Sequence Alignment Algorithm Research Articles

Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping

Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area.

Long Read Alignment with Parallel MapReduce Cloud Platform.

Genomic sequence alignment is an important technique to decode genome sequences in bioinformatics. Next-Generation Sequencing technologies produce genomic data of longer reads. Cloud platforms are adopted to address the problems arising from storage and analysis of large genomic data. Existing genes sequencing tools for cloud platforms predominantly consider short read gene sequences and adopt the Hadoop MapReduce framework for computation. However, serial execution of map and reduce phases is a problem in such systems. Therefore, in this paper, we introduce Burrows-Wheeler Aligner's Smith-Waterman Alignment on Parallel MapReduce (BWASW-PMR) cloud platform for long sequence alignment. The proposed cloud platform adopts a widely accepted and accurate BWA-SW algorithm for long sequence alignment. A custom MapReduce platform is developed to overcome the drawbacks of the Hadoop framework. A parallel execution strategy of the MapReduce phases and optimization of Smith-Waterman algorithm are considered. Performance evaluation results exhibit an average speed-up of 6.7 considering BWASW-PMR compared with the state-of-the-art Bwasw-Cloud. An average reduction of 30% in the map phase makespan is reported across all experiments comparing BWASW-PMR with Bwasw-Cloud. Optimization of Smith-Waterman results in reducing the execution time by 91.8%. The experimental study proves the efficiency of BWASW-PMR for aligning long genomic sequences on cloud platforms.

Prediction of antimicrobial peptides based on sequence alignment and support vector machine-pairwise algorithm utilizing LZ-complexity.

This study concerns an attempt to establish a new method for predicting antimicrobial peptides (AMPs) which are important to the immune system. Recently, researchers are interested in designing alternative drugs based on AMPs because they have found that a large number of bacterial strains have become resistant to available antibiotics. However, researchers have encountered obstacles in the AMPs designing process as experiments to extract AMPs from protein sequences are costly and require a long set-up time. Therefore, a computational tool for AMPs prediction is needed to resolve this problem. In this study, an integrated algorithm is newly introduced to predict AMPs by integrating sequence alignment and support vector machine- (SVM-) LZ complexity pairwise algorithm. It was observed that, when all sequences in the training set are used, the sensitivity of the proposed algorithm is 95.28% in jackknife test and 87.59% in independent test, while the sensitivity obtained for jackknife test and independent test is 88.74% and 78.70%, respectively, when only the sequences that has less than 70% similarity are used. Applying the proposed algorithm may allow researchers to effectively predict AMPs from unknown protein peptide sequences with higher sensitivity.

Fast Model-based Protein Homology Discovery without Alignment

The need for quick gene categorization tools is growing as more genomes are sequenced. To evaluate a newly sequenced genome, the genes must first be identified and translated into amino acid sequences, which are then categorized into structural or functional classes. Protein homology detection using sequence alignment algorithms is the most effective way for protein categorization. Discriminative approaches such as support vector machines (SVMs) and position-specific scoring matrices (PSSM) derived from PSI-BLAST have recently been used to improve alignment algorithms. However, if a fresh sequence is being aligned, alignment algorithms take time. must be compared to a large number of previously published sequences — the same is true for SVMs. Building a PSSM for the PSSM is even more time-consuming than a fresh order It would take roughly 25 hours to implement the best-performing approaches to classify the sequences on today's computers. Describing a novel genome (20, 000 genes) as belonging to one single organism. There are hundreds of classes to choose from, though. Another flaw with alignment algorithms is that they do not construct a model of the positive class, instead of measuring the mutual distance between sequences or profiles. Only multiple alignments and hidden Markov models are common classification approaches for creating a positive class model, but they have poor classification performance. A model's advantage is that it may be evaluated for chemical features that are shared by all members of the class to get fresh insights into protein function and structure. We used LSTM to solve a well-known remote protein homology detection benchmark, in which a protein must be categorized as a member of the SCOP superfamily. LSTM achieves state-of-the-art classification performance while being significantly faster than other algorithms with similar classification performance. LSTM is five orders of magnitude quicker than the quickest SVM-based approaches and two orders of magnitude faster than methods that perform somewhat better in classification (which, however, have lower classification performance than LSTM). We applied LSTM to PROSITE classes and analyzed the derived patterns to test the modeling capabilities of the algorithm. Because it does not require established similarity metrics like BLOSUM or PAM matrices, LSTM is complementary to alignment-based techniques. The PROSITE motif was retrieved by LSTM in 8 out of 15 classes. In the remaining seven examples, alternative motifs are developed that, on average, outperform the PROSITE motifs in categorization.

PASTA: Ultra-Large Multiple Sequence Alignment for Nucleotide and Amino-Acid Sequences.

We introduce PASTA, a new multiple sequence alignment algorithm. PASTA uses a new technique to produce an alignment given a guide tree that enables it to be both highly scalable and very accurate. We present a study on biological and simulated data with up to 200,000 sequences, showing that PASTA produces highly accurate alignments, improving on the accuracy and scalability of the leading alignment methods (including SATé). We also show that trees estimated on PASTA alignments are highly accurate--slightly better than SATé trees, but with substantial improvements relative to other methods. Finally, PASTA is faster than SATé, highly parallelizable, and requires relatively little memory.

정적 주요 경로 API 시퀀스를 이용한 소프트웨어 유사성 검사

Software birthmarks are used to detect software plagiarism. For binaries, however, only a few birthmarks have been developed. In this paper, we propose a static approach to generate API sequences along major paths, which are analyzed from control flow graphs of the binaries. Since our API sequences are extracted along the most plausible paths of the binary codes, they can represent actual API sequences produced from binary executions, but in a more concise form. Our similarity measures use the Smith-Waterman algorithm that is one of the popular sequence alignment algorithms for DNA sequence analysis. We evaluate our static path-based API sequence with multiple versions of five applications. Our experiment indicates that our proposed method provides a quite reliable similarity birthmark for binaries.

A Noble Approach on Bioinformatics: Smart Sequence Alignment Algorithm applying DNA Replication (SSAADR)

Alignment generally means lining up characters of strings, allowing matches and mismatches and also the characters of one string to be placed opposite spaces made in opposing strings. Sequence alignment arises in many fields, such as: molecular biology, inexact text matching (e.g. spell checkers; web page search), speech recognition and many more. This Paper presents a new implemented algorithm for sequence alignment based on the concepts of some already established algorithms of Bioinformatics which is named as “Smart Sequence Alignment Algorithm applying DNA Replication”, in short “SSAADR”. It aligns two sequences of any length, based on filling up a matrix of size m X n where m is the length of the first sequence and n is the length of the second sequence, and using the techniques of both global and local alignment. To make the proposed algorithm faster, the theory of DNA replication has also been introduced here, according to which, while using the alternative sequences of both DNA sequences instead of the original ones, the execution time of the proposed algorithm SSAADR decreases immensely.

Analyzing Spatio-Temporal Patterns and Their Evolution via Sequence Alignment

Temporal patterns indicate consistency and change, providing insight into social processes and phenomena. This article contributes to understanding patterns in social science by confirming the existence of known patterns under new conditions and quantifying the amount of observed deviation. We introduce a technique for matching a pattern in real-world events using an extension to the sequence alignment algorithm developed in biology. We demonstrate our algorithm and its utility for social science applications using event data collected from RSS news feeds. By comparing patterns derived from events in Yemen during the Arab Spring of 2011–2012 to events in Yemen's history and to other countries during the same time period, this algorithm contributes to time geographic concepts and comparative political research.

Automatic face annotation in TV series by video/script alignment

This paper describes a method for automatically tagging the names to the faces which are collected from uncontrolled TV series videos. The detected faces are firstly partitioned into several clusters. Then we construct a face sequence based on their occurrence order in the video and denote them by cluster labels. It can be assumed that the temporal distribution of the faces in the video roughly follows the temporal distribution of the names in the script. Hence, we propose to annotate the faces by video/script alignment. A global sequence alignment algorithm is employed to find the most probable faces in the face sequence matching to the names in the name sequence. The novelty lies in that we consider the temporal order relationship of the faces and names over the whole video and directly align two heterogeneous sequences. Experiments on real-world videos have demonstrated the effectiveness and efficiency of the proposed method.

Fast DNA Sequence Alignment Algorithm Based on Quality Score Using Improved Dynamic Programming and Fuzzy Gap Cost Control

A sequence alignment algorithm is a basic building block for protein analysis and nucleic acid analysis in bioinformatics. Such alignment represents the similarities and differences of two or more compared sequences. Thus, there have been many algorithms and tools studied and developed. In this paper, we focus on the PHRED based sequence alignment algorithm using Needleman-Wunsch dynamic programming. Although it is well known and proven to be reliable to some extent, it suffers from the heavy computation of producing scoring matrix based on dynamic programming whose time complexity is O(mn). We propose a method applying quadrant method in that process to reduce the computational loads. Also, PHRED based algorithms suffer from the environment when low quality bases are frequently in tips of DNA fragments. Thus, we designed a fuzzy logic system to control the gap cost dynamically to improve the quality of the alignment. In the experiment using real genome data from NCBI (National Center for Biotechnology Information), we verify that the proposed method reduces the computational loads by half in producing scoring matrix and thus the alignment quality is also improved by our fuzzy inference system. Keywords: DNA sequence alignment, dynamic programming, fuzzy inference system, gap cost, quadrant, quality score.

A Multiple Sequence Alignment Algorithm Based on Inertia Weights Particle Swarm Optimization

A Multiple Sequence Alignment Algorithm Based on Inertia Weights Particle Swarm Optimization

GSWABE: faster GPU‐accelerated sequence alignment with optimal alignment retrieval for short DNA sequences

SummaryIn this paper, we present GSWABE, a graphics processing unit (GPU)‐accelerated pairwise sequence alignment algorithm for a collection of short DNA sequences. This algorithm supports all‐to‐all pairwise global, semi‐global and local alignment, and retrieves optimal alignments on Compute Unified Device Architecture (CUDA)‐enabled GPUs. All of the three alignment types are based on dynamic programming and share almost the same computational pattern. Thus, we have investigated a general tile‐based approach to facilitating fast alignment by deeply exploring the powerful compute capability of CUDA‐enabled GPUs. The performance of GSWABE has been evaluated on a Kepler‐based Tesla K40 GPU using a variety of short DNA sequence datasets. The results show that our algorithm can yield a performance of up to 59.1 billions cell updates per second (GCUPS), 58.5 GCUPS and 50.3 GCUPS for global, semi‐global and local alignment, respectively. Furthermore, on the same system GSWABE runs up to 156.0 times faster than the Streaming SIMD Extensions (SSE)‐based SSW library and up to 102.4 times faster than the CUDA‐based MSA‐CUDA (the first stage) in terms of local alignment. Compared with the CUDA‐based gpu‐pairAlign, GSWABE demonstrates stable and consistent speedups with a maximum speedup of 11.2, 10.7, and 10.6 for global, semi‐global, and local alignment, respectively. Copyright © 2014 John Wiley & Sons, Ltd.

Identifying duplicate functionality in textual use cases by aligning semantic actions

Developing high-quality requirements specifications often demands a thoughtful analysis and an adequate level of expertise from analysts. Although requirements modeling techniques provide mechanisms for abstraction and clarity, fostering the reuse of shared functionality (e.g., via UML relationships for use cases), they are seldom employed in practice. A particular quality problem of textual requirements, such as use cases, is that of having duplicate pieces of functionality scattered across the specifications. Duplicate functionality can sometimes improve readability for end users, but hinders development-related tasks such as effort estimation, feature prioritization, and maintenance, among others. Unfortunately, inspecting textual requirements by hand in order to deal with redundant functionality can be an arduous, time-consuming, and error-prone activity for analysts. In this context, we introduce a novel approach called ReqAligner that aids analysts to spot signs of duplication in use cases in an automated fashion. To do so, ReqAligner combines several text processing techniques, such as a use case-aware classifier and a customized algorithm for sequence alignment. Essentially, the classifier converts the use cases into an abstract representation that consists of sequences of semantic actions, and then these sequences are compared pairwise in order to identify action matches, which become possible duplications. We have applied our technique to five real-world specifications, achieving promising results and identifying many sources of duplication in the use cases.

Comparison of Sequence Alignment Algorithms

Comparison of Sequence Alignment Algorithms

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.

A Survey of Sequence Alignment Algorithms with Distributed System

A Survey of Sequence Alignment Algorithms with Distributed System

BitPAl: a bit-parallel, general integer-scoring sequence alignment algorithm.

Motivation: Mapping of high-throughput sequencing data and other bulk sequence comparison applications have motivated a search for high-efficiency sequence alignment algorithms. The bit-parallel approach represents individual cells in an alignment scoring matrix as bits in computer words and emulates the calculation of scores by a series of logic operations composed of AND, OR, XOR, complement, shift and addition. Bit-parallelism has been successfully applied to the longest common subsequence (LCS) and edit-distance problems, producing fast algorithms in practice.Results: We have developed BitPAl, a bit-parallel algorithm for general, integer-scoring global alignment. Integer-scoring schemes assign integer weights for match, mismatch and insertion/deletion. The BitPAl method uses structural properties in the relationship between adjacent scores in the scoring matrix to construct classes of efficient algorithms, each designed for a particular set of weights. In timed tests, we show that BitPAl runs 7–25 times faster than a standard iterative algorithm.Availability and implementation: Source code is freely available for download at http://lobstah.bu.edu/BitPAl/BitPAl.html. BitPAl is implemented in C and runs on all major operating systems.Contact: jloving@bu.edu or yhernand@bu.edu or gbenson@bu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Correction: QuickProbs—A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors

Correction: QuickProbs—A Fast Multiple Sequence Alignment Algorithm Designed for Graphics Processors

A New Quantum Cuckoo Search Algorithm for Multiple Sequence Alignment

AbstractMultiple sequence alignment (MSA) is one of the major problems that can be encountered in the bioinformatics field. MSA consists in aligning a set of biological sequences to extract the similarities between them. Unfortunately, this problem has been shown to be NP-hard. In this article, a new algorithm was proposed to deal with this problem; it is based on a quantum-inspired cuckoo search algorithm. The other feature of the proposed approach is the use of a randomized progressive alignment method based on a hybrid global/local pairwise algorithm to construct the initial population. The results obtained by this hybridization are very encouraging and show the feasibility and effectiveness of the proposed solution.