Next-generation bioinformatics: using many-core processor architecture to develop a web service for sequence alignment

Gálvez, Sergio; Díaz, David; Hernández, Pilar; Esteban, Francisco J.; Caballero, J. A.; Dorado, Gabriel

doi:10.1093/bioinformatics/btq017

Cited by 36 publications

(24 citation statements)

References 15 publications

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“…Начальные ячейки таблицы (12) не войдут ни в один из блоков (19), а составят блоки с отрицательными индексами I = −1, J = −1 со значениями согласно формуле (13):…”

Section: рисunclassified

“…Пусть множество u = {u I,J } состоит из всех комплексов (21), вычисленных для каждого блока вида (19), тогда оно определяет все внутренние значения (19) согласно рекуррентной формуле (11). Можно сделать важный вывод, что множество u является сеточным представлением таблицы V , где скаляры u axe I,J и векторы u row I,J , u col I,J являются узлами и рёбрами этой сетки соответственно.…”

Section: рисunclassified

“…Несмотря на появление и развитие более быстрых эвристических алгоритмов для исследования генетических последовательностей [13], тема глобального выравнивания продолжает развиваться, имеются современные подходы его оптимизации [14,15,16,17,18], а также реализации для новых компьютерных архитектур [19,20]. Среди этих работ особое место занимает работа [14], в которой предложена сеточная схема алгоритма глобального выравнивания.…”

Section: Introductionunclassified

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Parallel algorithm for global alignment of long aminoacid and nucleotide sequences

Tetuev¹

2017

Math.Biol.Bioinf.

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A parallel algorithm is developed for the global alignment of long DNA and protein sequences. The algorithm uses an arbitrary substitution matrix. An affine internal and end gap penalty systems might be set up separately for each of the two sequences. The possibility to control the choice of an optimal alignment from the set of alternative ones is implemented. The two parameters of the parallel algorithm which are called grid steps were introduced. They are used to split the global alignment matrix into blocks of specified size. The analysis of these parameters was performed in order to obtain optimal values that reduce either time complexity or memory complexity of the algorithm. It is shown that when using the block sizes that provide lowest memory complexity, the algorithm allows to align two long sequences of length L using the memory of size O(L4/3). It is shown that the algorithm is well scaled on multi-core systems, reaching superlinear execution time acceleration. The algorithm is implemented as a high-performance parallel web application in JavaScript and is freely available at http://sbars.impb.ru/aligner.html.

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Section: рисunclassified

Section: Introductionunclassified

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Parallel algorithm for global alignment of long aminoacid and nucleotide sequences

Tetuev¹

2017

Math.Biol.Bioinf.

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“…Results show that the speedup can be up to 20x when compared with similar algorithms running in a Xeon Quadcore workstation with 8 GB of RAM [8]. This ultra-performance is achieved when the length of the sequences to align is very long; e.g., from 300 kilobases (kb) to 1,000 kb.…”

Section: Introductionmentioning

confidence: 99%

MC64: A Web Platform to Test Bioinformatics Algorithms in a Many-Core Architecture

Esteban¹,

Díaz²,

Hernández

et al. 2011

5th International Conference on Practical Applications of Computational Biology &Amp; Bioinformatics (PACBB 2011)

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Abstract. New analytical methodologies, like the so-called "next-generation sequencing" (NGS), allow the sequencing of full genomes with high speed and reduced price. Yet, such technologies generate huge amounts of data that demand large raw computational power. Many-core technologies can be exploited to overcome the involved bioinformatics bottleneck. Indeed, such hardware is currently in active development. We have developed parallel bioinformatics algorithms for many-core microprocessors containing 64 cores each. Thus, the MC64 web platform allows executing high-performance alignments (NeedlemanWunsch, Smith-Waterman and ClustalW) of long sequences. The MC64 platform can be accessed via web browsers, allowing easy resource integration into thirdparty tools. Furthermore, the results obtained from the MC64 include timeperformance statistics that can be compared with other platforms.

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“…Paired sequence comparison is inherently a parallel process in which many sequence pairs can be analysed at the same time by means of functions or algorithms that are iteratively performed over sequences. This is impelling the parallelisation of sequence comparison algorithms [5][6][7][8][9] as well as other bioinformatic algorithms [10,11].…”

Section: Introductionmentioning

confidence: 99%

SCBI_MapReduce, a New Ruby Task-Farm Skeleton for Automated Parallelisation and Distribution in Chunks of Sequences: The Implementation of a Boosted Blast+

Guerrero-Fernández

Falgueras

Claros

2013

Computational Biology Journal

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Current genomic analyses often require the managing and comparison of big data using desktop bioinformatic software that was not developed regarding multicore distribution. The task-farm SCBI MapReduce is intended to simplify the trivial parallelisation and distribution of new and legacy software and scripts for biologists who are interested in using computers but are not skilled programmers. In the case of legacy applications, there is no need of modification or rewriting the source code. It can be used from multicore workstations to heterogeneous grids. Tests have demonstrated that speed-up scales almost linearly and that distribution in small chunks increases it. It is also shown that SCBI MapReduce takes advantage of shared storage when necessary, is faulttolerant, allows for resuming aborted jobs, does not need special hardware or virtual machine support, and provides the same results than a parallelised, legacy software. The same is true for interrupted and relaunched jobs. As proof-of-concept, distribution of a compiled version of Blast+ in the SCBI Distributed Blast gem is given, indicating that other blast binaries can be used while maintaining the same SCBI Distributed Blast code. Therefore, SCBI MapReduce suits most parallelisation and distribution needs in, for example, gene and genome studies.

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Next-generation bioinformatics: using many-core processor architecture to develop a web service for sequence alignment

Cited by 36 publications

References 15 publications

Parallel algorithm for global alignment of long aminoacid and nucleotide sequences

Parallel algorithm for global alignment of long aminoacid and nucleotide sequences

MC64: A Web Platform to Test Bioinformatics Algorithms in a Many-Core Architecture

SCBI_MapReduce, a New Ruby Task-Farm Skeleton for Automated Parallelisation and Distribution in Chunks of Sequences: The Implementation of a Boosted Blast+

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