalytic Module of a β-Agarase Affects the Agarose Degradation Pattern

Agarase hydrolyzes agarose into a series of oligosaccharides with repeating disaccharide units. The glycoside hydrolase (GH) module of agarase is known to be responsible for its catalytic activity. However, variations in the composition of the GH module and its effects on enzymatic functions have been minimally elucidated. The agaG4 gene, cloned from the genome of the agarolytic Flammeovirga strain MY04, encodes a 503-amino acid protein, AgaG4. Compared with elucidated agarases, AgaG4 contains an extra peptide (Asn(246)-Gly(302)) within its GH module. Heterologously expressed AgaG4 (recombinant AgaG4; rAgaG4) was determined to be an endo-type β-agarase. The protein degraded agarose into neoagarotetraose and neoagarohexaose at a final molar ratio of 1.5:1. Neoagarooctaose was the smallest substrate for rAgaG4, whereas neoagarotetraose was the minimal degradation product. Removing the extra fragment from the GH module led to the inability of the mutant (rAgaG4-T57) to degrade neoagarooctaose, and the final degradation products of agarose by the truncated protein were neoagarotetraose, neoagarohexaose, and neoagarooctaose at a final molar ratio of 2.7:2.8:1. The optimal temperature for agarose degradation also decreased to 40 °C for this mutant. Bioinformatic analysis suggested that tyrosine 276 within the extra fragment was a candidate active site residue for the enzymatic activity. Site-swapping experiments of Tyr(276) to 19 various other amino acids demonstrated that the characteristics of this residue were crucial for the AgaG4 degradation of agarose and the cleavage pattern of substrate.

A data parallel strategy for aligning multiple biological sequences on multi-core computers

In this paper, we address the large-scale biological sequence alignment problem, which has an increasing demand in computational biology. We employ data parallelism paradigm that is suitable for handling large-scale processing on multi-core computers to achieve a high degree of parallelism. Using the data parallelism paradigm, we propose a general strategy which can be used to speed up any multiple sequence alignment method. We applied five different clustering algorithms in our strategy and implemented rigorous tests on an 8-core computer using four traditional benchmarks and artificially generated sequences. The results show that our multi-core-based implementations can achieve up to 151-fold improvements in execution time while losing 2.19% accuracy on average. The source code of the proposed strategy, together with the test sets used in our analysis, is available on request.

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

Editorial: special issue on parallel nature-inspired optimization

Nature-inspired optimization techniques have proven to efficiently solve a wide range of problems related to parallel computing and more generally to computer science. In addition, the intrinsic parallelization and distribution capabilities of nature-inspired techniques can been exploited in order to provide powerful optimization solutions. However many research challenges remain to be addressed, such as the design and implementation of efficient nature-inspired optimization algorithms for massively parallel and distributed architectures and their application to real-world problems. This special issue is composed of extended versions of selected best papers presented at the 14th International Workshop on Nature Inspired Distributed Computing (NIDISC 2011). These articles provide a good theoretical and practical overview of nature-inspired optimization techniques and their application to metaheuristics and parallel/distributed computing. In the following, we propose a brief overview of the different contributions included in this special issue. In the first paper by E.A. Macedo et al., “Multiple Biological Sequence Alignment in Heterogeneous Multicore Clusters with User-Selectable Task Allocation Policies” a parallel version of a heuristic iterative algorithm, DIALIGN-TX, is proposed to tackle the Multiple Sequence Alignment (MSA) problem, a well-known NP-Hard bioinformatics problem. The authors empirically demonstrate the execution time reduction obtained with the proposed parallel implementation as well as the impact of the choice of the allocation policy. The second paper by P. Korosec et al., “Multi-core Implementation of the Differential Ant-Stigmergy Algorithm,” also proposes to parallelize a state-of-the-art optimization algorithm, the Differential Ant-Stigmergy algorithm (DASA) which is a

A Systolic Array-Based FPGA Parallel Architecture for the BLAST Algorithm.

A design of systolic array-based Field Programmable Gate Array (FPGA) parallel architecture for Basic Local Alignment Search Tool (BLAST) Algorithm is proposed. BLAST is a heuristic biological sequence alignment algorithm which has been used by bioinformatics experts. In contrast to other designs that detect at most one hit in one-clock-cycle, our design applies a Multiple Hits Detection Module which is a pipelining systolic array to search multiple hits in a single-clock-cycle. Further, we designed a Hits Combination Block which combines overlapping hits from systolic array into one hit. These implementations completed the first and second step of BLAST architecture and achieved significant speedup comparing with previously published architectures.

Multiple biological sequence alignment in heterogeneous multicore clusters with user-selectable task allocation policies

Multiple Sequence Alignment (MSA) is an important problem in Bioinformatics that aims to align more than two sequences in order to emphasize similarity regions. This problem is known to be NP-Hard, so heuristic methods are used to solve it. DIALIGN-TX is an iterative heuristic method for MSA that generates alignments by concatenating ungapped regions with high similarity. Usually, the first phase of MSA algorithms is parallelized by distributing several independent tasks among the nodes. Even though heterogeneous multicore clusters are becoming very common nowadays, very few task allocation policies were proposed for this type of architecture. This paper proposes an MPI/OpenMP master/slave parallel strategy to run DIALIGN-TX in heterogeneous multicore clusters, with several allocation policies. We show that an appropriate choice of the master node has great impact on the overall system performance. Also, the results obtained in a heterogeneous multicore cluster composed of 4 nodes (30 cores), with real sequence sets show that the execution time can be drastically reduced when the appropriate allocation policy is used.

Performance comparison of GPU programming frameworks with the striped Smith-Waterman algorithm

This paper evaluates and discusses how different GPU programming frameworks affect the performance obtained from GPU acceleration of the striped smith-waterman algorithm used for biological sequence alignment. A total of 6 GPU implementations of the algorithm on NVIDIA GT200b and AMD RV870 using the CUDA and the OpenCL frameworks are compared to analyze cons and pros of explicit descriptions for architecture specific hardware mechanisms in the code. The evaluation results show that the primitive descriptions with the CUDA are still efficient especially for small size data, while better instruction scheduling and optimizations are carried out by the OpenCL compiler. On the other hand, the combination of OpenCL and RV870 which provides a relatively simple view of the architecture is efficient for the large data size.

Comprehensive Study on Computational Methods for K-Shortest Paths Problem

application domains like network connection routing, highway and power line engineering, robot motion planning and other optimization problems require the computation of shortest path. Computations of K-shortest paths provide more (K-1) numbers of backup shortest paths for consideration, which enable the applicability of additional constraints on the particular domains. For instance, a biologist can determine the best of an alignment from the available instances of biological sequence alignments generated from more than one shortest paths computation. The purpose of this paper is to provide a comprehensive review of existing algorithms available for K- shortest paths computation. It will be useful for researcher to implement the effective K-shortest paths computation based on their matching computational requirements over the domain of their interest.

A novel genetic approach for optimized biological sequence alignment

Biological sequence alignment is one of the most important problems in computational biology. The objective of the alignment process is to maximize the alignment score between two given sequences of varying or equal length. The alignment score of two sequences is calculated based on matches, mismatches and gaps in the alignment. We have proposed a new genetic approach for finding optimized match between two DNA or protein sequences. The process is compared with two well known relevant sequence alignment techniques.

Objective method for estimating asymptotic parameters, with an application to sequence alignment.

Sequence alignment is an indispensable computational tool in modern molecular biology. The model underlying biological sequence alignment is of interest to physicists because it approximates the statistical mechanics of DNA and protein annealing, while bearing an intimate relationship to models of directed polymers in random media. Recent methods for determining the statistics of random sequence alignments have reduced the computation time to less than 1 s, opening up some interesting possibilities for online computation with biological search engines. Before implementation, however, the methods required an objective technique for computing regression coefficients pertinent to an asymptotic regime. Typically, physicists estimate parameters pertinent to an asymptotic regime subjectively: They eyeball their data; estimate the asymptotic regime where the regression model holds with reasonable accuracy; and then regress data only within the estimated asymptotic regime. Our publicly available computer program ARRP replaces the subjective assessment of the asymptotic regime with an objective change-point detection method, increasing confidence in the scientific objectivity of the parameter estimates. Asymptotic regression has potential applications across most of physics.

Waveform Mapping and Time-Frequency Processing of DNA and Protein Sequences

Current state-of-the-art approaches for biological sequence querying and alignment require preprocessing and lack robustness to repetitions in the sequence. In addition, these approaches do not provide much support for efficiently querying subsequences, a process that is essential for tracking localized database matches. We propose a query-based alignment method for biological sequences that first maps sequences to time-domain waveforms before processing the waveforms for alignment in the time-frequency plane. The mapping uses waveforms, such as Gaussian functions, with unique sequence representations in the time-frequency plane. The proposed alignment method employs a robust querying algorithm that utilizes a time-frequency signal expansion whose basis function is matched to the basic waveform in the mapped sequences. The resulting WAVEQuery approach was demonstrated for both deoxyribonucleic acid (DNA) and protein sequences using the matching pursuit decomposition as the signal basis expansion. We specifically evaluated the alignment localization of WAVEQuery over repetitive database segments, and we demonstrated its operation in real-time without preprocessing. We also demonstrated that WAVEQuery significantly outperformed the biological sequence alignment method BLAST for queries with repetitive segments for DNA sequences. A generalized version of the WAVEQuery approach with the metaplectic transform is also described for protein sequence structure prediction.

Efficient Parallel Design for Edit distance algorithm in DNA Sequence Alignment

The focus of Bioinformatics research is usually on two aspects—genomics and proteomics, specifically, it’s starting from nucleic acid and protein sequences, analyzing the structural and functional biological information expressed in the sequences. Biological sequence alignment is one of the common problems, the NeedlemanWunsch algorithm based on dynamic programming is the most basic algorithm, and Edit Distance(Levenshtein Distance) algorithm is also widely used in DNA sequence alignment. Nowadays, there are large amount of improvements on the Needleman-Wunsch algorithm, while few on Edit Distance algorithm, so this paper focuses on revealing the effects of parallel design on optimizing the Edit Distance algorithm, and it also compares the two algorithms’ different significances in DNA sequence alignment objectively.

Biological Sequence Matching using Boolean algebra vs. Fuzzy Logic

Biological sequence alignment is one of the crucial tasks of computational bioinformatics, and provides base for other tasks of bioinformatics. In this paper, we discuss two different approaches to sequence matching – Boolean algebra and fuzzy logic. First method is a two-valued logic whereas the second is a multi-valued logic. Both the methods perform sequence matching by direct comparison method using the operations of Boolean algebra and fuzzy logic respectively. To ensure the optimal alignment, dynamic programming is employed to align the sequences progressively. Both the methods are implemented and then tested on few sets of real biological sequences taken from NCBI bank and their performances are compared with the CLUSTALW algorithm.