Parallel Multiple Sequence Alignment Research Articles

An Improved Strategy for Task Scheduling in the Parallel Computational Alignment of Multiple Sequences.

Task scheduling in parallel multiple sequence alignment (MSA) through improved dynamic programming optimization speeds up alignment processing. The increased importance of multiple matching sequences also needs the utilization of parallel processor systems. This dynamic algorithm proposes improved task scheduling in case of parallel MSA. Specifically, the alignment of several tertiary structured proteins is computationally complex than simple word-based MSA. Parallel task processing is computationally more efficient for protein-structured based superposition. The basic condition for the application of dynamic programming is also fulfilled, because the task scheduling problem has multiple possible solutions or options. Search space reduction for speedy processing of this algorithm is carried out through greedy strategy. Performance in terms of better results is ensured through computationally expensive recursive and iterative greedy approaches. Any optimal scheduling schemes show better performance in heterogeneous resources using CPU or GPU.

Parallel multiple DNA sequence alignment using genetic algorithm and asynchronous advantage actor critic model

Multiple sequence alignment plays a crucial role in bioinformatics field. In this process, sequences are compared by establishing residue-residue correspondence among related sequences. In this paper, we present a novel framework that uses genetic algorithm and asynchronous advantage actor critic model to look for the optimal multiple DNA sequence alignment. In the first step, we generate initial alignments by applying the T-COFFEE method. Then, we recursively use a genetic algorithm to find the optimal fragmentation of the pre-aligned sequences. At this stage, the obtained short sub alignments are introduced to the A3C model for parallel alignments. So, the optimal multiple alignment of the original sequences represents the fusion of sub-alignments. To evaluate the performances of the proposed approach, we used the Balibase 3.0 benchmark and compared to TCOFFEE, MAFFT and RLALIGN methods. The obtained experimental results show that the proposed approach can achieve solutions of competitive quality.

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.

An Improved Parallel Multiple Sequence Alignment Algorithm on Multi-core System

In this paper, we introduce an improved parallel algorithm for computing the number of exact matches nid (S,T) in the local alignment of two biological sequences S and T. This number is used in the first stage of progressive alignment to compute the distance between two sequences. The distance computations are usually its most computationally intensive part. Therefore, this work concentrates on improving an algorithm for this stage using vectorizing technique and running on multi-core. Our program is able to compute nid (S,T) between very long sequences, up to 34 k residues by C++ with OpenMP library on an Intel Core-i7-3770 quad-core processor of 3.40 GHz and main memory of 8 GB. It outperforms ClustalW-MPI 0.13 with 2.9-fold speedup, and the efficiency reached 0.35. Furthermore, a higher speedup with improved efficiency can be accomplished. Its performance figures vary from a low of 0.438 GCUPS to a high of 3.66 GCUPS as the lengths of the query sequences decrease from 34,500 to 9200. Index Terms: Bioinformatics, Distance Computation, Multi-cores, Multiple Sequence Alignment, Parallel Programming

PaMSA: A Parallel Algorithm for the Global Alignment of Multiple Protein Sequences

Multiple sequence alignment (MSA) is a well-known problem in bioinformatics whose main goal is the identification of evolutionary, structural or functional similarities in a set of three or more related genes or proteins. We present a parallel approach for the global alignment of multiple protein sequences that combines dynamic programming, heuristics, and parallel programming techniques in an iterative process. In the proposed algorithm, the longest common subsequence technique is used to generate a first MSA by aligning identical residues. An iterative process improves the MSA by applying a number of operators that were defined in the present work, in order to produce more accurate alignments. The accuracy of the alignment was evaluated through the application of optimization functions. In the proposed algorithm, a number of processes work independently at the same time searching for the best MSA of a set of sequences. There exists a process that acts as a coordinator, whereas the rest of the processes are considered slave processes. The resulting algorithm was called PaMSA, which stands for Parallel MSA. The MSA accuracy and response time of PaMSA were compared against those of Clustal W, T-Coffee, MUSCLE, and Parallel T-Coffee on 40 datasets of protein sequences. When run as a sequential application, PaMSA turned out to be the second fastest when compared against the nonparallel MSA methods tested (Clustal W, T-Coffee, and MUSCLE). However, PaMSA was designed to be executed in parallel. When run as a parallel application, PaMSA presented better response times than Parallel T-Cofffee under the conditions tested. Furthermore, the sum-of-pairs scores achieved by PaMSA when aligning groups of sequences with an identity percentage score from approximately 70% to 100%, were the highest in all cases. PaMSA was implemented on a cluster platform using the C++ language through the application of the standard Message Passing Interface (MPI) library.

Comparison of Data Partitioning Schema of Parallel Pairwise Alignment on Shared Memory System

The pairwise alignment (PA) algorithm is widely used in bioinformatics to analyze biological sequence. With the advance of sequencer technology, a massive amount of DNA fragments are sequenced much quicker and cheaper. The alignment algorithm needs to be parallelized to be able to align them in a shorter time. Many previous researches have parallelize PA algorithm using various data partitioning schema, but it is unclear which one is the best. The data partitioning schema is important for parallel PA performance, because this algorithm use dynamic programming technique that needs intense inter-thread communication. In this paper, we compared four partitioning schemas to find the best performing one on shared memory system. Those schemas are: blocked columnwise, rowwise, antidiagonal, and blocked columnwise with manual scheduling and loop unrolling. The last schema gave the best performance of 89% efficiency on 4 threads. This result provided fine-grain parallelism that can be used further to develop parallel multiple sequence alignment (MSA).

Parallel progressive multiple sequence alignment on reconfigurable meshes

BackgroundOne of the most fundamental and challenging tasks in bio-informatics is to identify related sequences and their hidden biological significance. The most popular and proven best practice method to accomplish this task is aligning multiple sequences together. However, multiple sequence alignment is a computing extensive task. In addition, the advancement in DNA/RNA and Protein sequencing techniques has created a vast amount of sequences to be analyzed that exceeding the capability of traditional computing models. Therefore, an effective parallel multiple sequence alignment model capable of resolving these issues is in a great demand.ResultsWe design O(1) run-time solutions for both local and global dynamic programming pair-wise alignment algorithms on reconfigurable mesh computing model. To align m sequences with max length n, we combining the parallel pair-wise dynamic programming solutions with newly designed parallel components. We successfully reduce the progressive multiple sequence alignment algorithm's run-time complexity from O(m × n4) to O(m) using O(m × n3) processing units for scoring schemes that use three distinct values for match/mismatch/gap-extension. The general solution to multiple sequence alignment algorithm takes O(m × n4) processing units and completes in O(m) time.ConclusionsTo our knowledge, this is the first time the progressive multiple sequence alignment algorithm is completely parallelized with O(m) run-time. We also provide a new parallel algorithm for the Longest Common Subsequence (LCS) with O(1) run-time using O(n3) processing units. This is a big improvement over the current best constant-time algorithm that uses O(n4) processing units.