Biological Sequence Alignment Research Articles

Spatial Memory Bias in Children Tourists

ABSTRACTWhat do children absorb from tourism activities? Memory can provide answers to this question – but children’s memories do not necessarily tell the truth. Their recollections could be blurred by time, interference, or imagination. Taking Ocean Park Hong Kong as a case study, this research was carried out to explore children tourists’ spatial memory bias by matching activity paths based on GPS recordings and recall diaries via biological sequence alignment. False memories and forgetting are the two types of memory bias of interest in this paper. Findings show that false memories, compared to forgetting, are often generated from later parts of the tourism experience. The spatial distribution of tourism memory bias is influenced by the entrance effect, aggregation effect, and competition effect. These results provide new insight into research on children education and tourist memory.

MPSAGA: a matrix-based pair-wise sequence alignment algorithm for global alignment with position based sequence representation

The proposed algorithm is a novel matrix-based global pair-wise sequence alignment with a de novo sequence representation. Needleman–Wunsch, noblest, Emboss-Needle, ALIGN, LALIGN, FOGSAA, DIALIGN, ACANA, MUMmer, etc. are few other algorithms that are most commonly used for global pair-wise sequence alignment. Needleman–Wunsch algorithm is one of the most popular algorithms that provides the best possible pair-wise sequence alignment, but the algorithm output is associated with high time and space complexities. To resolve these complex issues, researchers have proposed several algorithms to reduce time and space complexities in the pair-wise sequence alignment. Most of these algorithms provide solutions, but compromise the optimal result in favor of plummeting time and space complexities. An attempt has been made in the present research to develop MPSAGA and a completely unique positional matrix (PM) based sequence representation to deal with the time and space complexities without compromising sequence alignment results (MPSAGA is in public domain available at https://github.com/JyotiLakhani1/MPSAGA ). A benchmarking of the proposed algorithm has also been performed with other popular pair-wise sequence alignment algorithms with and without positional matrix-based sequence representation. The use of an integer instead of string data type and exclusive clustering method in MPSAGA with positional matrix based sequence representation resulted in a noteworthy reduction in the memory usage (space) and execution time in the pair-wise alignment of biological sequences.

A WORK DISTRIBUTION STRATEGY FOR GLOBAL SEQUENCE ALIGNMENT

The sequence alignment comprises to identify similarities and dissimilarities between two given sequences. In this paper, we propose a work distribution strategy for the implementation of DNA global sequence alignment. The main objective of this work is to minimize the execution time required for DNA global alignment of large biological sequences. The proposed approach dealt the issues with the memory optimizations and minimization of execution time. We considered the biological sequences of different size to fit into the global memory of the system. The proposed strategy is implemented in shared memory architecture using OpenMP programming for large biological sequences. Parallelization using OpenMP directive is relatively easy and execute the code fast. We experimented on the Dell Precision Tower 7910 with Intel Xeon processor with 32GB RAM and 28 CPU cores. The efficient use of global memory and cache memory optimization dominate the results of execution time. The results demonstrate the significantly high speed up using OpenMP as compared with other implementations.

Current Trends and Ongoing Progress in the Computational Alignment of Biological Sequences

The computational techniques for nucleic acid and protein sequence comparison reduce the extensive burden of molecular biologists. The sequence alignment is one of the main research areas in bioinformatics, and comparative genomics and proteomics lead us to important discoveries in various fields of bioinformatics. Researchers develop and use different heuristics and evolutionary algorithms for optimal DNA and protein sequence alignment. There are different categories of improved computational sequence matching methods. In this paper, the goal is to cover almost all computational approaches toward sequence alignment. Different aspects and issues related to the optimal alignment of biological sequences will be analyzed. The sequence comparisons through mathematical and computational techniques have manifold benefits and importance in bioinformatics. Researchers recently explore proposing novel computational techniques for simultaneous matching of multiple sequences or multiple sequence alignment (MSA). Pairwise alignment, or the alignment of two sequences, is the basic building block of all alignment methods. The goal is to design optimal and relevant algorithms with less computational complexity and more efficiency.

A Large-Gap Clone Detection Approach Using Sequence Alignment via Dynamic Parameter Optimization

Large-gap clones, a kind of clones that reuses code with many edits, are very common in software development practice and widespread in software systems. The detection of such clones is very crucial. However, due to a large number of edits, most of the current work fails to detect such clones effectively. This paper aims to find an effective approach for accurate detection of large-gap clones. We transform the code clone detection problem into a biological sequence alignment question and propose a novel approach that combines code fingerprint with sequence alignment. The sequence alignment is Smith-Waterman algorithm based, but shows significant improvements using dynamic parameter acquisition strategy. Furthermore, we design new rational criteria for clone identification. The proposed approach is automatically evaluated extensively by more than 10 million lines of code for general clones detection. We further conduct an empirical study on five large-scale Java projects to manually measure the approach for large-gap clones detection. The experimental results show that the proposed approach can effectively detect large-gap clones and exhibit good performance, and at the same time remains the competitiveness with existing advanced detection tools in detecting general clone detection.

ASW: Accelerating Smith–Waterman Algorithm on Coupled CPU–GPU Architecture

Smith–Waterman algorithm (SW) is a popular dynamic programming algorithm widely used in bioinformatics for local biological sequence alignment. Due to the $$O(n^2)$$ high time and space complexity of SW and growing size of biological data, it is crucial to accelerate SW for high performance. In view of the GPU high efficiency in science computation, many existing studies (e.g., CUDAlign, CUDASW++) speedup SW with GPU. However, the strong data dependency makes SW communication intensive, and the previous works fail to fully leverage the heterogeneous capabilities of the GPU machine for either the neglect of the CPU ability or the low bandwidth of PCI-e. In this paper, we propose ASW, which aims at accelerating SW algorithm with accelerated processing unit (APU), a heterogeneous processor integrates CPU and GPU in a single die and share the same memory. This coupled CPU–GPU architecture is more suitable for frequent data exchanging due to the elimination of PCI-e bus. For the full utilization of both CPU and GPU in APU system, ASW partitions the whole SW matrix into blocks and dynamically dispatches each block to CPU and GPU for the concurrent execution. A DAG-based dynamic scheduling method is presented to dispatch the workload automatically. Moreover, we also design a time cost model to determine the partition granularity in the matrix division phase. We have evaluated ASW on AMD A12 platform and our results show that ASW achieves a good performance of 7.2 GCUPS (gigacells update per second).

Acceleration of Biological Sequence Alignment Using Residue Number System


 
 
 
 Smith-Waterman Algorithms (SWA) is becoming popular among researchers especially in the field of bioinformatics. The algorithm performance is better among other known alignment algorithms because of the high level of accuracy it exhibits. However, the algorithm performance is at low speed due to its computational complexity. Researchers are concerned with this problem and are looking for various ways to address the issue. Different approaches are adopted to improve the speed, such as the use of a systolic array to accelerate the algorithm, use of recursive variable expansion (RVE) method approach; some implemented the algorithm on software and hardware, etc. This paper used Residue Number System (RNS) approach to the algorithm of Smith-Waterman and carried out hardware implementation on Quartus II, 64-Bit version 12.1 (Cyclone II family) VHDL application software.
 
 
 
 

Process-oriented Iterative Multiple Alignment for Medical Process Mining.

Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.

HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing

BackgroundMultiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types.MethodsDistributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction.ResultsThe experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource.ConclusionsTHAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.

Comparison of Different Sequence Alignment Methods- A Survey

Bioinformatics is a promising and inventive research field. Biological Sequence alignment is the inborn part of bioinformatics, which helps to find similarity between biological sequences i.e. DNA and protein. Alignment of biological sequences helps to discover functional and structural similarity of sequences. The biological sequence database has been expanding rapidly due to new sequences being found, which has raised the demand to employ more efficient and fast algorithm. There has been an eruption algorithm in the past few decades to find optimal or nearly-optimal alignments. This paper is focused on the popular sequence alignment algorithms. Different types of alignment method have been discussed on the basis of their optimality and approximate solutions. It has been studied that optimal algorithms, which are based on dynamic programming are giving exact solutions. But these are highly computationally complexed. The stochastic optimization methods has been chosen from literature as the potential candidates for the solution of complex multiple sequence alignments with better speed and care.

Accelerating Viterbi algorithm on graphics processing units

Viterbi algorithm is used in different scientific applications including biological sequence alignment, speech recognition, and probabilistic inference. However, high computational complexity of the Viterbi algorithm is a major concern. Accelerating the Viterbi algorithm is important, especially when the number of states or the length of the sequences increase significantly. In this paper, a parallel solution to improve the performance of Viterbi algorithm is presented. This is achieved by formulating a matrix product based algorithm. This algorithm has been mapped to a NVIDIA graphics processing unit. The performance for different parameters and realizations are compared. The results depicts matrix product is not a viable option for small number of states. However, matrix product solution using shared memory for large number of states gains good performance when compared with the serial version.

FPGASW: Accelerating Large-Scale Smith-Waterman Sequence Alignment Application with Backtracking on FPGA Linear Systolic Array.

The Smith-Waterman (SW) algorithm based on dynamic programming is a well-known classical method for high precision sequence matching and has become the gold standard to evaluate sequence alignment software. In this paper, we propose fine-grained parallelized SW algorithms using affine gap penalty and implement a parallel computing structures to accelerating the SW with backtracking on FPGA platform. We analysis the dynamic parallel computing features of anti-diagonal elements and storage expansion problem resulting from backtracking stage, and propose a series of optimization strategies to eliminate data dependency, reduce storage requirements, and overlap memory access latency. Our implementation is capable of supporting multi-type, large-scale biological sequence alignment applications. We obtain a speedup between 3.6 and 25.2 over the typical SW algorithm running on a general-purpose computer configured with an Intel Core i5 3.2 GHz CPU. Moreover, our work is superior to other FPGA implementations in both array size and clock frequency, and the experiment results show that it can get a performance closed to that of the latest GPU implementation, but the power consumption is only about 26% of that of the GPU platforms.

The implementation of bit-parallelism for DNA sequence alignment

Dynamic Programming (DP) remain the central algorithm of biological sequence alignment. Matching score computation is the most time-consuming process. Bit-parallelism is one of approximate string matching techniques that transform DP matrix cell unit processing into word unit (groups of cell). Bit-parallelism computate the scores column-wise. Adopting from word processing in computer system work, this technique promise reducing time in score computing process in DP matrix. In this paper, we implement bit-parallelism technique for DNA sequence alignment. Our bit-parallelism implementation have less time for score computational process but still need improvement for there construction process.

Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics.

A Characteristic-Based Framework for Multiple Sequence Aligners.

The multiple sequence alignment is a well-known bioinformatics problem that consists in the alignment of three or more biological sequences (protein or nucleic acid). In the literature, a number of tools have been proposed for dealing with this biological sequence alignment problem, such as progressive methods, consistency-based methods, or iterative methods; among others. These aligners often use a default parameter configuration for all the input sequences to align. However, the default configuration is not always the best choice, the alignment accuracy of the tool may be highly boosted if specific parameter configurations are used, depending on the biological characteristics of the input sequences. In this paper, we propose a characteristic-based framework for multiple sequence aligners. The idea of the framework is, given an input set of unaligned sequences, extract its characteristics and run the aligner with the best parameter configuration found for another set of unaligned sequences with similar characteristics. In order to test the framework, we have used the well-known multiple sequence comparison by log-expectation (MUSCLE) v3.8 aligner with different benchmarks, such as benchmark alignments database v3.0, protein reference alignment benchmark v4.0, and sequence alignment benchmark v1.65. The results shown that the alignment accuracy and conservation of MUSCLE might be greatly improved with the proposed framework, specially in those scenarios with a low percentage of identity. The characteristic-based framework for multiple sequence aligners is freely available for downloading at http://arco.unex.es/arl/fwk-msa/cbf-msa.zip.

A Hybrid Multiobjective Memetic Metaheuristic for Multiple Sequence Alignment

Over the last 25 years, the multiple sequence alignment (MSA) problem has attracted the attention of biologists because it is one of the major techniques used in several areas of computational biology, such as homology searches, genomic annotation, protein structure prediction, gene regulation networks, or functional genomics. This problem implicates the alignment of more than two biological sequences, and is considered as a nondeterministic polynomial time optimization problem. In this paper, we find a number of different approaches for dealing with this biological sequence alignment problem. Basically, we distinguish six main groups: 1) exact methods; 2) progressive methods; 3) consistency-based methods; 4) iterative methods; 5) evolutionary algorithms; and 6) structure-based methods. In this paper, we propose the use of evolutionary computation and multiobjective optimization for solving this bioinformatics problem. A multiobjective version of a memetic metaheuristic is presented: hybrid multiobjective metaheuristics for MSA. In order to prove the effectiveness of the new proposal, we use three structure-based benchmarks created by using empirical data as input. The results obtained by our method are compared with well-known methods published in this paper, concluding that the new approach presents remarkable accuracy when dealing with sets of sequences with a low sequence similarity, the most frequent ones in real world.

Parallel algorithms for large-scale biological sequence alignment on Xeon-Phi based clusters.

BackgroundComputing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators.ResultsThis paper presents new approaches to high performance biological sequence database scanning with the Smith-Waterman algorithm and the first stage of progressive multiple sequence alignment based on the ClustalW heuristic on a Xeon Phi-based compute cluster. Our approach uses a three-level parallelization scheme to take full advantage of the compute power available on this type of architecture; i.e. cluster-level data parallelism, thread-level coarse-grained parallelism, and vector-level fine-grained parallelism. Furthermore, we re-organize the sequence datasets and use Xeon Phi shuffle operations to improve I/O efficiency.ConclusionsEvaluations show that our method achieves a peak overall performance up to 220 GCUPS for scanning real protein sequence databanks on a single node consisting of two Intel E5-2620 CPUs and two Intel Xeon Phi 7110P cards. It also exhibits good scalability in terms of sequence length and size, and number of compute nodes for both database scanning and multiple sequence alignment. Furthermore, the achieved performance is highly competitive in comparison to optimized Xeon Phi and GPU implementations. Our implementation is available at https://github.com/turbo0628/LSDBS-mpi.

MASA

Biological sequence alignment is a very popular application in Bioinformatics, used routinely worldwide. Many implementations of biological sequence alignment algorithms have been proposed for multicores, GPUs, FPGAs and CellBEs. These implementations are platform-specific; porting them to other systems requires considerable programming effort. This article proposes and evaluates MASA, a flexible and customizable software architecture that enables the execution of biological sequence alignment applications with three variants (local, global, and semiglobal) in multiple hardware/software platforms with block pruning, which is able to reduce significantly the amount of data processed. To attain our flexibility goals, we also propose a generic version of block pruning and developed multiple parallelization strategies as building blocks, including a new asynchronous dataflow-based parallelization, which may be combined to implement efficient aligners in different platforms. We provide four MASA aligner implementations for multicores (OmpSs and OpenMP), GPU (CUDA), and Intel Phi (OpenMP), showing that MASA is very flexible. The evaluation of our generic block pruning strategy shows that it significantly outperforms the previously proposed block pruning, being able to prune up to 66.5% of the cells when using the new dataflow-based parallelization strategy.

The Ensembl gene annotation system.

The Ensembl gene annotation system has been used to annotate over 70 different vertebrate species across a wide range of genome projects. Furthermore, it generates the automatic alignment-based annotation for the human and mouse GENCODE gene sets. The system is based on the alignment of biological sequences, including cDNAs, proteins and RNA-seq reads, to the target genome in order to construct candidate transcript models. Careful assessment and filtering of these candidate transcripts ultimately leads to the final gene set, which is made available on the Ensembl website. Here, we describe the annotation process in detail.Database URL: http://www.ensembl.org/index.html

English

English