Smith-Waterman Local Alignment Algorithm Research Articles

Editorial Introduction: Governance, Citizenry, and Methodological Advancement in Policy Research

Editorial Introduction: Governance, Citizenry, and Methodological Advancement in Policy Research

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.

Protein database search of hybrid alignment algorithm based on GPU parallel acceleration

In biological research, alignment of protein sequences by computer is often needed to find similarities between them. Although results can be computed in a reasonable time for alignment of two sequences, it is still very central processing unit (CPU) time-consuming when solving massive sequences alignment problems such as protein database search. In this paper, an optimized protein database search method is presented and tested with Swiss-Prot database on graphic processing unit (GPU) devices, and further, the power of CPU multi-threaded computing is also involved to realize a GPU-based heterogeneous parallelism. In our proposed method, a hybrid alignment approach is implemented by combining Smith–Waterman local alignment algorithm with Needleman–Wunsch global alignment algorithm, and parallel database search is realized with compute unified device architecture (CUDA) parallel computing framework. In the experiment, the algorithm is tested on a lower-end and a higher-end personal computers equipped with GeForce GTX 750 Ti and GeForce GTX 1070 graphics cards, respectively. The results show that the parallel method proposed in this paper can achieve a speedup up to 138.86 times over the serial counterpart, improving efficiency and convenience of protein database search significantly.

Partitioning clustering algorithms for protein sequence data sets

BackgroundGenome-sequencing projects are currently producing an enormous amount of new sequences and cause the rapid increasing of protein sequence databases. The unsupervised classification of these data into functional groups or families, clustering, has become one of the principal research objectives in structural and functional genomics. Computer programs to automatically and accurately classify sequences into families become a necessity. A significant number of methods have addressed the clustering of protein sequences and most of them can be categorized in three major groups: hierarchical, graph-based and partitioning methods. Among the various sequence clustering methods in literature, hierarchical and graph-based approaches have been widely used. Although partitioning clustering techniques are extremely used in other fields, few applications have been found in the field of protein sequence clustering. It is not fully demonstrated if partitioning methods can be applied to protein sequence data and if these methods can be efficient compared to the published clustering methods.MethodsWe developed four partitioning clustering approaches using Smith-Waterman local-alignment algorithm to determine pair-wise similarities of sequences. Four different sets of protein sequences were used as evaluation data sets for the proposed methods.ResultsWe show that these methods outperform several other published clustering methods in terms of correctly predicting a classifier and especially in terms of the correctness of the provided prediction. The software is available to academic users from the authors upon request.

SWPS3 – fast multi-threaded vectorized Smith-Waterman for IBM Cell/B.E. and ×86/SSE2

BackgroundWe present swps3, a vectorized implementation of the Smith-Waterman local alignment algorithm optimized for both the Cell/BE and ×86 architectures. The paper describes swps3 and compares its performances with several other implementations.FindingsOur benchmarking results show that swps3 is currently the fastest implementation of a vectorized Smith-Waterman on the Cell/BE, outperforming the only other known implementation by a factor of at least 4: on a Playstation 3, it achieves up to 8.0 billion cell-updates per second (GCUPS). Using the SSE2 instruction set, a quad-core Intel Pentium can reach 15.7 GCUPS. We also show that swps3 on this CPU is faster than a recent GPU implementation. Finally, we note that under some circumstances, alignments are computed at roughly the same speed as BLAST, a heuristic method.ConclusionThe Cell/BE can be a powerful platform to align biological sequences. Besides, the performance gap between exact and heuristic methods has almost disappeared, especially for long protein sequences.

NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition

BackgroundBiomedical named entity recognition (Bio-NER) is a challenging problem because, in general, biomedical named entities of the same category (e.g., proteins and genes) do not follow one standard nomenclature. They have many irregularities and sometimes appear in ambiguous contexts. In recent years, machine-learning (ML) approaches have become increasingly common and now represent the cutting edge of Bio-NER technology. This paper addresses three problems faced by ML-based Bio-NER systems. First, most ML approaches usually employ singleton features that comprise one linguistic property (e.g., the current word is capitalized) and at least one class tag (e.g., B-protein, the beginning of a protein name). However, such features may be insufficient in cases where multiple properties must be considered. Adding conjunction features that contain multiple properties can be beneficial, but it would be infeasible to include all conjunction features in an NER model since memory resources are limited and some features are ineffective. To resolve the problem, we use a sequential forward search algorithm to select an effective set of features. Second, variations in the numerical parts of biomedical terms (e.g., "2" in the biomedical term IL2) cause data sparseness and generate many redundant features. In this case, we apply numerical normalization, which solves the problem by replacing all numerals in a term with one representative numeral to help classify named entities. Third, the assignment of NE tags does not depend solely on the target word's closest neighbors, but may depend on words outside the context window (e.g., a context window of five consists of the current word plus two preceding and two subsequent words). We use global patterns generated by the Smith-Waterman local alignment algorithm to identify such structures and modify the results of our ML-based tagger. This is called pattern-based post-processing.ResultsTo develop our ML-based Bio-NER system, we employ conditional random fields, which have performed effectively in several well-known tasks, as our underlying ML model. Adding selected conjunction features, applying numerical normalization, and employing pattern-based post-processing improve the F-scores by 1.67%, 1.04%, and 0.57%, respectively. The combined increase of 3.28% yields a total score of 72.98%, which is better than the baseline system that only uses singleton features.ConclusionWe demonstrate the benefits of using the sequential forward search algorithm to select effective conjunction feature groups. In addition, we show that numerical normalization can effectively reduce the number of redundant and unseen features. Furthermore, the Smith-Waterman local alignment algorithm can help ML-based Bio-NER deal with difficult cases that need longer context windows.

Clustering protein sequences--structure prediction by transitive homology.

It is widely believed that for two proteins Aand Ba sequence identity above some threshold implies structural similarity due to a common evolutionary ancestor. Since this is only a sufficient, but not a necessary condition for structural similarity, the question remains what other criteria can be used to identify remote homologues. Transitivity refers to the concept of deducing a structural similarity between proteins A and C from the existence of a third protein B, such that A and B as well as B and C are homologues, as ascertained if the sequence identity between A and B as well as that between B and C is above the aforementioned threshold. It is not fully understood if transitivity always holds and whether transitivity can be extended ad infinitum. We developed a graph-based clustering approach, where transitivity plays a crucial role. We determined all pair-wise similarities for the sequences in the SwissProt database using the Smith-Waterman local alignment algorithm. This data was transformed into a directed graph, where protein sequences constitute vertices. A directed edge was drawn from vertex A to vertex B if the sequences A and B showed similarity, scaled with respect to the self-similarity of A, above a fixed threshold. Transitivity was important in the clustering process, as intermediate sequences were used, limited though by the requirement of having directed paths in both directions between proteins linked over such sequences. The length dependency-implied by the self-similarity-of the scaling of the alignment scores appears to be an effective criterion to avoid clustering errors due to multi-domain proteins. To deal with the resulting large graphs we have developed an efficient library. Methods include the novel graph-based clustering algorithm capable of handling multi-domain proteins and cluster comparison algorithms. Structural Classification of Proteins (SCOP) was used as an evaluation data set for our method, yielding a 24% improvement over pair-wise comparisons in terms of detecting remote homologues. The software is available to academic users on request from the authors. e.bolten@science-factory.com; schliep@zpr.uni-koeln.de; s.schneckener@science-factory.com; d.schomburg@uni-koeln.de; schrader@zpr.uni-koeln.de. http://www.zaik.uni-koeln.de/~schliep/ProtClust.html.

An analytic study of the phase transition line in local sequence alignment with gaps

A detailed analytic study of the log-linear phase transition of the Smith–Waterman local alignment algorithm is presented. A rectangular alignment lattice is introduced to facilitate the statistical analysis for alignment with gaps. With a few simplifying assumptions, we obtain an analytic expression for the loci of the phase transition line. Our result reproduces the exact and conjectured values for the very large and very small gap costs; the latter corresponds to the related problem of the longest common subsequence. For intermediate values of gap costs, our result is not exact, although a comparison to numerical results yielded a difference of no more than several percent.

Classifying molecular sequences using a linkage graph with their pairwise similarities

This paper presents a method for classifying a large and mixed set of uncharacterized sequences provided by genome projects. As the measure of sequence similarity, we use similarity score computed by a method based on the dynamic programming (DP), such as the Smith-Waterman local alignment algorithm. Although comparison by DP based method is very sensitive, when given sequences include a family of sequences that are much diverged in evolutionary process, similarity among some of them may be hidden behind spurious similarity of some unrelated sequences. Also the distance derived from the similarity score may not be metric (i.e., triangle inequality may not hold) when some sequences have multi-domain structure. To cope with these problems, we introduce a new graph structure called p-quasi complete graph for describing a family of sequences with a confidence measure. We prove that a restricted version of the p-quasi complete graph problem (given a positive integer k, whether a graph contains a 0.5-quasi complete subgraph of which size ⩾ k or not) is NP-complete. Thus we present an approximation algorithm for classifying a set of sequences using p-quasi complete subgraphs. The effectiveness of our method is demonstrated by the result of classifying over 4000 protein sequences on the Escherichia coli genome that was completely determined recently.

Clustering Molecular Sequences with Their Components.

Motivation: Several methods in genetic information have recently been developed to estimate classification of protein sequences through their sequence similarity. These methods are essential for understanding the function of predicted open reading frames (ORFs) and their molecular evolutionary processes. However, since many protein sequences consist of a number of independently evolved structural units (we refer to these units as components), the combinatorial nature of the components makes it difficult to classify the sequences. Results: This paper presents a new method for classifying uncharacterized protein sequences. As the measure of sequence similarity, we use similarity score computed by a method based on the Smith-Waterman local alignment algorithm. Here we introduce how this method cope when sequences have multi-component structure. This method was applied to predicted ORFs on the Escherichia coli genome and we discuss the algorithm and experimental results.