A Novel Markov Pairwise Protein Sequence Alignment Method for Sequence 665 Comparison

This chapter guides through the development of a pairwise sequence alignment tool that implements global, ends-free global, and local alignment. It focuses on the algorithms needed to implement the tool. However, because Python is a commonly used programming language for bioinformatics, the chapter encourages you to apply the Python concepts to implement your pairwise sequence alignment tool. The chapter begins by analysing the output report of an existing local sequence alignment tool, EMBOSS Water, to familiarize ourselves with its inputs, outputs, and functionality. It then looks at an overview of simple pairwise alignment (SPA), and introduces the concept of algorithms–taking a look at different ways to express algorithms. Towards the end, the chapter explains the longest common subsequence (LCS) algorithm and how it can be extended to implement local and global pairwise alignment. It then assesses the complexity of algorithms–that is, how much memory and time they require.

In this paper, we investigate the feasibility of computing DNA patterns on FPGAs by utilising high-performance and parallel computing. In particular, the report analyses and contrasts the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the dynamic and heuristic alternatives that are currently on the market. In this research project, we investigate the use of parallel and high-performance computing to determine how DNA patterns can be computed on FPGAs. In particular, the report details the findings of a study that compared the FSM -based core implementation of the well-known Smith-Waterman pair-wise sequence alignment algorithm with the algorithm's current dynamic and heuristic programming methods. The study was carried out by comparing these three aspects of the algorithm. The traditional methods of sequence alignment that are software-implied are not adequate to meet the actual data rate requirements. A strategy that is based on the use of hardware makes it possible to process multiple new databases in parallel, which enables a high degree of scalability. A finite state machine serves as the primary data processor in our demonstration of how to organize the sequences of amino acids in a protein (FSM). We also investigate the effectiveness of bit-based sequence alignment algorithms and present the inner stage pipelined FPGA architecture for implementing sequence alignment. Both of these are presented in this paper. Synchronized controllers are utilized in the programming techniques of the algorithm for the purpose of parallel sequence alignment. Inference is the foundation of traditional software-based methods for sequence alignment, but these methods are unable to keep up with the required data rates. A strategy that is based on hardware allows for the processing of parallel tasks with a large number of new databases and provides excellent scalability. Our explanation of the classification of protein sequences is based on a Finite State Machine, which serves as the primary processing unit (FSM). In addition to this, we investigate how well bit-based sequence alignment algorithms work and present the inner stage pipelined Field Programmable Gate Array architecture that can be used to implement sequence alignment. Synchronized controllers are utilized in the process of performing sequence alignment in parallel. The architecture as a whole was made to be scalable and fast, with a small number of computations and the ability to perform parallel processing in hardware using field-programmable gate arrays to compare bits in patterns. Finally, we use FPGA hardware synthesis to show off impressive performance and low resource consumption.

The emerging science of sequences: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins, edited by Andreas Baxevanis and B. F. Francis Ouellette

Pairwise sequence alignment is a fundamental compute-intensive problem in bioinformatics that has helped researchers analyse biological sequences. The analysis has helped biologists detect pathogens, develop drugs, and identify common genes. The biological sequence database has been growing rapidly due to new sequences being discovered. This has brought many new challenges including sequence database searching and aligning long sequences. To solve these problems, many sequence alignment algorithms have been developed. These algorithms employ various techniques to efficiently find optimal or nearly-optimal alignments. In this paper, we present the popular past and recent work on both local and global pairwise sequence alignment algorithms. In addition to identifying the techniques used, the advantages and limitations of the algorithms are also presented.

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.

VP1 is the most suitable region for use in the identification of enterovirus. Although VP1 sequencing methods may vary, it is necessary to agree on a common strategy of sequence analysis. Identification of a strain type may be achieved by three different approaches: pairwise sequence alignment, multiple-sequence alignment, and phylogenetic inference. Other methods are also available, but they are not simple enough to be performed at a virology laboratory. The performances of these methods were evaluated with nucleotide and protein sequences obtained from 32 original samples, 8 enterovirus isolates, and 64 GenBank sequences. Pairwise sequence alignment methods had very different results. The DNASTAR package identified only 28.8% of enterovirus strains, while the Genetics Computer Group package identified 50.0 or 72.1% of enterovirus strains when nucleotide or amino acid sequences were analyzed, respectively. Multiple-sequence alignment methods identified 94.2% (Clustal W program) or 92.3% (Pileup program) of the enterovirus strains, while the phylogenetic method increased this rate to 99.0%. Comparative evaluation of these analysis methods showed that the Clustal W program (version 1.81), a freely available multiple-sequence alignment program, presented one of the best performances when used with the correct criteria. Other commercial and expensive programs did not achieve the same performances, making them less suitable for molecular typing of enteroviruses. Finally, although phylogenetic inference is the most demanding method in terms of knowledge of the user, it remained the best option analyzed.

MotivationMost methods for pairwise and multiple genome alignment use fast local homology search tools to identify anchor points, i.e. high-scoring local alignments of the input sequences. Sequence segments between those anchor points are then aligned with slower, more sensitive methods. Finding suitable anchor points is therefore crucial for genome sequence comparison; speed and sensitivity of genome alignment depend on the underlying anchoring methods.ResultsIn this article, we use filtered spaced word matches to generate anchor points for genome alignment. For a given binary pattern representing match and don’t-care positions, we first search for spaced-word matches, i.e. ungapped local pairwise alignments with matching nucleotides at the match positions of the pattern and possible mismatches at the don’t-care positions. Those spaced-word matches that have similarity scores above some threshold value are then extended using a standard X-drop algorithm; the resulting local alignments are used as anchor points. To evaluate this approach, we used the popular multiple-genome-alignment pipeline Mugsy and replaced the exact word matches that Mugsy uses as anchor points with our spaced-word-based anchor points. For closely related genome sequences, the two anchoring procedures lead to multiple alignments of similar quality. For distantly related genomes, however, alignments calculated with our filtered-spaced-word matches are superior to alignments produced with the original Mugsy program where exact word matches are used to find anchor points.Availability and implementationhttp://spacedanchor.gobics.deSupplementary informationSupplementary data are available at Bioinformatics online.

Goal: Over the decades, there have been improvements in the sequence alignment algorithm, with significant advances in various aspects such as complexity and accuracy. However, human-defined algorithms have an explicit limitation in view of developmental completeness. This paper introduces a novel local alignment method to obtain optimal sequence alignment based on reinforcement learning. Methods: There is a DQNalign algorithm that learns and performs sequence alignment through deep reinforcement learning. This paper proposes a DQN x-drop algorithm that performs local alignment without human intervention by combining the x-drop algorithm with this DQNalign algorithm. The proposed algorithm performs local alignment by repeatedly observing the subsequences and selecting the next alignment direction until the x-drop algorithm terminates the DQNalign algorithm. This proposed algorithm has an advantage in view of linear computational complexity compared to conventional local alignment algorithms. Results: This paper compares alignment performance (coverage and identity) and complexity for a fair comparison between the proposed DQN x-drop algorithm and the conventional greedy x-drop algorithm. Firstly, we prove the proposed algorithm's superiority by comparing the two algorithms’ computational complexity through numerical analysis. After that, we tested the alignment performance actual HEV and E.coli sequence datasets. The proposed method shows the comparable identity and coverage performance to the conventional alignment method while having linear complexity for the n}{}X parameter. Conclusions: Through this study, it was possible to confirm the possibility of a new local alignment algorithm that minimizes computational complexity without human intervention.

Differences between pair-wise and multi-sequence alignment methods affect vertebrate genome comparisons

While pairwise sequence alignment (PSA) by dynamic programming is guaranteed to generate one of the optimal alignments, multiple sequence alignment (MSA) of highly divergent sequences often results in poorly aligned sequences, plaguing all subsequent phylogenetic analysis. One way to avoid this problem is to use only PSA to reconstruct phylogenetic trees, which can only be done with distance-based methods. I compared the accuracy of this new computational approach (named PhyPA for phylogenetics by pairwise alignment) against the maximum likelihood method using MSA (the ML+MSA approach), based on nucleotide, amino acid and codon sequences simulated with different topologies and tree lengths. I present a surprising discovery that the fast PhyPA method consistently outperforms the slow ML+MSA approach for highly diverged sequences even when all optimization options were turned on for the ML+MSA approach. Only when sequences are not highly diverged (i.e., when a reliable MSA can be obtained) does the ML+MSA approach outperforms PhyPA. The true topologies are always recovered by ML with the true alignment from the simulation. However, with MSA derived from alignment programs such as MAFFT or MUSCLE, the recovered topology consistently has higher likelihood than that for the true topology. Thus, the failure to recover the true topology by the ML+MSA is not because of insufficient search of tree space, but by the distortion of phylogenetic signal by MSA methods. I have implemented in DAMBE PhyPA and two approaches making use of multi-gene data sets to derive phylogenetic support for subtrees equivalent to resampling techniques such as bootstrapping and jackknifing.

The Smith-Waterman algorithm is used in Bio-informatics to perform pairwise local alignment between a query sequence and a subject sequence. We present a GPU based parallel version of this algorithm that is able to perform pair-wise alignment faster than previous algorithms. In particular, it parallelizes each alignment, rather than relying on parallelism across multiple pair alignments, which many other proposed GPU algorithms do. As a result it scales better. We further extend our algorithm to work efficiently on a cluster of GPUs. At a high level, our approach subdivides the iterative computation of elements of a matrix among blocks of processors such that each block can simply recompute the data it needs instead of waiting for other processors to compute them. Sometimes this may lead to excessive recomputation, however. We evaluate these cases and employ a hybrid approach, recomputing only limited data and communicating the rest. Our algorithm is also extended to produce not only the best but all ‘best K’ alignments. Our results on SSCA#1 benchmark show that our method is upto 5-24 times faster than previous method.

Non-coding RNAs (ncRNAs) show a unique evolutionary process in which the substitutions of distant bases are correlated in order to conserve the secondary structure of the ncRNA molecule. Therefore, the multiple alignment method for the detection of ncRNAs should take into account both the primary sequence and the secondary structure. Recently, there has been intense focus on multiple alignment investigations for the detection of ncRNAs; however, most of the proposed methods are designed for global multiple alignments. For this reason, these methods are not appropriate to identify locally conserved ncRNAs among genomic sequences. A more efficient local multiple alignment method for the detection of ncRNAs is required. We propose a new local multiple alignment method for the detection of ncRNAs. This method uses a local multiple alignment construction procedure inspired by ProDA, which is a local multiple aligner program for protein sequences with repeated and shuffled elements. To align sequences based on secondary structure information, we propose a new alignment model which incorporates secondary structure features. We define the conditional probability of an alignment via a conditional random field and use a gamma-centroid estimator to align sequences. The locally aligned subsequences are clustered into blocks of approximately globally alignable subsequences between pairwise alignments. Finally, these blocks are multiply aligned via MXSCARNA. In benchmark experiments, we demonstrate the high ability of the implemented software, SCARNA_LM, for local multiple alignment for the detection of ncRNAs. The C++ source code for SCARNA_LM and its experimental datasets are available at http://www.ncrna.org/software/scarna_lm/download. Supplementary data are available at Bioinformatics online.

The tremendous quantity and quality of data obtained by conformations of DNA and protein sequences makes their analysis very time consuming, complex, expensive and impractical. Therefore, the feasible way to identify new sequences is to compare them with well-known sequences available in established genetic databank. Comparing sequences may reveal functional, structural, and evolutionary analogies between sequences. Needleman and Wunsch (NW), and Smith and Waterman (SW) algorithms are pioneers in dynamic programming matrix for comparing two sequences with gap penalty function. However, for long sequences both methods contribute toward time and space complexity. FASTA and BLAST are heuristic method based on hitting techniques for fast detection of similar region; unfortunately they produced results with no sensitivity. There remains a need for an efficient method(s) that can detect similar regions in two sequences with accurate results and reasonable time. In this paper, we extend an existing approach to develop an efficient parallel algorithm for pairwise local sequence alignment. Our method is based on load-balancing algorithm with the CPU scheduling technique in order to accelerate the calculation of data-dependency problem in sequences alignment. Using X86-based PC with eight logical processors we able to apply 4 MBP on the proposed algorithm with a speedup of 33% increase compared to the original SW algorithm.

With the development of new sequencing techniques, whole genomes of many species have become available. This huge amount of data gives rise to new opportunities and challenges. These new sequences provide valuable information on relationships among species, e.g. genome recombination and conservation. One of the principal ways to investigate such information is multiple sequence alignment (MSA). Currently, there is large amount of MSA data on the internet, such as the UCSC genome database, but how to effectively use this information to solve classical and new problems is still an area lacking of exploration. In this thesis, we explored how to use this information in four problems, i.e. sequence orthology search problem, multiple alignment improvement problem, short read mapping problem, and genome rearrangement inference problem. For the first problem, we developed a EM algorithm to iteratively align a query with a multiple alignment database with the information from a phylogeny relating the query species and the species in the multiple alignment. We also infer the query's location in the phylogeny. We showed that by doing alignment and phylogeny inference together, we can improve the accuracies for both problems. For the second problem, we developed an optimization algorithm to iteratively refine the multiple alignment quality. Experiment results showed our algorithm is very stable in term of resulting alignments. The results showed that our method is more accurate than existing methods, i.e. Mafft, Clustal-O, and Mavid, on test data from three sets of species from the UCSC genome database. For the third problem, we developed a model, PhyMap, to align a read to a multiple alignment allowing mismatches and indels. PhyMap computes local alignments of a query sequence against a fixed multiple-genome alignment of closely related species. PhyMap uses a known phylogenetic tree on the species in the multiple alignment to improve the quality of its computed alignments while also estimating the placement of the query on this tree. Both theoretical computation and experiment results show that our model can differentiate between orthologous and paralogous alignments better than other popular short read mapping tools (BWA, BOWTIE and BLAST). For the fourth problem, we gave a simple genome recombination model which can express insertions, deletions, inversions, translocations and inverted translocations on aligned genome segments. We also developed an MCMC algorithm to infer the order of the query segments. We proved that using any Euclidian metrics to measure distance between two sequence orders in the tree optimization goal function will lead to a degenerated solution where the inferred order will be the order of one of the leaf nodes. We also gave a graph-based formulation of the problem which can represent the probability distribution of the order of the query sequences.

Introduction: Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Vitae sapien pellentesque habitant morbi tristique senectus et netus. Dignissim cras tincidunt lobortis feugiat vivamus at augue eget arcu. At risus viverra adipiscing at in. Cras semper auctor neque vitae tempus quam. Sed cras ornare arcu dui. Turpis massa sed elementum tempus. Risus commodo viverra maecenas accumsan lacus vel facilisis volutpat est.
 Objectives: Effective data management has become an absolute necessity in today's world as a direct result of the widespread adoption of electronic medical records for patients. For medicine and healthcare domains, particularly effective web healthcare providers, computer-assisted categorization of such information into functional classifications such as medical issues or illnesses may save time and efforts. However, a substantial volume of data in the healthcare domain is still unstructured.
 Methods: The utilization of unstructured text datasets is a challenge in comparison with structured text datasets. Many researchers in various domains have proposed to convert unstructured text into structured text. The text needs to be built in a fixed and aligned pattern for easy comparison, matching, and classification. In the text classification process, accuracy is one of the challenges when text is unstructured. The text with a fixed pattern is easy to classify. The regular expressions are sequences of characters with fixed patterns of text. This strength of regular expression is used in the proposed text classification. Finding patterns in unstructured text is possible with the use of sequence alignment. Basically, sequence alignment is a concept of biomedical research, but the same concept is used to align text snippets that are not perfectly similar to each other.
 Results: The sequence alignment technique detects the similarity score assigned to each potential alignment in order to choose among the numerous local alignments of sequences. The proposed local pairwise alignment method is used to get sequence alignments, which are useful to generate regular expressions.
 Conclusions: A regular expression is a string of characters used to describe a text pattern. The regular expressions are typically created manually by the domain experts. The proposed work is the automatic generation of regular expressions from aligned sequences with a bottom up approach. The generated regular expressions are used as a dataset on which various machine learning algorithms are applied for text classification.