Sequence Alignment Algorithm Research Articles

A New Numerical Method for DNA Sequence Analysis Based on 8-Dimensional Vector Representation

Background: The multiple sequence alignment (MSA) algorithms are the traditional ways to compare and analyze DNA sequences. However, for large DNA sequences, these algorithms require a long time computationally. Objective: Here we will propose a new numerical method to characterize and compare DNA sequences quickly. Method: Based on a new 2-dimensional (2D) graphical representation of DNA sequences, we can obtain an 8-dimensional vector using two basic concepts of probability, the mean and the variance. Results: We perform similarity/dissimilarity analyses among two real DNA data sets, the coding sequences of the first exon of beta-globin gene of 11 species and 31 mammalian mitochondrial genomes, respectively. Conclusion: Our results are in agreement with the existing analyses in our literatures. We also compare our approach with other methods and find that ours is more effective.

Implementation of Hybrid Alignment Algorithm for Protein Database Search on the SW26010 Many-Core Processor

In biological research, biology sequence alignment algorithm aims to find similarities between sequences. As the size of biological database increases exponentially, the complexity of sequence alignment process also increases rapidly, which results in a large amount of computational time. The Sunway TaihuLight is the world's first heterogeneous supercomputer with peak performance over 100 PFlops and provides a new hardware platform for database search. In this paper we present an efficient method of protein database search based on Sunway TaihuLight supercomputer. Furthermore, we also optimize protein database search on Sunway TaihuLight to give full play to the performance of the SW26010 processor. In our proposed approach, we design hybrid sequence alignment by combining the Smith-Waterman local alignment algorithm and the Needleman-Wunsch global alignment algorithm. The protein database search is paralleled by message passing interface (MPI) and accelerated thread library (Athread). Experiment results with the Swiss-Prot database show that our implementation can effectively leverage the SW26010 processor's special hardware architecture and achieve a speedup to 15.91 times on a single node. In addition, we expand the scale to 64 nodes to test the scalability of the parallel method on the Sunway TaihuLight system, and the results show that our parallel implementation of protein database search have a good expansibility and reliability.

Opti-SW: An improved gene sequence alignment algorithm

Opti-SW: An improved gene sequence alignment algorithm

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.

A Way to Improve the Key Recovery Accuracy Based on Dynamic Programming

Key-recovery technology is often used by an adversary to attempt to recover the cryptographic key of an encryption scheme. The most obvious key-recovery attack is the exhaustive key-search attack. But modern ciphers often have a key space of size 2128 or greater, making such attacks infeasible with current technology. Cache-based side channel attack is another way to get the cryptographic key of an encryption scheme, but there are random noises in side channel attack. In order to reduce random errors, it is advisable to repeat the key recovery process many times. This paper is focused on the way to improve the key recovery accuracy by dealing with the key sequences obtained from the repeated Cache-based side channel attacks. To get the real key, private key bits from side channel attack are collected firstly. And then the key sequences are aligned using sequence alignment algorithms based on dynamic programming. The provided method of key recovery is universal, which is not limited to any cryptographic algorithm. The experiment shows that the proposed method has a good performance and a high availability when the error rate of the collected key bit is within a reasonable range.

BGSA: a bit-parallel global sequence alignment toolkit for multi-core and many-core architectures

Modern bioinformatics tools for analyzing large-scale NGS datasets often need to include fast implementations of core sequence alignment algorithms in order to achieve reasonable execution times. We address this need by presenting the BGSA toolkit for optimized implementations of popular bit-parallel global pairwise alignment algorithms on modern microprocessors. BGSA outperforms Edlib, SeqAn and BitPAl for pairwise edit distance computations and Parasail, SeqAn and BitPAl when using more general scoring schemes for pairwise alignments of a batch of sequence reads on both standard multi-core CPUs and Xeon Phi many-core CPUs. Furthermore, banded edit distance performance of BGSA on a Xeon Phi-7210 outperforms the highly optimized NVBio implementation on a Titan X GPU for the seed verification stage of a read mapper by a factor of 4.4. BGSA is open-source and available at https://github.com/sdu-hpcl/BGSA. Supplementary data are available at Bioinformatics online.

Identification of Protein Homologs and Domain Boundaries by Iterative Sequence Alignment.

Evolutionary domains are protein regions with observable sequence similarity to other known domains. Here we describe how to use common sequence and profile alignment algorithms (i.e., BLAST, HHsearch) to delineate putative domains in novel protein sequences, given a reference library of protein domains. In this case, we use our database of evolutionary domains (ECOD) as a reference, but other domain sequence libraries could be used (e.g., SCOP, CATH). We describe our domain partition algorithm along with specific notes on how to avoid domain indexing errors when working with multiple data sources and software algorithms with differing outputs.

SOGA: space oriented genetic algorithm for multiple sequence alignment

Multiple sequence alignment is one of the recurrent assignments in Bioinformatics. This method allows organizing a set of molecular sequences in order to expose their similarities and their differences. Although several applicable techniques were observed in this re- search, from traditional method such as dynamic programming to the extent of widely used stochastic optimization method such as Simu- lated Annealing and motif finding for solving this problem, their use is limited by the computing demands which are necessary for ex- ploring such a large and complex search space. This paper presents a new genetic algorithm, namely SOGA (Space Oriented Genetic Algorithm for Multiple Sequence Alignment), which has two new mechanisms: the first generates the population with randomly inserting the space between the selected sequences and the second applying new crossover and mutation operator, within an iterative process, to generate new and better solutions. This method is simple and fast. Its performance will further be tested on standard benchmark databas- es and will be compared with well-known algorithms. However, as its solutions clears that there is scope for further improvement.

A Survey of the State-of-the-Art Parallel Multiple Sequence Alignment Algorithms on Multicore Systems

Evolutionary modeling applications are the best way to provide full information to support in-depth understanding of evaluation of organisms. These applications mainly depend on identifying the evolutionary history of existing organisms and understanding the relations between them, which is possible through the deep analysis of their biological sequences. Multiple Sequence Alignment (MSA) is considered an important tool in such applications, where it gives an accurate representation of the relations between different biological sequences. In literature, many efforts have been put into presenting a new MSA algorithm or even improving existing ones. However, little efforts on optimizing parallel MSA algorithms have been done. Nowadays, large datasets become a reality, and big data become a primary challenge in various fields, which should be also a new milestone for new bioinformatics algorithms. This survey presents four of the state-of-the-art parallel MSA algorithms, TCoffee, MAFFT, MSAProbs, and M2Align. We provide a detailed discussion of each algorithm including its strengths, weaknesses, and implementation details and the effectiveness of its parallel implementation compared to the other algorithms, taking into account the MSA accuracy on two different datasets, BAliBASE and OXBench.

Freiburg RNA tools: a central online resource for RNA-focused research and teaching.

The Freiburg RNA tools webserver is a well established online resource for RNA-focused research. It provides a unified user interface and comprehensive result visualization for efficient command line tools. The webserver includes RNA-RNA interaction prediction (IntaRNA, CopraRNA, metaMIR), sRNA homology search (GLASSgo), sequence-structure alignments (LocARNA, MARNA, CARNA, ExpaRNA), CRISPR repeat classification (CRISPRmap), sequence design (antaRNA, INFO-RNA, SECISDesign), structure aberration evaluation of point mutations (RaSE), and RNA/protein-family models visualization (CMV), and other methods. Open education resources offer interactive visualizations of RNA structure and RNA-RNA interaction prediction as well as basic and advanced sequence alignment algorithms. The services are freely available at http://rna.informatik.uni-freiburg.de.

A novel pattern recognition system for detecting Android malware by analyzing suspicious boot sequences

This paper introduces a malware detection system for smartphones based on studying the dynamic behavior of suspicious applications. The main goal is to prevent the installation of the malicious software on the victim systems. The approach focuses on identifying malware addressed against the Android platform. For that purpose, only the system calls performed during the boot process of the recently installed applications are studied. Thereby the amount of information to be considered is reduced, since only activities related with their initialization are taken into account. The proposal defines a pattern recognition system with three processing layers: monitoring, analysis and decision-making. First, in order to extract the sequences of system calls, the potentially compromised applications are executed on a safe and isolated environment. Then the analysis step generates the metrics required for decision-making. This level combines sequence alignment algorithms with bagging, which allow scoring the similarity between the extracted sequences considering their regions of greatest resemblance. At the decision-making stage, the Wilcoxon signed-rank test is implemented, which determines if the new software is labeled as legitimate or malicious. The proposal has been tested in different experiments that include an in-depth study of a particular use case, and the evaluation of its effectiveness when analyzing samples of well-known public datasets. Promising experimental results have been shown, hence demonstrating that the approach is a good complement to the strategies of the bibliography.

Improving the performance of Smith–Waterman sequence algorithm on GPU using shared memory for biological protein sequences

In Bioinformatics, sequence alignment algorithm aims to find out whether biological sequences (e.g., DNA, RNA, or Protein sequences) are related or not. A variety of algorithms are developed, Smith–Waterman Algorithm (SW) is a well-known local alignment algorithm to find the similarity of two sequences and provides optimal result using dynamic programming. As the size of sequence database is doubling about every 6 months, the computational time also increases. Sequence alignment algorithms performance can have improved by using the parallel computing technology on the GPU. In this paper, we proposed a method to improve the performance of SW algorithm by using GPU’s shared memory instead of global memory. By using shared memory, the data being transferred between the global memory and processing elements is reduced, which in turn improves the performance. The tabulated result showed positive sign of correctness in proposed method and tested using UniProt sequence database.

An accurate algorithm for multiple sequence alignment in MapReduce

An accurate algorithm for multiple sequence alignment in MapReduce

JABAWS 2.2 distributed web services for Bioinformatics: protein disorder, conservation and RNA secondary structure.

SummaryJABAWS 2.2 is a computational framework that simplifies the deployment of web services for Bioinformatics. In addition to the five multiple sequence alignment (MSA) algorithms in JABAWS 1.0, JABAWS 2.2 includes three additional MSA programs (Clustal Omega, MSAprobs, GLprobs), four protein disorder prediction methods (DisEMBL, IUPred, Ronn, GlobPlot), 18 measures of protein conservation as implemented in AACon, and RNA secondary structure prediction by the RNAalifold program. JABAWS 2.2 can be deployed on a variety of in-house or hosted systems. JABAWS 2.2 web services may be accessed from the Jalview multiple sequence analysis workbench (Version 2.8 and later), as well as directly via the JABAWS command line interface (CLI) client. JABAWS 2.2 can be deployed on a local virtual server as a Virtual Appliance (VA) or simply as a Web Application Archive (WAR) for private use. Improvements in JABAWS 2.2 also include simplified installation and a range of utility tools for usage statistics collection, and web services querying and monitoring. The JABAWS CLI client has been updated to support all the new services and allow integration of JABAWS 2.2 services into conventional scripts. A public JABAWS 2 server has been in production since December 2011 and served over 800 000 analyses for users worldwide.Availability and implementationJABAWS 2.2 is made freely available under the Apache 2 license and can be obtained from: http://www.compbio.dundee.ac.uk/jabaws.

Aligning the large-scale ontologies on schema-level for weaving Chinese linked open data

This study purposes a data field and sequence alignment-based ontology mapping architecture, as ontology mapping is an important way to solve the problem of ontology heterogeneous; meanwhile, it also can be considered as a typical scenario on finding the schema-level links in linked open data (LOD). Current researches on LOD is mainly focused on the level of instances so that the task on finding schema-level links between LOD datasets is being ignored. Furthermore, nowadays the knowledge described in Chinese is also an important part of online knowledge base; unfortunately, current Chinese ontology mapping systems have low efficiency and availability, there is still lack of relevant system for large-scale Chinese ontology mapping task in LOD environment. In order to solve this problem, firstly, based on the improved nuclear field-like potential function, we compress the size of unaligned large-scale Chinese ontology. Secondly, we use the sequence alignment algorithm to compute similarity between the concepts. Finally, we compare our system to other typical concept mapping algorithms. Results prove it can reduce the scale of mapping task effectively and also have higher overall performance. This study may have important practical significance for promoting Chinese knowledge sharing, reusing and interoperation.

EGSA: a new enhanced gravitational search algorithm to resolve multiple sequence alignment problem

Multiple sequence alignment is a very important and useful tool for genomic analysis in many tasks in bioinformatics. However, finding an accurate alignment of DNA or protein sequences is very difficult since the computational effort required grows exponentially with the sequences number. In this paper, we propose a new sequence alignment algorithm based on gravitational search algorithm (GSA). The gravitational search algorithm (GSA) is a recent metaheuristic inspired from Newton's laws of universal gravitation and motion. Moreover, to avoid the convergence toward local optima, we enhance GSA behaviour by introducing a new mechanism based on simulated annealing concept. Such concept offers a good balance between the exploration and exploitation in GSA and can lead to good alignment quality for the MSA problem. The accuracy and efficiency of the proposed algorithm are compared with recent and well-known alignment methods using BAliBASE benchmark database. The analysis of the experimental results shows that the algorithm can achieve competitive solutions quality.

Analyzing Glycan-Binding Profiles Using Weighted Multiple Alignment of Trees.

This chapter describes the Multiple Carbohydrate Alignment with Weights (MCAW) tool, which is available as a part of the RINGS (Resource for INformatics of Glycomes at Soka) website. It implements a combination of KCaM (Aoki, Yamaguchi, Ueda, et al., Nucl Acids Res 32:W267-W272, 2004), a pairwise glycan alignment algorithm, and ClustalW (Thompson, Higgins, Gibson, Nucleic Acids Res 22:4673-80, 1994), a weighted multiple protein sequence alignment algorithm. This tool computes the multiple glycan alignment by first computing a guide tree to determine the order by which to progressively add glycans to the multiple alignment. The dynamic programming algorithm results in a glycan profile of the alignment glycans, containing "monosaccharide positions" indicating the ratio of monosaccharides and their glycosidic bonds that are aligned at the corresponding position. This tool has been used to analyze databases of glycan array experimental data, incorporating weights to reflect the biological significance of certain glycans over others. As a result, it has been shown that the alignments obtained are biologically relevant, matching the results as found in the literature.

EGSA: a new enhanced gravitational search algorithm to resolve multiple sequence alignment problem

Multiple sequence alignment is a very important and useful tool for genomic analysis in many tasks in bioinformatics. However, finding an accurate alignment of DNA or protein sequences is very difficult since the computational effort required grows exponentially with the sequences number. In this paper, we propose a new sequence alignment algorithm based on gravitational search algorithm (GSA). The gravitational search algorithm (GSA) is a recent metaheuristic inspired from Newton's laws of universal gravitation and motion. Moreover, to avoid the convergence toward local optima, we enhance GSA behaviour by introducing a new mechanism based on simulated annealing concept. Such concept offers a good balance between the exploration and exploitation in GSA and can lead to good alignment quality for the MSA problem. The accuracy and efficiency of the proposed algorithm are compared with recent and well-known alignment methods using BAliBASE benchmark database. The analysis of the experimental results shows that the algorithm can achieve competitive solutions quality.

Pro-malign: Multiple Sequence Alignment Algorithm using Approached Profile

Pro-malign: Multiple Sequence Alignment Algorithm using Approached Profile

Bi-level error correction for PacBio long reads.

The latest sequencing technologies such as the Pacific Biosciences (PacBio) and Oxford Nanopore machines can generate long reads at the length of thousands of nucleic bases which is much longer than the reads at the length of hundreds generated by Illumina machines. However, these long reads are prone to much higher error rates, for example 15%, making downstream analysis and applications very difficult. Error correction is a process to improve the quality of sequencing data. Hybrid correction strategies have been recently proposed to combine Illumina reads of low error rates to fix sequencing errors in the noisy long reads with good performance. In this paper, we propose a new method named Bicolor, a bi-level framework of hybrid error correction for further improving the quality of PacBio long reads. At the first level, our method uses a de Bruijn graph-based error correction idea to search paths in pairs of solid -mers iteratively with an increasing length of -mer. At the second level, we combine the processed results under different parameters from the first level. In particular, a multiple sequence alignment algorithm is used to align those similar long reads, followed by a voting algorithm which determines the final base at each position of the reads. We compare the superior performance of Bicolor with three state-of-the-art methods on three real data sets. Results demonstrate that Bicolor always achieves the highest identity ratio. Bicolor also achieves a higher alignment ratio () and a higher number of aligned reads than the current methods on two data sets. On the third data set, our method is closely competitive to the current methods in terms of number of aligned reads and genome coverage. The C++ source codes of our algorithm are freely available at https://github.com/yuansliu/Bicolor.