An adaptive grid implementation of DNA sequence alignment

Chen, Chunxi; Schmidt, Bertil

doi:10.1016/j.future.2005.03.001

Cited by 20 publications

(13 citation statements)

References 11 publications

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“…Combination of several PC clusters creates a hierarchical grid computing. [7] have implemented a DNA sequence alignment model under this hierarchical grid architecture. They used dynamic programming algorithm with linear space parallelism and is separated into two parts: parallelization of the similarity matrix and parallelization of the divide-and-conquer algorithm.…”

Section: A Related Workmentioning

confidence: 99%

Parallel Guided Dynamic Programming Approach for DNA Sequence Similarity Search

Nordin¹,

Yazid²,

Aziz³

et al. 2009

IJCEE

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 Development of DNA sequence comparison technique is an active research activity in computational biology application.Commonly techniques studied are dynamic programming and heuristic algorithms. Exhaustive dynamic programming algorithm produces optimal result but requires longer time and bigger space. Heuristic algorithm gives approximate results with much faster processing. We have developed a new model that improves the speed of large scale DNA sequence similarity search and at the same time the best possible alignment result is retained. The model is known as a guided dynamic programming approach for DNA sequence similarity search (FRA-Search). Two approaches are used to complete the FRA-Search model: an automaton based exact string matching algorithm is employed to skip irrelevant database sequences from being computed for dynamic programming alignment processing and; the rough sets theory has been employed to classify and reduct the dataset. This paper discusses the parallel model for FRA-Search application. The parallel FRA-Search model is implemented on PC-based cluster system. It is developed on a single program multiple data (SPMD) architecture and MPJ Express software is used as a communication interface protocol between processors.

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Section: A Related Workmentioning

confidence: 99%

Parallel Guided Dynamic Programming Approach for DNA Sequence Similarity Search

Nordin¹,

Yazid²,

Aziz³

et al. 2009

IJCEE

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“…They used dynamic programming algorithm with linear space parallelism and is separated into two parts: parallelization of the similarity matrix and parallelization of the divide-and-conquer algorithm. [10].…”

Section: B) Agentmentioning

confidence: 99%

Designing and Building a Framework for DNA Sequence Alignment Using Grid Computing

Orabi¹,

Assal²,

Azim³

et al. 2014

ijacsa

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Abstract-Deoxyribonucleic acid (DNA) is a molecule that encodes unique genetic instructions used in the development and functioning of all known living organisms and many viruses. This Genetic information is encoded as a sequence of nucleotides (adenine, cytosine, guanine, and thymine) recorded using the letters A, C, G, and T.DNA querying or alignment of these sequences required dynamic programming tools and very complex matrices and some heuristic methods like FASTA and BLAST that use massive force of processing and highly time consuming. We present a parallel solution to reduce the processing time. Smith waterman algorithm, NeedlemanWunsch, some weighting matrices and a grid of computers are used to find field of similarity between these sequences in large DNA datasets. This grid consists of master computer and unlimited number of agents. The master computer is the user interface for insert the queried sequence and coordinates the processing between the grid agents.

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“…Constrained by the fact that it must make use of the selective computation and allocation discussed previously, along with the need for the dynamic programming tables to be distributed to allow for more available memory, the design follows a new parallel programming paradigm known as manager-worker. Manager-worker is a highly modified version of the master-slave paradigm, sharing some similarities with the recently proposed schedulerworker paradigm, which is designed for robustness under disturbance on heterogeneous platforms [5]. Where the manager-worker differs from the scheduler-worker is in the management of its tasks and task-related communication.…”

Section: Parallel Algorithm and Implementationmentioning

confidence: 99%

“…There has been significant recent work on the parallelization of dynamic programming algorithms in computational biology [9], [10], [11], including implementations suitable for computational grids [5]. What distinguishes this work is the data-driven recursive implementation, with resulting dynamically allocated tasks.…”

Section: Introductionmentioning

confidence: 99%

Dynamic parallelization for RNA structure comparison

Snow

Aubanel

Evans

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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In this paper we describe the parallelization of a dynamic programming algorithm used to find common RNA secondary structures including pseudoknots and similar structures. The sequential algorithm is recursive and uses memoization and data-driven selective allocation of the tables, in order to cope with the high space and time demands. These features, in addition to the irregular nature of the data access pattern, present particular challenges to parallelization. We present a new manager-worker approach, where workers are responsible for task creation and the manager's sole responsibility is overseeing load balancing. Special considerations are given to the management of distributed, dynamic task creation and data structures, along with general inter-process communication and load balancing on a heterogeneous computational platform. Experimental results show a modest level of speedup with a highly-scalable level of memory usage, allowing the comparison of much longer RNA molecules than is possible in the sequential implementation.

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An adaptive grid implementation of DNA sequence alignment

Cited by 20 publications

References 11 publications

Parallel Guided Dynamic Programming Approach for DNA Sequence Similarity Search

Parallel Guided Dynamic Programming Approach for DNA Sequence Similarity Search

Designing and Building a Framework for DNA Sequence Alignment Using Grid Computing

Dynamic parallelization for RNA structure comparison

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