Sequence Alignment Algorithm Research Articles

An Efficient Bulk Synchronous Parallelized Scheduler for Bioinformatics Application on Public Cloud

Genomic sequence alignment of varied species is one of the most sort of applications in bioinformatics. In future bioinformatics technologies are expected to produce genomic data of terabyte. Bioinformatics computation require super computer for sequence alignment computation which involves huge cost. Parallelization technique is a way forward in computing sequence alignment with limited cost and time. Cloud computing and MapReduce framework play an important role in bioinformatics intensive application to achieve parallelization since it provides a consistent performance over time and it also provides good fault tolerant mechanism. The existing gene sequencing methodologies are designed based on Hadoop-MapReduce framework which adopts a serial execution strategy which is an area of concern. This work introduces a Smith-Waterman Alignment on the Bulk synchronous Parallel Map Reduce (SW-BSPMR) cloud platform for bioinformatics gene sequence alignment. This work adopts a widely accepted and accurate SW algorithm for sequence alignment and parallel synchronous scheduler methodology of map and reduce framework process is considered. A customized MapReduce based on Microsoft Azure cloud platform is developed to overcome the issue in Hadoop-MapReduce framework. The experimental study presented in this work proves that the SW-BSPMR can accurately and effectively align bioinformatics genomic sequences of various read length.

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.

A local alignment approach to similarity analysis of industrial alarm flood sequences

Similar alarm sequence alignment algorithms have been used to find similar alarm floods in the historical database for the prediction and prevention of alarm floods. However, the existing modified Smith–Waterman (SW) algorithm has a high computation complexity, preventing its online applications within a tolerable computation time period. This paper proposes a new local alignment algorithm, based on the basic local alignment search tool (BLAST). The novelty of the proposed algorithm is three-fold. First, a priority-based similarity scoring strategy makes the proposed algorithm more sensitive to alarms having higher alarm priorities. Second, a set-based pre-matching mechanism avoids unnecessary computations by excluding all irrelevant alarm floods and alarm tags. Third, the seeding and extending steps of the conventional BLAST are adapted for alarm floods, which reduce the searching space significantly. Owing to the novelties, the proposed algorithm is much faster in computation and provides a higher alignment accuracy than the SW algorithm. The efficiency of the proposed algorithm is demonstrated by industrial case studies based on the historical alarm floods from an oil conversion plant.

A Comprehensive Analysis of Sequence Alignment Algorithms for LongRead Sequencing

As the length of sequencing read increasing, greater bioinformation is demanded from longread aligner. The short-read aligner is often widely used to make alignment very fast and accurate, but the approach is ill-suited to finding longer, gapped alignments with long indels. A wide variety of alignment algorithms and aligners have been subsequently developed over the past few years. In this article, we survey the theoretical foundations that underlie long-read alignments and highlight the options and practical trade-offs that need to be considered. Through the evaluation of the sophisticated experiments both on simulated and real data, we illustrate the performance of these aligners on the accuracy, the time and memory cost, as well as the scalability for the modern multi-core architecture. We also consider the future development of long-read alignment algorithms. Keywords: Alignment algorithm, long read sequencing, memory usage, performance, runtime, sequence analysis.

Survey of the use of genetic algorithm for multiple sequence alignment

Multiple Sequence Alignment (MSA) is used in genomic analysis, such as the identification of conserved sequence motifs, the estimation of evolutionary divergence between sequences, and the genes’ historical relationships inference. Several researches were conducted to determine the level of similarity of a set of sequences. Due to the problem of the NP-complete class property, a number of researches use genetic algorithms (GA) to find a solution to the multiple sequence alignment. However, the nature of genetic algorithms makes the complexity extremely high due to the redundancy provided by the different operators. The aim of this paper is to study some proposed GA solutions provided for MSA and to compare them using some criteria which we believe any solution should comply with in matters of representativeness, closeness and original sequence invariance.

Reactive Sampling for Efficient Defect Source Identification

Defectivity control in semiconductor manufacturing is crucial to improve the product quality and to reduce the production cost. When defects are detected, the objective is to identify the tool that generates them. Tool commonality analysis (TCA) is believed to be an efficient method for defect source identification. The critical element of successful TCA is that multiple entry lots within the same process flow are available for the analysis. Hence, since deployment of dynamic sampling strategies, lots selected for inspection are taken from different process flows at different manufacturing levels. In these cases, the TCA cannot identify the defect source but a set of potentially faulty tools. This paper consists in increasing the number of inspected lots that can be used for the TCA. The approach is a combination of two methods. The first one is the comparable lots identification processed by a sequence alignment algorithm. The second method is the reactive sampling algorithm based on the set covering model, this algorithm consists in sampling the minimal number of lots that cover the maximal number of potentially faulty tools. The industrial experiments of the proposed algorithms show a significant increase of number of available lots for analysis.

Bit‐oriented format extraction approach for automatic binary protocol reverse engineering

Protocol message format extraction is a principal process of automatic network protocol reverse engineering when target protocol specifications are not available. However, binary protocol reverse engineering has been a new challenge in recent years for approaches that traditionally have dealt with text-based protocols rather than binary protocols. In this study, the authors propose a novel approach called PRE-Bin that automatically extracts binary-type fields of binary protocols based on fine-grained bits. First, a silhouette coefficient is introduced into the hierarchical clustering to confirm the optimal clustering number of binary frames. Second, a modified multiple sequence alignment algorithm, in which the matching process and back-tracing rules are redesigned, is also proposed to analyse binary field features. Finally, a Bayes decision model is invoked to describe field features and determine bit-oriented field boundaries. The maximum a posteriori criterion is leveraged to complete an optimal protocol format estimation of binary field boundaries. The authors implemented a prototype system of PRE-Bin to infer the specification of binary protocols from actual traffic traces. Experimental results indicate that PRE-Bin effectively extracts binary fields and outperforms the existing algorithms.

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.

Extracting DNA words based on the sequence features: non-uniform distribution and integrity.

BackgroundDNA sequence can be viewed as an unknown language with words as its functional units. Given that most sequence alignment algorithms such as the motif discovery algorithms depend on the quality of background information about sequences, it is necessary to develop an ab initio algorithm for extracting the “words” based only on the DNA sequences.MethodsWe considered that non-uniform distribution and integrity were two important features of a word, based on which we developed an ab initio algorithm to extract “DNA words” that have potential functional meaning. A Kolmogorov-Smirnov test was used for consistency test of uniform distribution of DNA sequences, and the integrity was judged by the sequence and position alignment. Two random base sequences were adopted as negative control, and an English book was used as positive control to verify our algorithm. We applied our algorithm to the genomes of Saccharomyces cerevisiae and 10 strains of Escherichia coli to show the utility of the methods.ResultsThe results provide strong evidences that the algorithm is a promising tool for ab initio building a DNA dictionary.ConclusionsOur method provides a fast way for large scale screening of important DNA elements and offers potential insights into the understanding of a genome.Electronic supplementary materialThe online version of this article (doi:10.1186/s12976-016-0028-3) contains supplementary material, which is available to authorized users.

A Novel PD-L1-targeting Antagonistic DNA Aptamer With Antitumor Effects.

The PD-1/PD-L1 axis is a major pathway involved in tumor immune evasion. Here, we report the novel PD-L1 antagonizing DNA aptamer (aptPD-L1) and demonstrate an integrated pipeline that expedites therapeutic aptamer development. Aptamer can exert antibody-mimic functions and is advantageous over antibody for its chemically synthetic nature, low immunogenicity, and efficient tissue penetration. Our results showed that aptPD-L1 blocked the binding between human PD-1 and PD-L1. Experiments using murine models showed that aptPD-L1 promoted in vitro lymphocyte proliferation and suppressed in vivo tumor growth without the induction of observable liver or renal toxicity. Analyses on the aptPD-L1-treated tumors further revealed elevated levels of infiltrating CD4+ and CD8+ T cells, intratumoral IL-2, TNF-α, interferon (IFN)-γ and the C-X-C motif chemokines, CXCL9 and CXCL10. The CD8+ T cells in the aptPD-L1-treated tumors had higher CXCR3 expression level compared to the random-sequence oligonucleotides-treated ones. Besides, the length and density of CD31+ intratumoral microvessels were significantly decreased in the aptPD-L1 treatment group. Collectively, our data suggested that aptPD-L1 helps T cell function restoration and modifies tumor microenvironment. These chemokines might orchestrate together to attract more T cells into the tumor tissues to form the positive amplification loop against tumor growth, indicating the translational potential of aptPD-L1 in cancer immunotherapy.

Classification of biosensor time series using dynamic time warping: applications in screening cancer cells with characteristic biomarkers.

The development of biosensors that produce time series data will facilitate improvements in biomedical diagnostics and in personalized medicine. The time series produced by these devices often contains characteristic features arising from biochemical interactions between the sample and the sensor. To use such characteristic features for determining sample class, similarity-based classifiers can be utilized. However, the construction of such classifiers is complicated by the variability in the time domains of such series that renders the traditional distance metrics such as Euclidean distance ineffective in distinguishing between biological variance and time domain variance. The dynamic time warping (DTW) algorithm is a sequence alignment algorithm that can be used to align two or more series to facilitate quantifying similarity. In this article, we evaluated the performance of DTW distance-based similarity classifiers for classifying time series that mimics electrical signals produced by nanotube biosensors. Simulation studies demonstrated the positive performance of such classifiers in discriminating between time series containing characteristic features that are obscured by noise in the intensity and time domains. We then applied a DTW distance-based k-nearest neighbors classifier to distinguish the presence/absence of mesenchymal biomarker in cancer cells in buffy coats in a blinded test. Using a train–test approach, we find that the classifier had high sensitivity (90.9%) and specificity (81.8%) in differentiating between EpCAM-positive MCF7 cells spiked in buffy coats and those in plain buffy coats.

Short Read Alignment Using SOAP2.

Next-generation sequencing (NGS) technologies have rapidly evolved in the last 5 years, leading to the generation of millions of short reads in a single run. Consequently, various sequence alignment algorithms have been developed to compare these reads to an appropriate reference in order to perform important downstream analysis. SOAP2 from the SOAP series is one of the most commonly used alignment programs to handle NGS data, and it efficiently does so using low computer memory usage and fast alignment speed. This chapter describes the protocol used to align short reads to a reference genome using SOAP2, and highlights the significance of using the in-built command-line options to tune the behavior of the algorithm according to the inputs and the desired results.

Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets.

Sequence alignment is an important tool for describing the relationships between DNA sequences. Many sequence alignment algorithms exist, differing in efficiency, in their models of the sequences, and in the relationship between sequences. The focus of this study is to obtain an optimal alignment between two sequences of biological data, particularly DNA sequences. The algorithm is discussed with particular emphasis on time, speedup, and efficiency optimizations. Parallel programming presents a number of critical challenges to application developers. Today’s supercomputer often consists of clusters of SMP nodes. Programming paradigms such as OpenMP and MPI are used to write parallel codes for such architectures. However, the OpenMP programs cannot be scaled for more than a single SMP node. However, programs written in MPI can have more than single SMP nodes. But such a programming paradigm has an overhead of internode communication. In this work, we explore the tradeoffs between using OpenMP and MPI. We demonstrate that the communication overhead incurs significantly even in OpenMP loop execution and increases with the number of cores participating. We also demonstrate a communication model to approximate the overhead from communication in OpenMP loops. Our results are astonishing and interesting to a large variety of input data files. We have developed our own load balancing and cache optimization technique for message passing model. Our experimental results show that our own developed techniques give optimum performance of our parallel algorithm for various sizes of input parameter, such as sequence size and tile size, on a wide variety of multicore architectures.

The Method of a Gene Sequence Alignment BWT Index Based on Hadoop

Gene sequence alignment, used to recognize the homology and variability in different species, is an important part of Bioinformatics. Creating indexes is a crucial step of gene sequence alignment algorithm. Usual algorithms of creating indexes are divided into two types. The first is algorithm based on hash table, while another is based on suffix tree or suffix array, among which BWT (Burrows-Wheeler Transform) index is a significant index structure. Currently, BWT index needs several hours’ serial computing in building a large genome sequence (such as human genome sequence). A parallel computing method based on Hadoop is presented is this paper to build suffix array and BWT index. Map Reduce is adopted as a type of data processing function, cutting suffix array into block, which will be handled separately. Ultimately, totally ordered suffix array and BWT index are output, reducing the time in building index. Meanwhile, verifying the effectiveness of the algorithm by experiments.

A Complete and Accurate Short Sequence Alignment Algorithm for Repeats

Eukaryotic genomes contain a significant fraction of repeats, which have very important biomedical function. Thus, aligning repeats from short sequences back to reference genome is the key step for further genome analysis. Unfortunately, the current aligning algorithms performed poorly in distinguishing repeats and nonrepeats. To this end, we proposed a new algorithm, named HashRepAligner, to address this problem. Finally, the cross comparison with other algorithms was performed, and the results indicated that HashRepAligner outperformed other aligners in terms of the detecting repeats.

An enhanced algorithm for multiple sequence alignment of protein sequences using genetic algorithm.

One of the most fundamental operations in biological sequence analysis is multiple sequence alignment (MSA). The basic of multiple sequence alignment problems is to determine the most biologically plausible alignments of protein or DNA sequences. In this paper, an alignment method using genetic algorithm for multiple sequence alignment has been proposed. Two different genetic operators mainly crossover and mutation were defined and implemented with the proposed method in order to know the population evolution and quality of the sequence aligned. The proposed method is assessed with protein benchmark dataset, e.g., BALIBASE, by comparing the obtained results to those obtained with other alignment algorithms, e.g., SAGA, RBT-GA, PRRP, HMMT, SB-PIMA, CLUSTALX, CLUSTAL W, DIALIGN and PILEUP8 etc. Experiments on a wide range of data have shown that the proposed algorithm is much better (it terms of score) than previously proposed algorithms in its ability to achieve high alignment quality.

Simple scalable nucleotic FPGA based short read aligner for exhaustive search of substitution errors

Abstract With the advent of the new and continuously improving technologies, in a couple of years DNA sequencing can be as commonplace as a simple blood test. The growth of sequencing efficiency has a larger exponent than the Moore’s law of standard processors, hence alignment and further processing of sequenced data is the bottleneck. The usage of FPGA (Field Programmable Gate Arrays) technology may provide an efficient alternative. We propose a simple algorithm for DNA sequence alignment, which can be realized efficiently by nucleotic principal agents of Non.Neumann nature. The prototype FPGA implementation runs on a small Terasic DE1-SoC demo board with a Cyclone V chip. We present test results and furthermore analyse the theoretical scalability of this system, showing that the execution time is independent of the length of reference genome sequences. A special advantage of this parallel algorithm is that it performs exhaustive search producing all match variants up to a predetermined number of point (mutation) errors.

Spatio-temporal segmentation for the similarity measurement of deforming meshes

Although there have been a large body of works on computing the similarity of static shapes, similarity judgments on deforming meshes are not studied well. In this study, we investigate a similarity measurement method for comparing two deforming meshes. Based on the degree of deformation, we first binarily label each triangle within each frame as either `deformed' or `rigid', then merge the `deformed' triangles in both spatial and temporal domains for the segmentation. The segmentation results are encoded in a form of evolving graph, with an aim of obtaining a compact representation of the motion of the mesh. Finally, we formulate the similarity measurement as a sequence matching problem: after clustering similar graphs and assigning each of the graphs with the cluster labels, each deforming mesh is represented with a sequence of labels. Then, we apply a sequence alignment algorithm to compute the locally optimal alignment between the two label sequences, and to compute the similarity by normalizing the alignment score. The experimental results over several datasets show that the similarities of animation data can be captured correctly using our approach. This may be significant, as it solves a problem that cannot be handled by current approaches.

MSAIndelFR: a scheme for multiple protein sequence alignment using information on indel flanking regions.

BackgroundThe alignment of multiple protein sequences is one of the most commonly performed tasks in bioinformatics. In spite of considerable research and efforts that have been recently deployed for improving the performance of multiple sequence alignment (MSA) algorithms, finding a highly accurate alignment between multiple protein sequences is still a challenging problem.ResultsWe propose a novel and efficient algorithm called, MSAIndelFR, for multiple sequence alignment using the information on the predicted locations of IndelFRs and the computed average log–loss values obtained from IndelFR predictors, each of which is designed for a different protein fold. We demonstrate that the introduction of a new variable gap penalty function based on the predicted locations of the IndelFRs and the computed average log–loss values into the proposed algorithm substantially improves the protein alignment accuracy. This is illustrated by evaluating the performance of the algorithm in aligning sequences belonging to the protein folds for which the IndelFR predictors already exist and by using the reference alignments of the four popular benchmarks, BAliBASE 3.0, OXBENCH, PREFAB 4.0, and SABRE (SABmark 1.65).ConclusionsWe have proposed a novel and efficient algorithm, the MSAIndelFR algorithm, for multiple protein sequence alignment incorporating a new variable gap penalty function. It is shown that the performance of the proposed algorithm is superior to that of the most–widely used alignment algorithms, Clustal W2, Clustal Omega, Kalign2, MSAProbs, MAFFT, MUSCLE, ProbCons and Probalign, in terms of both the sum–of–pairs and total column metrics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0826-3) contains supplementary material, which is available to authorized users.

A hydrophobic proclivity index for protein alignments

Sequence alignment algorithms are fundamental to modern bioinformatics. Sequence alignments are widely used in diverse applications such as phylogenetic analysis, database searches for related sequences to aid identification of unknown protein domain structures and classification of proteins and protein domains. Additionally, alignment algorithms are integral to the location of related proteins to secure understanding of unknown protein functions, to suggest the folded structure of proteins of unknown structure from location of homologous proteins and/or by locating homologous domains of known 3D structure. For proteins, alignment algorithms depend on information about amino acid substitutions that allows for matching sequences that are similar, but not exact. When primary sequence percent identity falls below about 25%, algorithms often fail to identify proteins that may have similar 3D structure. We have created a hydrophobicity scale and a matching dynamic programming algorithm called TMATCH (unpublished report) that is able to match proteins with remote homologs with similar secondary/tertiary structure, even with very low primary sequence matches. In this paper, we describe how we arrived at the hydrophobic scale, how it provides much more information than percent identity matches and some of the implications for better alignments and understanding protein structure.